Power distribution network station area scheduling method and device based on micro pumped storage power station, computer equipment and storage medium

Abstract

The invention relates to the technical field of pumped storage control, and particularly provides a power distribution network district scheduling method and device based on a micro pumped storage power station, and the method comprises the steps: determining an additional constraint condition based on the reservoir sharing condition of the micro pumped storage power station of a district, adding the additional constraint condition into a power distribution network transformer area scheduling model corresponding to the transformer area; solving the power distribution network transformer area scheduling model corresponding to the transformer area to obtain an optimization result; taking the optimization result as a control scheduling scheme of the transformer area; according to the technical scheme provided by the invention, the energy supply relation of multiple transformer areas is effectively changed, a single energy mutual aid mode is changed into an electricity and water two-path energy mutual aid mode, the optimal control of the whole system is formed, and the method has important significance on the stability and economical efficiency of local power grid operation.

Description

Technical Field

[0001] The present invention relates to the technical field of pumped storage control, and in particular to a distribution network area dispatching method and device based on a micro pumped storage power station. Background Art

[0002] Due to environmental pollution and energy shortages caused by fossil energy, renewable energy sources such as wind and light have been used on a large scale, and the proportion of traditional thermal power units has decreased. However, wind and light power generation have problems such as intermittency and instability. Energy storage systems are usually used to adjust the power fluctuations of wind and photovoltaic power generation, and improve the utilization and penetration of renewable energy.

[0003] Commonly used energy storage technologies include pumped storage, battery storage, compressed air storage, hydrogen storage, etc. Because pumped storage power stations are more flexible in start and stop control, environmentally friendly, and have functions such as peak and valley regulation, frequency regulation, and phase regulation, traditional pumped storage power stations are a widely used new energy storage method. However, the construction cost of building traditional pumped storage power stations in adjacent substations is relatively high; multiple substations can only achieve energy transmission through local power grid connections, and the single transmission method makes the local power grid under greater transmission pressure at peak times, and the energy dispatching capacity and stability are limited; further leading to a weakening of the absorption capacity of new energy and an increase in the load loss rate of the power grid. The method of building micro pumped storage power stations in adjacent substations can solve this problem, but there is still the defect of high construction cost. Summary of the invention

[0004] In order to overcome the above-mentioned defects, the present invention proposes a distribution network area dispatching method and device based on a micro pumped storage power station.

[0005] In a first aspect, a method for dispatching a distribution network area based on a micro pumped storage power station is provided, wherein the method for dispatching a distribution network area based on a micro pumped storage power station comprises:

[0006] Determine additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and add the additional constraints to the distribution network substation dispatch model corresponding to the substation;

[0007] Solve the distribution network substation dispatch model corresponding to the substation to obtain an optimization result;

[0008] The optimization result is used as the control and dispatching plan of the substation.

[0009] Preferably, the optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage unit, activation coefficient of turbine generator unit, water storage capacity of downstream reservoir, regulation time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

[0010] Preferably, the reservoir sharing situation includes: the micro pumped storage power stations in two substations share a downstream reservoir or the micro pumped storage power stations in two substations share an upstream reservoir.

[0011] Furthermore, when the micro pumped storage power stations in two areas share a downstream reservoir, the additional constraints are as follows:

[0012]

[0013] V L (T N )=V L,end

[0014] In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), V L (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the i-th station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

[0015] Furthermore, when the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows:

[0016]

[0017] V H (T N )=V H,end ,

[0018] In the above formula, V H (k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

[0019] Furthermore, the distribution network area dispatching model corresponding to the area includes:

[0020] The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

[0021] Furthermore, the objective function is as follows:

[0022]

[0023] In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

[0024] Furthermore, the mathematical model of the constraint condition is as follows:

[0025]

[0026]

[0027]

[0028]

[0029] In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

[0030] Furthermore, the and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

[0031] In a second aspect, a distribution network area dispatching device based on a micro pumped storage power station is provided, and the distribution network area dispatching device based on the micro pumped storage power station comprises:

[0032] A determination module, used to determine additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and add the additional constraints to the distribution network substation dispatching model corresponding to the substation;

[0033] An analysis module is used to solve the distribution network area dispatching model corresponding to the area to obtain an optimization result;

[0034] The scheduling module is used to use the optimization result as a control scheduling plan for the substation.

