Multi-target collaborative planning method and system for novel power system

Abstract

The invention relates to the technical field of power system planning, and discloses a multi-target collaborative planning method and system for a novel power system, and the method comprises the steps: respectively building a dual-carbon target constraint, a renewable energy permeability constraint, a light and wind abandoning rate constraint, a demand response constraint and an energy storage capacity configuration constraint based on the state data of the power system; generating a source-side restriction constraint, a source-storage collaborative constraint, a source-load-storage collaborative constraint and a source-network-load-storage constraint based on restriction relevance and collaborative relevance among the constraints, and establishing a source-network-load-storage optimization model by taking the minimum total cost of the power system as a target; and performing iterative calculation on the installed capacity of each power generation technology in the power system based on the source network load storage optimization model, obtaining an optimal planning scheme after convergence, and planning the installed capacity of each power generation technology in the power system based on the optimal planning scheme. According to the invention, the economical efficiency, safety and low-carbon property of electric power system planning can be improved, and the operation efficiency and operation safety of the electric power system are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning, and in particular to a novel multi-objective collaborative planning method and system for a power system. Background Art

[0002] In the existing new power system planning technology solutions that consider the coordinated optimization of "source-load-storage", the constraint module is usually generated according to the power generation technology, generally consisting of upper and lower limits of installed capacity, annual power generation curves, etc. With the introduction of a number of regulations to promote the transformation of the power sector, the current new power system planning technology solutions lack rationality and will lead to higher planning costs for the power system, greatly reducing its applicability. Summary of the invention

[0003] The present invention provides a novel multi-objective collaborative planning method and system for a power system, which can improve the economy, safety and low carbon nature of power system planning, thereby improving the operating efficiency and safety of the power system.

[0004] In order to solve the above technical problems, the present invention provides a novel multi-objective collaborative planning method for a power system, comprising:

[0005] Based on the status data of the power system, dual carbon target constraints, renewable energy penetration constraints, wind and solar curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints are established respectively;

[0006] Based on the constraint correlation between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint, establishing a source-side constraint;

[0007] Based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint, establishing a source-storage collaborative constraint;

[0008] Based on the collaborative association between the source-storage collaborative constraint and the demand response constraint, establishing a source-load-storage collaborative constraint;

[0009] Based on the constraint correlation between the source-load-storage synergy constraint and the dual carbon target constraint, a source-grid-load-storage constraint is established;

[0010] In combination with the dual carbon target constraint, the renewable energy penetration rate constraint, the abandoned solar and wind rate constraint, the demand response constraint, the energy storage capacity configuration constraint, the source side restriction constraint, the source storage coordination constraint, the source load storage coordination constraint and the source grid load storage constraint, a source grid load storage optimization model is established with the goal of minimizing the total cost of the power system;

[0011] Based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence. The installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme.

[0012] Furthermore, based on the state data of the power system, dual carbon target constraints, renewable energy penetration rate constraints, solar and wind curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints are established respectively, including:

[0013] The dual carbon target constraints are specifically:

[0014]

[0015] P Fire (n)≤φ Fire (n)

[0016] Where P Fire is the power generation capacity of the thermal power plant, p is the planning period, m is the planning start year, n is the planning realization year, φ Fire is the carbon neutrality ratio coefficient;

[0017] The renewable energy penetration rate constraint is specifically:

[0018]

[0019]

[0020] Where P wind is the installed capacity of wind power; wind_per is the wind power penetration rate; P Gen is the total installed capacity; g is the power generation technology; P PV is the photovoltaic installed capacity; φ PV_per is the photovoltaic penetration rate;

[0021] The constraints on abandoned solar and wind power rates are specifically:

[0022]

[0023]

[0024] Where P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; P wind is wind power installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV for photovoltaic installed capacity;

[0025] The demand response constraints are specifically:

[0026]

[0027] Where P D_opr is the demand side response load; φ D_opr is the transformation ratio of the demand-side response load; P D For the total load demand;

[0028] The energy storage capacity configuration constraints are specifically:

[0029]

[0030]

[0031] Where P ES is the energy storage capacity; is the maximum energy storage capacity; P res is the backup energy storage capacity; res is the ratio of system maximum load to system reserve capacity; P D For the total load demand.

[0032] Furthermore, the establishing of a source-side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind power abandonment rate constraint includes:

[0033] The source-side constraints are specifically:

[0034] φ wind_per =α·φ wind_cur

[0035] φ PV_per =α·φ PV_per

[0036] In the formula, φ wind_per is the wind power penetration rate; φ wind_cur is the wind abandonment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capacity value.

[0037] Furthermore, the establishing of source-storage synergy constraint based on the synergy association between the source-side constraint and the energy storage capacity configuration constraint includes:

[0038] The source-reservoir coordination constraints are specifically:

[0039]

[0040]

[0041] Where P wind_1 is the actual power generated by wind power; P windis wind power installed capacity; P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; wind_per is the wind power penetration rate; P Gen is the total installed capacity; α is the preset energy storage regulation capability value; P PV_1 is the actual photovoltaic power generation; P PV is the photovoltaic installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV_per is the photovoltaic penetration rate.

