A new multi-objective collaborative planning method and system for power systems

Abstract

The present invention relates to the technical field of power system planning, and discloses a novel multi-objective collaborative planning method and system for power systems. The method establishes dual-carbon target constraints, renewable energy penetration rate constraints, curtailed solar and wind power rate constraints, demand response constraints, and energy storage capacity configuration constraints based on the state data of the power system; based on the constraint correlation and collaborative correlation between the constraints, it generates source-side constraint constraints, source-storage collaborative constraints, source-load-storage collaborative constraints, and source-grid-load-storage constraints, and establishes a source-grid-load-storage optimization model with the goal of minimizing the total cost of the power system; based on the source-grid-load-storage optimization model, it iteratively calculates the installed capacity of each power generation technology in the power system, obtains the optimal planning scheme after convergence, and plans the installed capacity of each power generation technology in the power system 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.

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] Existing new power system planning technologies that consider the coordinated optimization of "source-load-storage" operations typically use constraint modules generated based on power generation technology, typically consisting of upper and lower limits on installed capacity and annual power generation curves. With the introduction of multiple regulations promoting power sector transformation, these new power system planning technologies lack rationality, increase planning costs, and significantly reduce their applicability. Summary of the Invention

[0003] The present invention provides a novel multi-objective collaborative planning method and system for power systems, which can improve the economy, safety and low carbon nature of power system planning, thereby enhancing 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 power systems, comprising:

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

[0006] Establishing a source-side constraint based on the constraint correlation between the renewable energy penetration rate constraint and the curtailed solar and wind power rate constraint;

[0007] Establishing a source-storage synergy constraint based on the synergy correlation between the source-side constraint and the energy storage capacity configuration constraint;

[0008] Establishing a source-load-storage coordination constraint based on the coordination correlation between the source-storage coordination constraint and the demand response 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 constraints, the renewable energy penetration rate constraints, the curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control 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.

[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 an 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 power system status data, dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints are established, 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] 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;

[0020] The constraints on the abandoned solar and wind power rates are specifically:

[0021]

[0022] Where, P wind_cur is the wind power curtailment; wind_cur is the wind curtailment rate; P wind is the installed capacity of wind power; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; P PV is the installed capacity of photovoltaic power generation;

[0023] The demand response constraints are specifically:

[0024]

[0025] Where, P D_opr is the demand side response load; φD_opr is the transformation ratio of the demand-side response load; P D For the total load demand;

[0026] The energy storage capacity configuration constraints are specifically:

[0027]

[0028] 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 entire load demand.

[0029] Furthermore, the establishing of a source-side constraint based on the constraint correlation between the renewable energy penetration constraint and the curtailed solar and wind power rate constraint includes:

[0030] The source-side constraints are specifically:

[0031] φ wind_per =α·φ wind_cur

[0032] φ PV_per =α·φ PV_per

[0033] Where, φ wind_per is the wind power penetration rate; φ wind_cur is the wind curtailment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capability value.

[0034] Furthermore, the establishing of source-storage synergy constraints based on the synergy association between the source-side constraint and the energy storage capacity configuration constraint includes:

[0035] The source-reservoir synergy constraints are specifically:

[0036]

[0037] Where, P wind_1 is the actual power generated by wind power; P wind is the installed capacity of wind power; P wind_cur is the wind power curtailment; wind_cur is the wind curtailment 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 power; P PV is the photovoltaic installed capacity; PPV_cur is the abandoned optical power; PV_cur is the light abandonment rate; PV_per is the photovoltaic penetration rate.

[0038] Furthermore, the establishing of the source-load-storage coordination constraint based on the coordination correlation between the source-storage coordination constraint and the demand response constraint includes:

[0039] The source-load-storage coordination constraints are specifically:

[0040]

[0041] Where, 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 maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0042] Furthermore, the establishing of source-grid-load-storage constraints based on the constraint correlation between the source-load-storage synergy constraint and the dual-carbon target constraint includes:

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

[0044]

[0045] Where, is the maximum thermal power generation capacity; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import power.