[0035] Preferably, the optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage unit, activation coefficient of turbine generator unit, water storage capacity of downstream reservoir, regulation time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

[0036] Preferably, the reservoir sharing situation includes: the micro pumped storage power stations in two substations share a downstream reservoir or the micro pumped storage power stations in two substations share an upstream reservoir.

[0037] Furthermore, when the micro pumped storage power stations in two areas share a downstream reservoir, the additional constraints are as follows:

[0038]

[0039]

[0040] V L (T N )=V L,end

[0041] In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), V L (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the i-th station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

[0042] Furthermore, when the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows:

[0043]

[0044] V H (T N )=V H,end ,

[0045] In the above formula, V H (k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

[0046] Furthermore, the distribution network area dispatching model corresponding to the area includes:

[0047] The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

[0048] Furthermore, the objective function is as follows:

[0049]

[0050] In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

[0051] Furthermore, the mathematical model of the constraint condition is as follows:

[0052]

[0053] In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

[0054] Furthermore, the and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

[0055] In a third aspect, a computer device is provided, comprising: one or more processors;

[0056] The processor is configured to execute one or more programs;

[0057] When the one or more programs are executed by the one or more processors, the distribution network area scheduling method based on the micro pumped storage power station is implemented.

[0058] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed, the distribution network area dispatching method based on a micro pumped storage power station is implemented. The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:

[0059] The present invention provides a distribution network substation dispatching method and device based on a micro pumped storage power station, comprising: determining additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and adding the additional constraints to the distribution network substation dispatching model corresponding to the substation; solving the distribution network substation dispatching model corresponding to the substation to obtain an optimization result; using the optimization result as a control dispatching scheme for the substation; the technical scheme provided by the present invention effectively changes the energy supply relationship of multiple substations from a single energy mutual assistance mode to a mode of electricity and water energy mutual assistance, forming the optimal control of the overall system, and based on the operation mode of shared reservoirs, the flow of energy between multiple substations is no longer limited to the local power grid, but energy transmission can be achieved through the mutual assistance of shared reservoirs. Furthermore, based on the operation mode of shared reservoirs, one or more reservoirs can be built less, thereby reducing construction costs, enhancing the stability and flexibility of energy dispatching between multiple substations, and having important significance for the stability and economy of local power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a schematic flow chart of the main steps of the method for dispatching a distribution network area based on a micro pumped storage power station according to an embodiment of the present invention;

[0061] Figure 2 This is a schematic diagram of a shared reservoir operation mode of a micro pumped storage power station according to an embodiment of the present invention;

[0062] Figure 3 It is a flow chart of the particle swarm intelligent optimization algorithm of an embodiment of the present invention. DETAILED DESCRIPTION

[0063] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.

[0064] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] As disclosed in the background technology, due to the environmental pollution and energy shortage problems of fossil energy, renewable energy such as wind and light have been used on a large scale, and the proportion of traditional thermal power units has decreased. However, wind and light power generation have problems such as intermittency and instability. Energy storage systems are usually used to adjust the power fluctuations of wind power generation and photovoltaic power generation, and improve the utilization and penetration of renewable energy.

[0066] Commonly used energy storage technologies include pumped storage, battery storage, compressed air storage, hydrogen storage, etc. Because pumped storage power stations are more flexible in start and stop control, environmentally friendly, and have functions such as peak and valley regulation, frequency regulation, and phase regulation, traditional pumped storage power stations are a widely used new energy storage method. However, the construction cost of building traditional pumped storage power stations in adjacent substations is relatively high; multiple substations can only achieve energy transmission through local power grid connections, and the single transmission method makes the local power grid under greater transmission pressure at peak times, and the energy dispatching capacity and stability are limited; further leading to a weakening of the absorption capacity of new energy and an increase in the load loss rate of the power grid. The method of building micro pumped storage power stations in adjacent substations can solve this problem, but there is still the defect of high construction cost.