[0042] Furthermore, the establishing of a source-load-storage coordination constraint based on the coordination association between the source-storage coordination constraint and the demand response constraint includes:

[0043] The source-load-storage coordination constraints are specifically:

[0044]

[0045] In the formula, It is the equivalent power generation of the power system excluding thermal power; The actual wind power generation power when the energy storage regulation capacity is the maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is the maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0046] Furthermore, the establishing of source-grid-load-storage constraints based on the constraint association between the source-load-storage synergy constraints and the dual carbon target constraints includes:

[0047] The source-grid-load-storage constraints are specifically:

[0048]

[0049]

[0050] In the formula, is the maximum thermal power generation power; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import capacity.

[0051] Furthermore, the dual carbon target constraint, the renewable energy penetration rate constraint, the curtailment rate constraint, the demand response constraint, the energy storage capacity configuration constraint, the source side constraint, the source storage coordination constraint, the source load storage coordination constraint and the source grid load storage constraint are combined to establish a source grid load storage optimization model with the goal of minimizing the total cost of the power system, including:

[0052] The objective function of the source-grid-load-storage optimization model is:

[0053]

[0054]

[0055]

[0056] In the formula, C tol Total cost; C inv is the investment cost; C opr For operating costs; the cost of investing in power generation technology; The investment cost for energy storage; operating costs for power generation technologies; emission costs for electricity generation technologies; The operating cost of energy storage; is the demand side response cost.

[0057] Furthermore, based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, specifically:

[0058] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0059] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0060] If each first installed capacity in the first planning scheme cannot satisfy all constraints in the source-grid-load-storage optimization model, iteratively increase each first installed capacity according to a preset increment until each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, and output a second planning scheme;

[0061] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, the upper and lower bounds of the total cost of the power system are calculated based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, each first installed capacity is iteratively reduced according to a preset reduction amount until the upper and lower bounds of the total cost converge, and a second planning scheme is output;

[0062] The second planning scheme is determined as the optimal planning scheme.

[0063] Furthermore, the first installed capacity of each power generation technology in the power system is obtained to form a first planning scheme, specifically:

[0064] Obtain status data of the power system;

[0065] The state data is input into a preset first constraint model for solution to obtain the first installed capacity of each power generation technology in the power system; wherein the constraint conditions in the preset first constraint model are determined based on the upper and lower limits of the installed capacity of the power system, the annual power generation curve, the planned annual load curve, the investment cost, the operating cost and the emission cost.

[0066] Accordingly, the present invention provides a novel multi-objective collaborative planning system for a power system, comprising: a constraint establishment module, a source-side constraint association module, a source-storage constraint association module, a source-load-storage constraint association module, a source-grid-load-storage constraint association module, a model establishment module and a planning module;

[0067] The constraint establishment module is used to establish dual carbon target constraints, renewable energy penetration constraints, abandoned solar and wind power rate constraints, demand response constraints and energy storage capacity configuration constraints based on the state data of the power system;

[0068] The source side constraint association module is used to establish a source side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint;

[0069] The source-storage constraint association module is used to establish a source-storage coordination constraint based on the coordination association between the source-side restriction constraint and the energy storage capacity configuration constraint;

[0070] The source-load-storage constraint association module is used to establish a source-load-storage coordination constraint based on the coordination association between the source-storage coordination constraint and the demand response constraint;

[0071] The source-grid-load-storage constraint association module is used to establish a source-grid-load-storage constraint based on the constraint association between the source-load-storage synergy constraint and the dual-carbon target constraint;

[0072] The model building module is used to combine the dual carbon target constraints, the renewable energy penetration rate constraints, the abandoned solar and wind rate constraints, the demand response constraints, the energy storage capacity configuration constraints, the source side constraints, the source storage coordination constraints, the source load storage coordination constraints and the source grid load storage constraints to establish a source grid load storage optimization model with the goal of minimizing the total cost of the power system;

[0073] The planning module is used to iteratively calculate the installed capacity of each power generation technology in the power system based on the source-grid-load-storage optimization model, obtain the optimal planning scheme after convergence, and plan the installed capacity of each power generation technology in the power system based on the optimal planning scheme.