[0046] Furthermore, the dual-carbon target constraint, the renewable energy penetration 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:

[0047] The objective function of the source-grid-load-storage optimization model is:

[0048]

[0049] Where 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 of energy storage; operating costs for power generation technologies; emission costs for power generation technologies; The operating cost of energy storage; is the demand-side response cost; p is the planning period.

[0050] 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 an 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, specifically as follows:

[0051] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0052] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0053] 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;

[0054] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, calculating the upper and lower bounds of the total cost of the power system based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, iteratively reducing each first installed capacity according to a preset reduction amount until the upper and lower bounds of the total cost converge, and outputting a second planning scheme;

[0055] The second planning scheme is determined as the optimal planning scheme.

[0056] Furthermore, the first installed capacity of each power generation technology in the power system is obtained to form a first planning scheme, specifically:

[0057] Obtain status data of the power system;

[0058] The state data is input into a preset first constraint model for solution to obtain a 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.

[0059] 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;

[0060] The constraint establishment module is used to establish dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints based on the state data of the power system;

[0061] 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 curtailment rate constraint;

[0062] The source-storage constraint association module is used to establish a source-storage coordination constraint based on the coordination association between the source-side constraint and the energy storage capacity configuration constraint;

[0063] 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-load-storage coordination constraint and the demand response constraint;

[0064] 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;

[0065] The model building module is used to combine the dual carbon target constraints, the renewable energy penetration rate constraints, the curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control 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;

[0066] 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.

[0067] 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 constraints, curtailed solar and wind curtailment 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. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 A flowchart of an embodiment of a novel multi-objective collaborative planning method for a power system provided by the present invention;

[0069] Figure 2 A flowchart of another embodiment of the novel multi-objective collaborative planning method for a power system provided by the present invention;

[0070] Figure 3 A flowchart of another embodiment of the novel multi-objective collaborative planning method for a power system provided by the present invention;

[0071] Figure 4 This is a structural diagram of an embodiment of a new multi-objective collaborative planning system for a power system provided by the present invention. DETAILED DESCRIPTION

[0072] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0073] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0074] The following embodiments of the present invention are described in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments may be combined with each other.

[0075] Example 1

[0076] like Figure 1 FIG. 1 is a flow chart of an embodiment of a novel multi-objective collaborative planning method for a power system provided by the present invention. The method includes steps 101 to 107, and each step is specifically as follows:

[0077] 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.

[0078] In the first embodiment of the present invention, the dual carbon goals include a "carbon peak" goal and a "carbon neutrality" goal. The "carbon peak" goal can be expressed as the maximum value of coal-fired and natural gas-fired power plants reaching the entire planning period 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:

[0079]

[0080] P Fire (n)≤φ Fire (n)

[0081] Where, P Fire is the power generation capacity of the thermal power plant, p is the planning period, which is from the planning start year to the planning realization year; m is the planning start year; n is the planning realization year; φ Fire 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.

[0082] In the first embodiment of the present invention, the renewable energy penetration rate is defined as the percentage of installed capacity of renewable energy generation exceeding the total installed capacity. The renewable energy penetration rate constraint mainly considers wind power and photovoltaic power generation. Therefore, the renewable energy penetration rate constraint is specifically:

[0083]

[0084] 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 wind power penetration rate and photovoltaic penetration rate can be selected according to the recommended values ​​of different regions.

[0085] In the first embodiment of the present invention, the wind and solar curtailment rate is the ratio of the wind power and solar power curtailment during the promotion process, which cannot exceed the actual power generation. Therefore, the renewable energy penetration rate constraint is specifically:

[0086]

[0087] Where, P wind_cur is the wind power curtailment; wind_cur is the wind curtailment rate; P wind is the installed capacity of wind power; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; P PV is the installed capacity of photovoltaic power generation. The wind curtailment rate and solar curtailment rate can be selected according to the recommended values ​​of different regions.