[0067] In order to improve the above-mentioned problems, the present invention provides a distribution network substation dispatching method and device based on a micro pumped storage power station, including: determining additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and adding the additional constraints to the distribution network substation dispatching model corresponding to the substation; solving the distribution network substation dispatching model corresponding to the substation to obtain an optimization result; using the optimization result as a control dispatching scheme for the substation; the technical scheme provided by the present invention effectively changes the energy supply relationship of multiple substations from a single energy mutual assistance mode to a mode of mutual assistance of electricity and water, forming the optimal control of the overall system, and based on the operation mode of shared reservoirs, the flow of energy between multiple substations is no longer limited to the local power grid, but energy transmission can be realized through the mutual assistance of shared reservoirs. Furthermore, based on the operation mode of shared reservoirs, one or more reservoirs can be built less, thereby reducing construction costs, enhancing the stability and flexibility of energy dispatching between multiple substations, and having important significance for the stability and economy of local power grid operation.

[0068] The above scheme is described in detail below.

[0069] Example 1

[0070] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart of the main steps of a method for dispatching a distribution network area based on a micro pumped storage power station according to an embodiment of the present invention. Figure 1 As shown, the distribution network area dispatching method based on the micro pumped storage power station in the embodiment of the present invention mainly includes the following steps:

[0071] Step S101: determining additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and adding the additional constraints to the distribution network substation dispatching model corresponding to the substation;

[0072] Step S102: solving the distribution network substation dispatching model corresponding to the substation to obtain an optimization result;

[0073] Step S103: Using the optimization result as a control scheduling plan for the substation.

[0074] Among them, the optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage unit, activation coefficient of turbine generator unit, water storage capacity of downstream reservoir, control time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

[0075] In this embodiment, Figure 2 As shown, unlike the traditional pumped storage power station, which has a single correspondence between upstream and downstream reservoirs, in the shared reservoir operation mode, one downstream reservoir can correspond to two upstream reservoirs or one upstream reservoir can correspond to two downstream reservoirs. Therefore, the reservoir sharing situation includes: two micro pumped storage power stations in different areas share one downstream reservoir or two micro pumped storage power stations in different areas share one upstream reservoir.

[0076] For example, a micro pumped-storage power station shared downstream reservoir operation mode is established, whereby the upstream reservoirs of each substation share the same downstream reservoir. By sharing the downstream reservoir, the reservoir water volume of the pumped-storage power stations in each substation can be interconnected, thereby realizing energy transmission between different substations.

[0077] Alternatively, a micro pumped-storage power station shared upstream reservoir operation mode can be established, where the downstream reservoirs of each substation share the same upstream reservoir. By sharing the upstream reservoir, the reservoir water volume of the pumped-storage power stations in each substation can be interconnected, thereby realizing energy transmission between the substations.

[0078] In one embodiment, when the micro pumped storage power stations in two areas share a downstream reservoir, the additional constraints are as follows:

[0079]

[0080] V L (T N )=V L,end

[0081] In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), VL (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the i-th station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

[0082] When the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows:

[0083]

[0084] V H (T N )=V H,end ,

[0085] In the above formula, V H (k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

[0086] In one embodiment, the distribution network area dispatching model corresponding to the area includes:

[0087] The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

[0088] In one embodiment, the objective function is as follows:

[0089]

[0090] In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

[0091] In one embodiment, the mathematical model of the constraint condition is as follows:

[0092]

[0093] In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

[0094] In one embodiment, the and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