[0074] The present invention provides a novel multi-objective collaborative planning method and system for power systems. Based on the state data of the power system, dual carbon target constraints, renewable energy penetration rate constraints, abandoned solar and wind rate constraints, demand response constraints, and energy storage capacity configuration constraints are established respectively; based on the constraint correlation and collaborative correlation between the constraints, source-side constraint constraints, source-storage collaborative constraints, source-load-storage collaborative constraints, and source-grid-load-storage constraints are generated, and a source-grid-load-storage optimization model is established with the goal of minimizing the total cost of the power system; based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme. The present invention can improve the economy, safety, and low carbon of power system planning, thereby improving the operating efficiency and safety of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A schematic diagram of a flow chart of an embodiment of a novel multi-objective collaborative planning method for a power system provided by the present invention;

[0076] Figure 2 A flowchart of another embodiment of a novel multi-objective collaborative planning method for a power system provided by the present invention;

[0077] Figure 3 A flowchart of another embodiment of the multi-objective collaborative planning method for a novel power system provided by the present invention;

[0078] Figure 4 A schematic structural diagram of an embodiment of a multi-objective collaborative planning system for a novel power system provided by the present invention. DETAILED DESCRIPTION

[0079] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0080] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0081] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0082] Example 1

[0083] like Figure 1 FIG. 1 is a flow chart of an embodiment of a multi-objective collaborative planning method for a novel power system provided by the present invention. The method includes steps 101 to 107, and each step is specifically as follows:

[0084] Step 101: Based on the status data of the power system, establish dual carbon target constraints, renewable energy penetration constraints, solar and wind curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints respectively.

[0085] In the first embodiment of the present invention, the dual carbon goals include a "carbon peak" goal and a "carbon neutrality" goal. Among them, the "carbon peak" goal can be expressed as the maximum value of the thermal power plants fueled by coal and natural gas reaching the entire planning cycle in the planning start year. The "carbon neutrality" goal can be expressed in the power system as the complete elimination of thermal power plants in the planned implementation year or the carbon emissions generated by them can be achieved through carbon sinks to achieve carbon neutrality in the power system. Therefore, the dual carbon target constraints corresponding to the "dual carbon" goals are specifically:

[0086]

[0087] P Fire (n)≤φ Fire (n)

[0088] Where P Fire is the power generation capacity of the thermal power plant, p is the planning period, which refers to the period from the planning start year to the planning realization year; m is the planning start year; n is the planning realization year; φ Fire It is the carbon neutrality ratio coefficient, which is the ratio coefficient of carbon dioxide emissions from thermal power to achieve carbon neutrality through carbon saving and emission reduction technologies and the carbon sequestration capacity of ecosystems. It can be selected according to the actual conditions of different regions.

[0089] In the first embodiment of the present invention, the renewable energy penetration rate is to stipulate that the installed capacity of renewable energy power generation exceeds at least a certain proportion of the total installed capacity, and the renewable energy penetration rate constraint mainly considers the two power generation modes of wind power and photovoltaic power generation. Therefore, the renewable energy penetration rate constraint is specifically:

[0090]

[0091]

[0092] Where P wind is the installed capacity of wind power; wind_per is the wind power penetration rate; P Gen is the total installed capacity; g is the power generation technology; P PVis the photovoltaic installed capacity; φ PV_per is the photovoltaic penetration rate. Among them, the wind power penetration rate and photovoltaic penetration rate can be selected according to the recommended values ​​of different regions.

[0093] In the first embodiment of the present invention, the wind and solar power abandonment rate is a ratio of the wind power and solar power abandonment during the promotion process, which cannot exceed the actual power generation power. Therefore, the renewable energy penetration rate constraint is specifically:

[0094]

[0095]

[0096] Where P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; P wind is wind power installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV is the installed capacity of photovoltaic power generation. Among them, the wind abandonment rate and solar abandonment rate can be selected according to the recommended values ​​of different regions.

[0097] In the first embodiment of the present invention, the demand response is the ratio of the power load participating in the demand response to the total power load that is expected to be achieved. Therefore, the demand response constraint is specifically:

[0098]

[0099] Where P D_opr is the demand side response load; P D is the total load demand; φ D_opr The size of the demand-side response load can be determined by the proportion of all loads that have undergone demand-side response transformation, and this proportion can be selected based on the recommended values ​​for different regions.

[0100] In the first embodiment of the present invention, energy storage can be used for both power generation and storage, and its installed capacity is limited by geographical conditions. In addition, in order to cope with equipment maintenance and other situations, a backup energy storage capacity should be set, which is specified not to exceed a certain ratio of the total power load demand. Therefore, the energy storage capacity configuration constraints are specifically:

[0101]

[0102]

[0103] Where P ES is the energy storage capacity; is the maximum energy storage capacity considering geographical constraints; P res is the backup energy storage capacity; resis the ratio of the system maximum load to the system reserve capacity, which can be selected according to the recommended values ​​in different regions; P D For the total load demand.

[0104] Step 102: Establish a source-side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind power abandonment rate constraint.

[0105] In the first embodiment of the present invention, wind power and photovoltaic power are the most important technologies in the planning process of the new power system. There is a restrictive correlation between the penetration rate of renewable energy and the wind and solar power abandonment rate. The specific analysis is: as the penetration rate of renewable energy increases, the abandoned wind power and abandoned solar power also gradually increase. Since energy storage is an effective measure to reduce the wind and solar power abandonment rate, within the energy storage adjustment range, a higher renewable energy penetration rate and a lower wind and solar power abandonment rate can be met at the same time. Assuming that the energy storage capacity is constant, the maximum value of the renewable energy generation capacity will be constrained by the maximum allowable wind and solar power abandonment rate. Therefore, based on the restrictive correlation between the renewable energy penetration rate constraint and the wind and solar power abandonment rate constraint, a source-side constraint can be established, specifically:

[0106] φ wind_per =α·φ wind_cur

[0107] φ PV_per =α·φ PV_per

[0108] In the formula, φ wind_per is the wind power penetration rate; φ wind_cur is the wind abandonment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capacity value.