[0095] In this embodiment, Figure 3 As shown, the particle swarm intelligent optimization algorithm is used to solve the distribution network area dispatching model corresponding to the area, which specifically includes:

[0096] The optimization variables of the model in step a include Δt, represents the photovoltaic power generation power at time k in a typical day in the i-th station; represents the wind power generation at time k in a typical day in the i-th station area; It represents the pumped storage power at time k in a typical day in the i-th station; represents the turbine power generation at time k in a typical day in the i-th station; represents the activation of the pumped storage unit at time k in a typical day in the i-th station area, The pumped storage unit stops running. When the pumped storage unit starts running, It represents the activation of the turbine generator set at time k in a typical day in the i-th station area, When the turbine generator set stops running; When the turbine generator set starts running, Non-continuous variables, when Time to take when Pick The same reason Time to take when Pick represents the water storage capacity of the upstream reservoir at time k in a typical day in the ith station area; represents the water storage capacity of the downstream reservoir at time k in a typical day in the i-th station area; Δt represents the time interval between regulation (two regulation); represents the power injected into the transmission network at time k in a typical day of the ith station; It represents the change value of the power injected into the transmission network at time k in a typical day of the ith substation; represents the power of the line load at time k in a typical day of the ith station. Place N particles in the B-dimensional space composed of the above variables, and set the maximum number of iterations to S. Initialize the number of iterations s = 0, as well as the initial position and initial velocity of each particle in the particle swarm.

[0097] Step b: The position of the mth particle after the sth iteration is Speed Calculate the value of the objective function J at the position of each particle at this moment. And update the optimal position searched by the particle at this moment. The individual optimal solution of the mth particle after s iterations is: The objective function value corresponding to this optimal solution is recorded as That is, the objective function value corresponding to the individual optimal solution of the mth particle after s iterations; update the optimal position searched by the entire particle swarm, and the optimal solution searched by the particle swarm after s iterations is: The objective function value corresponding to this optimal solution is recorded as That is, the objective function value corresponding to the optimal solution of the particle swarm after s iterations.

[0098] Step c determines whether the number of iterations s is less than the maximum number of iterations S of the particle swarm algorithm. If so, s=s+1 and enters step 6.4; if not, the value of the objective function J corresponding to the optimal solution of the group at this time and the optimal solution of the group are output to complete the scheduling.

[0099] Step d: Update the position and speed of the particles according to the particle swarm algorithm, obtain the new position and continue iterative optimization. Update the speed of N particles according to the speed update formula. The speed of the particle updated in the s+1th iteration is as follows:

[0100]

[0101] in is the inertia part, which is composed of the inertia weight ω and the particle's own speed Composition, which represents the particle’s trust in its previous state of motion; The cognitive part represents the particle’s own thinking, that is, the particle’s own experience part, which can be understood as the distance and direction between the particle’s current position and its own historical optimal position; The social part represents the information sharing and cooperation between particles, which is derived from the experience of other excellent particles in the group. It can be understood as the distance and direction between the current position of the particle and the historical optimal position of the group. Among them, s represents the number of iterations; ω represents the inertia weight; c1 represents the individual learning factor; c2 represents the group learning factor; r1, r2 represent random numbers in the interval [0,1], which increase the randomness of the search; represents the b-th dimension velocity vector of particle m after the s-th iteration; represents the b-th dimension position vector of particle m after the s-th iteration; represents the historical optimal position of particle m in the bth dimension after the sth iteration, that is, the optimal solution obtained by the mth particle after the sth iteration; It represents the historical optimal position of the bth dimension of the group after the sth iteration, that is, the optimal solution in the entire particle swarm after the sth iteration.

[0102] Update the positions of N particles according to the position update formula:

[0103]

[0104] in represents the position of the b-dimensional vector of the m-th particle after the s+1th iteration; represents the position of the b-dimensional vector of the m-th particle after the s-th iteration; Represents the b-dimensional vector velocity of the mth particle after the sth iteration. Return the updated positions of the N particles to step b for further operation.