[0109] In the first embodiment of the present invention, α is a preset energy storage regulation capacity value, which represents the regulation capacity of a certain energy storage capacity, and the value can be obtained by designing a simulation experiment. Specifically: a certain specific energy storage capacity is selected, and a variation curve of renewable energy penetration rate and wind and solar power abandonment rate is plotted, and the energy storage regulation capacity value α is obtained from the variation curve. The above transformation realizes the solution of the maximum value of renewable energy penetration rate according to the maximum value of wind and solar power abandonment rate under the condition of certain energy storage capacity, and solves the problem of solving the maximum value of renewable energy power generation under the condition of certain energy storage capacity and meeting the constraints of renewable energy penetration rate and wind and solar power abandonment rate.

[0110] Step 103: Establish source-storage synergy constraints based on the synergy association between the source-side constraint and the energy storage capacity configuration constraint.

[0111] In the first embodiment of the present invention, from the perspective of the system, there is a synergistic correlation between the source side (renewable energy penetration rate and wind and solar power abandonment rate) and the storage side (energy storage capacity configuration) in the process of building a new power system. The specific analysis is: when the energy storage capacity reaches the maximum value, the regulation capacity of the energy storage is also the maximum, which means that under the maximum allowable wind and solar power abandonment rate, the renewable energy penetration rate also reaches the maximum, and the wind power output and photovoltaic output reach the maximum, achieving the synergy between the source side and the storage side. Therefore, based on the synergistic correlation between the source side constraint and the energy storage capacity configuration constraint, a source-storage synergy constraint is established, specifically:

[0112]

[0113]

[0114] Where P wind_1 is the actual power generated by wind power; P wind is wind power installed capacity; P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; wind_per is the wind power penetration rate; P Gen is the total installed capacity; α is the preset energy storage regulation capability value; P PV_1 is the actual photovoltaic power generation; P PV is the photovoltaic installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV_per is the photovoltaic penetration rate.

[0115] Step 104: Based on the collaborative association between the source-load-storage collaborative constraint and the demand response constraint, a source-load-storage collaborative constraint is established.

[0116] In the first embodiment of the present invention, demand response is an adjustable load, and its properties are the same as power generation when considering power balance. Therefore, the power generation on the source side (renewable energy penetration rate and abandoned solar and wind power rates), the load side (demand response) and the storage side (energy storage capacity configuration) can reach the maximum value at the same time, so that the thermal power pressure is minimized when the load demand is constant. Therefore, based on the synergistic correlation between the source-storage synergy constraint and the demand response constraint, a source-load-storage synergy constraint is established, specifically:

[0117]

[0118] In the formula, It is the equivalent power generation of the power system excluding thermal power; The actual wind power generation power when the energy storage regulation capacity is the maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is the maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0119] Step 105: Based on the constraint correlation between the source-load-storage synergy constraint and the dual carbon target constraint, establish a source-grid-load-storage constraint.

[0120] In the first embodiment of the present invention, when the power generation of renewable energy cannot meet the load demand, and thermal power is constrained by the dual carbon target due to its limited carbon sink capacity, appropriate power imports are required to make up for it. Therefore, based on the constraint correlation between the source-load-storage synergy constraint and the dual carbon target constraint, a source-grid-load-storage constraint is established, specifically:

[0121]

[0122]

[0123] In the formula, is the maximum thermal power generation power; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import capacity.

[0124] Step 106: In combination with the dual carbon target constraints, the renewable energy penetration rate constraints, the solar and wind curtailment rate constraints, the demand response constraints, the energy storage capacity configuration constraints, the source side constraints, the source-storage coordination constraints, the source-load-storage coordination constraints and the source-grid-load-storage constraints, a source-grid-load-storage optimization model is established with the goal of minimizing the total cost of the power system.

[0125] As an example of the first embodiment of the present invention, see Figure 2 , is a flow chart of another embodiment of the multi-objective collaborative planning method for the new power system provided by the present invention. The present invention comprehensively constructs the constraints of dual carbon targets, renewable energy penetration rate, wind and solar power abandonment rate, demand response, and energy storage capacity configuration from the three sides of source, load, and storage, and analyzes from three perspectives: the constraint relationship between the renewable energy penetration rate and the wind and solar power abandonment rate on the source side, the collaborative relationship between the three sides of source, load, and storage, and the constraint relationship between the three sides of source, load, and storage and the dual carbon targets, forming a collaborative constraint module that can be used for the planning of new power systems. The application of this collaborative constraint module in the planning of new power systems can derive a more economical, safe, and low-carbon power system planning scheme.