[0105] Example 2

[0106] Based on the same inventive concept, the present invention also provides a distribution network area dispatching device based on a micro pumped storage power station, and the distribution network area dispatching device based on a micro pumped storage power station includes:

[0107] A determination module, used to determine additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and add the additional constraints to the distribution network substation dispatching model corresponding to the substation;

[0108] An analysis module is used to solve the distribution network area dispatching model corresponding to the area to obtain an optimization result;

[0109] The scheduling module is used to use the optimization result as a control scheduling plan for the substation.

[0110] Preferably, the optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage unit, activation coefficient of turbine generator unit, water storage capacity of downstream reservoir, regulation time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

[0111] Preferably, the reservoir sharing situation includes: the micro pumped storage power stations in two substations share a downstream reservoir or the micro pumped storage power stations in two substations share an upstream reservoir.

[0112] Furthermore, when the micro pumped storage power stations in two areas share a downstream reservoir, the additional constraints are as follows:

[0113]

[0114] V L (T N )=V L,end

[0115] In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), V L (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the i-th station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

[0116] Furthermore, when the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows:

[0117]

[0118]

[0119] V H (T N )=V H,end ,

[0120] In the above formula, V H(k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

[0121] Furthermore, the distribution network area dispatching model corresponding to the area includes:

[0122] The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

[0123] Furthermore, the objective function is as follows:

[0124]

[0125] In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

[0126] Furthermore, the mathematical model of the constraint condition is as follows:

[0127]

[0128]

[0129] In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

[0130] Furthermore, the and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

[0131] Example 3

[0132] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a distribution network area dispatching method based on a micro pumped storage power station in the above embodiment.

[0133] Example 4

[0134] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in a computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a distribution network area dispatching method based on a micro pumped storage power station in the above embodiment.

[0135] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0136] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0137] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distribution network area dispatching method based on a micro pumped storage power station, characterized in that: The method comprises: Determine additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and add the additional constraints to the distribution network substation dispatch model corresponding to the substation; Solve the distribution network substation dispatch model corresponding to the substation to obtain an optimization result; The optimization result is used as the control and dispatching plan of the substation.

2. The method according to claim 1, characterized in that The optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage units, activation coefficient of turbine generator sets, water storage capacity of downstream reservoirs, regulation time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

3. The method according to claim 1, characterized in that The reservoir sharing situation includes: the micro pumped storage power stations in two substations share a downstream reservoir or the micro pumped storage power stations in two substations share an upstream reservoir.

4. The method according to claim 3, characterized in that When the micro pumped storage power stations in two substations share a downstream reservoir, the additional constraints are as follows: In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), V L (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the ith station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

5. The method according to claim 3, characterized in that When the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows: In the above formula, V H (k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

6. The method according to claim 4 or 5, characterized in that The distribution network area dispatching model corresponding to the area includes: The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

7. The method according to claim 6, characterized in that The objective function is as follows: In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

8. The method according to claim 7, characterized in that The mathematical model of the constraint condition is as follows: In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

9. The method according to claim 8, characterized in that Said and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

10. A distribution network area dispatching device based on a micro pumped storage power station, characterized in that: The device comprises: A determination module, used to determine additional constraints based on the reservoir sharing situation of the micro pumped storage power station in the substation, and add the additional constraints to the distribution network substation dispatching model corresponding to the substation; An analysis module is used to solve the distribution network area dispatching model corresponding to the area to obtain an optimization result; The scheduling module is used to use the optimization result as a control scheduling plan for the substation.

11. The device according to claim 10, characterized in that The optimization results include: photovoltaic power generation, wind power generation, water pump storage power, turbine power generation, activation coefficient of pumped storage units, activation coefficient of turbine generator sets, water storage capacity of downstream reservoirs, regulation time interval, power injected into the transmission network, change value of power injected into the transmission network, and power of line load.