[0126] In the first embodiment of the present invention, the objective function of the source-grid-load-storage optimization model is:

[0127]

[0128]

[0129]

[0130] In the formula, C tol Total cost; C inv is the investment cost; C opr For operating costs; the cost of investing in power generation technology; The investment cost for energy storage; The operating costs of power generation technologies include fuel, labor, maintenance, etc. of various power generation technologies; The emission costs of power generation technology include CO2, SO2, NO x Costs; The operating cost of energy storage, including the loss of energy storage, maintenance, labor and other costs; is the demand side response cost.

[0131] Step 107: Based on the source-grid-load-storage optimization model, iteratively calculate the installed capacity of each power generation technology in the power system, obtain the optimal planning scheme after convergence, and plan the installed capacity of each power generation technology in the power system based on the optimal planning scheme.

[0132] Further, in the first embodiment of the present invention, based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, specifically:

[0133] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0134] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0135] If each first installed capacity in the first planning scheme cannot satisfy all constraints in the source-grid-load-storage optimization model, iteratively increase each first installed capacity according to a preset increment until each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, and output a second planning scheme;

[0136] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, the upper and lower bounds of the total cost of the power system are calculated based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, each first installed capacity is iteratively reduced according to a preset reduction amount until the upper and lower bounds of the total cost converge, and a second planning scheme is output;

[0137] The second planning scheme is determined as the optimal planning scheme.

[0138] Furthermore, in the first embodiment of the present invention, the first installed capacity of each power generation technology in the power system is obtained to form a first planning scheme, specifically:

[0139] Obtain status data of the power system;

[0140] The state data is input into a preset first constraint model for solution to obtain the first installed capacity of each power generation technology in the power system; wherein the constraint conditions in the preset first constraint model are determined based on the upper and lower limits of the installed capacity of the power system, the annual power generation curve, the planned annual load curve, the investment cost, the operating cost and the emission cost.

[0141] As an example of the first embodiment of the present invention, see Figure 3 , which is a flow chart of another embodiment of the multi-objective collaborative planning method for the new power system provided by the present invention. The collaborative constraint module is applied to the planning of the new power system. The two-sided planning model of the new power system is explained as an example of the source-grid-load-storage optimization model. The two-sided planning model includes a planning layer and an operation layer, and its optimization goal is to minimize the total cost. The optimization variables of the planning layer include the installed capacity of various power generation technologies such as wind power, photovoltaics, hydropower, nuclear power, and thermal power, taking into account the reduction of thermal power installed capacity under the constraints of the dual carbon goals and the lower limit of wind power and photovoltaic installed capacity under the constraints of renewable energy penetration. The optimization variables of the operation layer include the output of adjustable power sources such as thermal power, nuclear power, and hydropower, the abandoned power of renewable energy such as wind power and photovoltaics, the generated power and stored power of energy storage, and the power of demand-side response, and the installed capacity of energy storage is solved based on the demand for energy storage in the optimized operation.

[0142] In the process of solving the two-level programming model, firstly, other preset constraints are used, including the upper and lower limits of the installed capacity of the power system, the annual power generation curve, the planned annual load curve, the investment cost, the operating cost and the emission cost, etc., to obtain the upper and lower limits of the installed capacity of each power generation technology; then, they are substituted into the various constraints in the collaborative constraint module; if the load demand cannot be met, the installed capacity of the power generation technology needs to be increased; if the operating demand can be met, the investment cost of energy storage, the operating cost of power generation technology and energy storage, the emission cost, etc. under the corresponding situation can be calculated, so that the upper and lower boundaries of the total cost can be calculated; if the upper and lower boundaries of the total cost do not converge, the installed capacity of the power generation technology can be reduced until the upper and lower boundaries of the total cost converge to obtain the optimal solution. The optimal solution is determined as the optimal planning scheme, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, so as to improve the economy, safety and low carbon of the new power system.

[0143] In summary, the first embodiment of the present invention provides a new type of multi-objective collaborative planning method for power systems. Based on the state data of the power system, dual carbon target constraints, renewable energy penetration constraints, abandoned solar and wind power rate constraints, demand response constraints, and energy storage capacity configuration constraints are established respectively; based on the constraint correlation and collaborative correlation between the constraints, source-side constraint constraints, source-storage collaborative constraints, source-load-storage collaborative constraints, and source-grid-load-storage constraints are generated, and a source-grid-load-storage optimization model is established with the goal of minimizing the total cost of the power system; based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme. The present invention can improve the economy, safety, and low-carbon nature of power system planning, thereby improving the operating efficiency and safety of the power system.

[0144] Example 2

[0145] See also Figure 4 , is a structural diagram of an embodiment of a multi-objective collaborative planning system for a novel power system provided by the present invention, the system comprising a constraint establishment module 201, a source side constraint association module 202, a source storage constraint association module 203, a source load storage constraint association module 204, a source grid load storage constraint association module 205, a model establishment module 206 and a planning module 207;

[0146] The constraint establishment module 201 is used to establish dual carbon target constraints, renewable energy penetration rate constraints, abandoned solar and wind rate constraints, demand response constraints and energy storage capacity configuration constraints based on the state data of the power system;

[0147] The source side constraint association module 202 is used to establish a source side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint;

[0148] The source-storage constraint association module 203 is used to establish source-storage coordination constraints based on the coordination association between the source-side constraint and the energy storage capacity configuration constraint;

[0149] The source-load-storage constraint association module 204 is used to establish a source-load-storage coordination constraint based on the coordination association between the source-storage coordination constraint and the demand response constraint;

[0150] The source-grid-load-storage constraint association module 205 is used to establish a source-grid-load-storage constraint based on the constraint association between the source-load-storage synergy constraint and the dual-carbon target constraint;

[0151] The model building module 206 is used to combine the dual carbon target constraints, the renewable energy penetration rate constraints, the abandoned solar and wind rate constraints, the demand response constraints, the energy storage capacity configuration constraints, the source side constraints, the source storage coordination constraints, the source load storage coordination constraints and the source grid load storage constraints to establish a source grid load storage optimization model with the goal of minimizing the total cost of the power system;

[0152] The planning module 207 is used to iteratively calculate the installed capacity of each power generation technology in the power system based on the source-grid-load-storage optimization model, obtain the optimal planning scheme after convergence, and plan the installed capacity of each power generation technology in the power system based on the optimal planning scheme.

[0153] Furthermore, in the second embodiment of the present invention, based on the state data of the power system, dual carbon target constraints, renewable energy penetration rate constraints, solar and wind curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints are established respectively, including:

[0154] The dual carbon target constraints are specifically:

[0155]

[0156] P Fire (n)≤φ Fire (n)

[0157] Where P Fire is the power generation capacity of the thermal power plant, p is the planning period, m is the planning start year, n is the planning realization year, φ Fire is the carbon neutrality ratio coefficient;

[0158] The renewable energy penetration rate constraint is specifically:

[0159]

[0160]

[0161] Where P wind is the installed capacity of wind power; wind_per is the wind power penetration rate; P Gen is the total installed capacity; g is the power generation technology; P PV is the photovoltaic installed capacity; φ PV_per is the photovoltaic penetration rate;

[0162] The constraints on abandoned solar and wind power rates are specifically:

[0163]

[0164]

[0165] Where Pwind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; P wind is wind power installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV for photovoltaic installed capacity;

[0166] The demand response constraints are specifically:

[0167]

[0168] Where P D_opr is the demand side response load; φ D_opr is the transformation ratio of the demand-side response load; P D For the total load demand;

[0169] The energy storage capacity configuration constraints are specifically:

[0170]

[0171]

[0172] Where P ES is the energy storage capacity; is the maximum energy storage capacity; P res is the backup energy storage capacity; res is the ratio of system maximum load to system reserve capacity; P D For the total load demand.

[0173] Furthermore, in the second embodiment of the present invention, based on the constraint association between the renewable energy penetration rate constraint and the solar and wind power abandonment rate constraint, a source-side constraint is established, including:

[0174] The source-side constraints are specifically:

[0175] φ wind_per =α·φ wind_cur

[0176] φ PV _per=α·φ PV _per

[0177] In the formula, φ wind_per is the wind power penetration rate; φ wind_cur is the wind abandonment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capacity value.

[0178] Furthermore, in the second embodiment of the present invention, based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint, a source-storage collaborative constraint is established, including:

[0179] The source-reservoir coordination constraints are specifically:

[0180]

[0181]

[0182] Where P wind_1 is the actual power generated by wind power; P wind is wind power installed capacity; P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; wind_per is the wind power penetration rate; P Gen is the total installed capacity; α is the preset energy storage regulation capability value; P PV_1 is the actual photovoltaic power generation; P PV is the photovoltaic installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV_per is the photovoltaic penetration rate.

[0183] Further, in the second embodiment of the present invention, based on the cooperative association between the source-storage cooperative constraint and the demand response constraint, a source-load-storage cooperative constraint is established, including:

[0184] The source-load-storage coordination constraints are specifically:

[0185]

[0186] In the formula, It is the equivalent power generation of the power system excluding thermal power; The actual wind power generation power when the energy storage regulation capacity is the maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is the maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0187] Furthermore, in the second embodiment of the present invention, based on the constraint association between the source-load-storage synergy constraint and the dual carbon target constraint, a source-grid-load-storage constraint is established, including:

[0188] The source-grid-load-storage constraints are specifically:

[0189]

[0190]

[0191] In the formula, is the maximum thermal power generation power; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import capacity.