12. The device according to claim 10, characterized in that The reservoir sharing situation includes: the micro pumped storage power stations in two substations share a downstream reservoir or the micro pumped storage power stations in two substations share an upstream reservoir.

13. The device according to claim 12, characterized in that When the micro pumped storage power stations in two substations share a downstream reservoir, the additional constraints are as follows: In the above formula, V L (k+1) is the water volume of the downstream reservoir at time (k+1), V L (k) is the water volume of the downstream reservoir at time k, is the water volume of the upstream reservoir of the ith station at time (k+1), is the water volume of the upstream reservoir of the ith station at time k, is the pumped storage power at time k in a typical day in the i-th station; is the turbine power generation at time k in a typical day in the ith station, ρ is the density of water, g is the acceleration of gravity, h (i) is the water head of the ith station, is the water pump energy storage efficiency of the ith station, is the turbine power generation efficiency of the ith station, Δt is the regulation time interval, is the upstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V L (T N ) is the downstream reservoir at the final time T N The amount of water in the reservoir, is the final water storage capacity of the upstream reservoir at the end of a typical day in the i-th station under the shared reservoir model, V L,end It is the final water storage capacity of the downstream reservoir at the end of a typical day under the shared reservoir model.

14. The device according to claim 12, characterized in that When the micro pumped storage power stations in two areas share an upstream reservoir, the additional constraints are as follows: In the above formula, V H (k+1) is the water volume of the upstream reservoir at time (k+1), V H (k) is the water volume of the upstream reservoir at time k, is the water volume of the downstream reservoir of the ith station at time (k+1), is the water volume of the downstream reservoir of the ith station at time k, V H (T N ) is the upstream reservoir at the final time T N The amount of water in the reservoir, is the downstream reservoir of the ith station at the final time T N The amount of water in the reservoir, V H,end is the final water storage capacity of the upstream reservoir at the end of a typical day under the shared reservoir model, is the final water storage capacity of the downstream reservoir at the end of a typical day in the i-th station under the shared reservoir mode.

15. The device according to claim 13 or 14, characterized in that The distribution network area dispatching model corresponding to the area includes: The objective function and its corresponding constraints are to abandon wind power, photovoltaic power generation and minimize the power injected into the grid between each station.

16. The device according to claim 15, characterized in that The objective function is as follows: In the above formula, J is the objective function value, σ is the preset weight, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the photovoltaic power generation power of the ith area at time k in a typical day, is the maximum photovoltaic power generation power of the ith area at time k in a typical historical day, is the wind power generation capacity of the ith station at time k in a typical day, Inject grid power into the ith substation at time k in a typical day.

17. The device according to claim 16, characterized in that The mathematical model of the constraint condition is as follows: In the above formula, is the energy storage power of the water pump in the ith station at time k in a typical day, is the maximum energy storage power of the water pump in the ith substation at time k in a typical historical day, is the power generation of the turbine in the ith station at time k in a typical day, is the maximum power generation of the turbine in the ith substation at time k in a typical historical day, is the maximum water storage capacity of the upstream reservoir of the ith station in a typical day, is the maximum water storage capacity of the downstream reservoir of the ith station in a typical day, is the total pumped storage power of the ith substation at time k in a typical day, is the power consumed by the line load of the ith substation at time k in a typical day, is the activation coefficient of the pumped storage unit, The activation factor for the turbine generator set, is the change in power injected into the transmission network of the ith substation at time k, and δ is the impact threshold.

18. The device according to claim 17, characterized in that Said and when When the pumped storage unit at time k in a typical day in the i-th station stops operating, When the pumped storage unit at time k in a typical day in the i-th station is started, When the turbine generator set at time k in a typical day in the i-th station stops running, At , the turbine generator set at time k in a typical day in the i-th station area starts running.

19. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the distribution network substation scheduling method based on the micro pumped storage power station as described in any one of claims 1 to 9 is implemented.

20. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the distribution network area dispatching method based on a micro pumped storage power station as described in any one of claims 1 to 9 is implemented.

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