[0192] Further, in the second embodiment of the present invention, in combination with the dual carbon target constraint, the renewable energy penetration rate constraint, the curtailment rate constraint of solar and wind power, the demand response constraint, the energy storage capacity configuration constraint, the source side restriction constraint, the source storage coordination constraint, the source load storage coordination constraint and the source grid load storage constraint, a source grid load storage optimization model is established with the goal of minimizing the total cost of the power system, including:

[0193] The objective function of the source-grid-load-storage optimization model is:

[0194]

[0195]

[0196]

[0197] In the formula, C tol Total cost; C inv is the investment cost; C opr For operating costs; the cost of investing in power generation technology; The investment cost for energy storage; operating costs for power generation technologies; emission costs for electricity generation technologies; The operating cost of energy storage; is the demand side response cost.

[0198] Further, in the second embodiment of the present invention, based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, specifically:

[0199] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0200] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0201] If each first installed capacity in the first planning scheme cannot satisfy all constraints in the source-grid-load-storage optimization model, iteratively increase each first installed capacity according to a preset increment until each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, and output a second planning scheme;

[0202] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, the upper and lower bounds of the total cost of the power system are calculated based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, each first installed capacity is iteratively reduced according to a preset reduction amount until the upper and lower bounds of the total cost converge, and a second planning scheme is output;

[0203] The second planning scheme is determined as the optimal planning scheme.

[0204] Furthermore, in the second embodiment of the present invention, the first installed capacity of each power generation technology in the power system is obtained to form a first planning scheme, specifically:

[0205] Obtain status data of the power system;

[0206] The state data is input into a preset first constraint model for solution to obtain the first installed capacity of each power generation technology in the power system; wherein the constraint conditions in the preset first constraint model are determined based on the upper and lower limits of the installed capacity of the power system, the annual power generation curve, the planned annual load curve, the investment cost, the operating cost and the emission cost.

[0207] In summary, the second embodiment of the present invention provides a new type of multi-objective collaborative planning system for power systems, which is based on the organic combination of modules and the state data of the power system to establish dual carbon target constraints, renewable energy penetration constraints, abandoned solar and wind power rate constraints, demand response constraints and energy storage capacity configuration constraints respectively; based on the constraint correlation and collaborative correlation between the constraints, the source side constraint constraints, source storage collaborative constraints, source load storage collaborative constraints and source grid load storage constraints are generated, and a source grid load storage optimization model is established with the goal of minimizing the total cost of the power system; based on the source grid load storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme. The present invention can improve the economy, safety and low carbon of power system planning, thereby improving the operating efficiency and safety of the power system.

[0208] The specific embodiments described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A novel multi-objective collaborative planning method for power systems, characterized in that: include: Based on the status data of the power system, dual carbon target constraints, renewable energy penetration constraints, wind and solar curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints are established respectively; Based on the constraint correlation between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint, establishing a source-side constraint; Based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint, establishing a source-storage collaborative constraint; Based on the collaborative association between the source-storage collaborative constraint and the demand response constraint, establishing a source-load-storage collaborative constraint; Based on the constraint correlation between the source-load-storage synergy constraint and the dual carbon target constraint, a source-grid-load-storage constraint is established; In combination with the dual carbon target constraint, the renewable energy penetration rate constraint, the abandoned solar and wind rate constraint, the demand response constraint, the energy storage capacity configuration constraint, the source side restriction constraint, the source storage coordination constraint, the source load storage coordination constraint and the source grid load storage constraint, a source grid load storage optimization model is established with the goal of minimizing the total cost of the power system; Based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence. The installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme.

2. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: Based on the state data of the power system, dual carbon target constraints, renewable energy penetration rate constraints, wind and solar curtailment rate constraints, demand response constraints and energy storage capacity configuration constraints are established respectively, including: The dual carbon target constraints are specifically: P F ire(n)≤φ F ire(n) Where P Fire is the power generation capacity of the thermal power plant, p is the planning period, m is the planning start year, n is the planning realization year, φ Fire is the carbon neutrality ratio coefficient; The renewable energy penetration rate constraint is specifically: Where P wind is the installed capacity of wind power; wind_per is the wind power penetration rate; P Gen is the total installed capacity; g is the power generation technology; P PV is the photovoltaic installed capacity; φ PV_per is the photovoltaic penetration rate; The constraints on abandoned solar and wind power rates are specifically: Where P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; P wind is wind power installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV for photovoltaic installed capacity; The demand response constraints are specifically: Where P D_opr is the demand side response load; φ D_opr is the transformation ratio of the demand-side response load; P D For the total load demand; The energy storage capacity configuration constraints are specifically: Where P ES is the energy storage capacity; is the maximum energy storage capacity; P res is the backup energy storage capacity; res is the ratio of system maximum load to system reserve capacity; P D For the total load demand.

3. The multi-objective collaborative planning method of the new power system according to claim 1 is characterized in that: The establishing of a source-side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint includes: The source-side constraints are specifically: f wind_per =a·f wind_cur f PV_per =a·f PV_per In the formula, φ wind_per is the wind power penetration rate; φ wind_cur is the wind abandonment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capacity value.

4. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: The establishing of source-storage synergy constraint based on the synergy association between the source-side constraint and the energy storage capacity configuration constraint includes: The source-reservoir coordination constraints are specifically: Where P wind_1 is the actual power generated by wind power; P wind is wind power installed capacity; P wind_cur is the abandoned wind power; wind_cur is the wind abandonment rate; wind_per is the wind power penetration rate; P Gen is the total installed capacity; α is the preset energy storage regulation capability value; P PV_1 is the actual photovoltaic power generation; P PV is the photovoltaic installed capacity; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV_per is the photovoltaic penetration rate.

5. The multi-objective collaborative planning method for a new type of power system according to claim 1 is characterized in that: The establishing of a source-load-storage coordination constraint based on the coordination association between the source-storage coordination constraint and the demand response constraint includes: The source-load-storage coordination constraints are specifically: In the formula, It is the equivalent power generation of the power system excluding thermal power; The actual wind power generation power when the energy storage regulation capacity is the maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is the maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

6. The multi-objective collaborative planning method for a new type of power system according to claim 1 is characterized in that: The establishing of source-grid-load-storage constraints based on the constraint association between the source-load-storage synergy constraints and the dual-carbon target constraints includes: The source-grid-load-storage constraints are specifically: In the formula, is the maximum thermal power generation power; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import capacity.

7. The multi-objective collaborative planning method for a new type of power system according to claim 1 is characterized in that: The dual carbon target constraint, the renewable energy penetration rate constraint, the abandoned solar and wind rate constraint, the demand response constraint, the energy storage capacity configuration constraint, the source side restriction constraint, the source storage coordination constraint, the source load storage coordination constraint and the source grid load storage constraint are combined to establish a source grid load storage optimization model with the goal of minimizing the total cost of the power system, including: The objective function of the source-grid-load-storage optimization model is: In the formula, C tol Total cost; C inv is the investment cost; C opr For operating costs; the cost of investing in power generation technology; The investment cost for energy storage; operating costs for power generation technologies; emission costs for electricity generation technologies; The operating cost of energy storage; is the demand side response cost.

8. The multi-objective collaborative planning method for a new type of power system according to claim 1 is characterized in that: Based on the source-grid-load-storage optimization model, the installed capacity of each power generation technology in the power system is iteratively calculated, and the optimal planning scheme is obtained after convergence, and the installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, specifically: Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme; Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model; If each first installed capacity in the first planning scheme cannot satisfy all constraints in the source-grid-load-storage optimization model, iteratively increase each first installed capacity according to a preset increment until each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, and output a second planning scheme; If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, the upper and lower bounds of the total cost of the power system are calculated based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, each first installed capacity is iteratively reduced according to a preset reduction amount until the upper and lower bounds of the total cost converge, and a second planning scheme is output; The second planning scheme is determined as the optimal planning scheme.

9. The multi-objective collaborative planning method for a new type of power system according to claim 8 is characterized in that: The obtaining of the first installed capacity of each power generation technology in the power system to form a first planning scheme is specifically: Obtain status data of the power system; The state data is input into a preset first constraint model for solution to obtain the first installed capacity of each power generation technology in the power system; wherein the constraint conditions in the preset first constraint model are determined based on the upper and lower limits of the installed capacity of the power system, the annual power generation curve, the planned annual load curve, the investment cost, the operating cost and the emission cost.

10. A new multi-objective collaborative planning system for power systems, characterized in that: include: Constraint establishment module, source side constraint association module, source storage constraint association module, source load storage constraint association module, source grid load storage constraint association module, model establishment module and planning module; The constraint establishment module is used to establish dual carbon target constraints, renewable energy penetration constraints, abandoned solar and wind power rate constraints, demand response constraints and energy storage capacity configuration constraints based on the state data of the power system; The source side constraint association module is used to establish a source side constraint based on the constraint association between the renewable energy penetration rate constraint and the solar and wind abandonment rate constraint; The source-storage constraint association module is used to establish a source-storage coordination constraint based on the coordination association between the source-side restriction constraint and the energy storage capacity configuration constraint; The source-load-storage constraint association module is used to establish a source-load-storage coordination constraint based on the coordination association between the source-storage coordination constraint and the demand response constraint; The source-grid-load-storage constraint association module is used to establish a source-grid-load-storage constraint based on the constraint association between the source-load-storage synergy constraint and the dual-carbon target constraint; The model building module is used to combine the dual carbon target constraints, the renewable energy penetration rate constraints, the abandoned solar and wind rate constraints, the demand response constraints, the energy storage capacity configuration constraints, the source side constraints, the source storage coordination constraints, the source load storage coordination constraints and the source grid load storage constraints to establish a source grid load storage optimization model with the goal of minimizing the total cost of the power system; The planning module is used to iteratively calculate the installed capacity of each power generation technology in the power system based on the source-grid-load-storage optimization model, obtain the optimal planning scheme after convergence, and plan the installed capacity of each power generation technology in the power system based on the optimal planning scheme.

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