[0088] In the first embodiment of the present invention, the demand response is the ratio of the power load that is expected to participate in the demand response to the total power load. Therefore, the demand response constraint is specifically:

[0089]

[0090] 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 the total load that has undergone demand-side response transformation, and this proportion can be selected based on the recommended values ​​for different regions.

[0091] 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, backup energy storage capacity should be provided to cope with equipment maintenance and other situations, and it is stipulated that it should not exceed a certain ratio of the total power load demand. Therefore, the energy storage capacity configuration constraints are specifically as follows:

[0092]

[0093] Where, P ES is the energy storage capacity; is the maximum energy storage capacity considering geographical restrictions; P res is the backup energy storage capacity; res is 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 entire load demand.

[0094] Step 102: Establish a source-side constraint based on the constraint correlation between the renewable energy penetration constraint and the curtailed solar and wind power rate constraint.

[0095] 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 regulation 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 power generation capacity will be constrained by the maximum value allowed by the 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:

[0096] φ wind_per =α·φ wind_cur

[0097] φ PV_per =α·φ PV_per

[0098] Where, φ wind_per is the wind power penetration rate; wind_cur is the wind curtailment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capability value.

[0099] In the first embodiment of the present invention, α is a preset energy storage regulation capability value, which represents the regulation capability of a certain energy storage capacity. This value can be obtained through a designed simulation experiment. Specifically, a specific energy storage capacity is selected, and a variation curve of the renewable energy penetration rate and the wind and solar power curtailment rate is plotted. The energy storage regulation capability value α is obtained from this variation curve. The above transformation achieves the solution of the maximum renewable energy penetration rate based on the maximum wind and solar power curtailment rate under the condition of a certain energy storage capacity. This solves the problem of solving the maximum renewable energy power generation problem under the conditions of a certain energy storage capacity and meeting the renewable energy penetration rate and wind and solar power curtailment rate constraints.

[0100] Step 103: Based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint, a source-storage collaborative constraint is established.

[0101] 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 curtailment 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 its maximum value, the regulation capacity of the energy storage is also the maximum, which means that under the maximum allowable wind and solar curtailment rate, the renewable energy penetration rate also reaches its maximum, then the wind power output and photovoltaic output reach their maximum, and the synergy between the source side and the storage side is achieved. 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:

[0102]

[0103] Where, P wind_1 is the actual power generated by wind power; P wind is the installed capacity of wind power; P wind_cur is the wind power curtailment; wind_cur is the wind curtailment 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 power; 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.

[0104] 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.

[0105] In the first embodiment of the present invention, demand response is an adjustable load, and its properties are the same as those of power generation when considering power balance. Therefore, the power generation on the source side (renewable energy penetration rate and wind and solar curtailment rate), 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-load-storage synergy constraint and the demand response constraint, a source-load-storage synergy constraint is established, specifically:

[0106]

[0107] Where, 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 maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0108] 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.

[0109] In the first embodiment of the present invention, when renewable energy generation cannot meet load demand, and thermal power generation is constrained by the dual carbon targets due to its limited carbon sink capacity, appropriate electricity imports are required to supplement the demand. 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:

[0110]

[0111] Where, is the maximum thermal power generation capacity; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import power.

[0112] Step 106: Combine the dual carbon target constraints, the renewable energy penetration rate constraints, the curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control constraints, the source-storage coordination constraints, the source-load-storage coordination constraints and the source-grid-load-storage constraints, and establish a source-grid-load-storage optimization model with the goal of minimizing the total cost of the power system.

[0113] 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 curtailment rate, demand response, and energy storage capacity configuration from the three sides of source, load, and storage. It also analyzes the constraint relationship between the renewable energy penetration rate and the wind and solar power curtailment 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 new power system planning. Applying this collaborative constraint module in new power system planning can produce a more economical, secure, and low-carbon power system planning scheme.

[0114] In the first embodiment of the present invention, the objective function of the source-grid-load-storage optimization model is:

[0115]

[0116] Where 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 of energy storage; The operating costs of power generation technologies include fuel, labor, maintenance, and other costs of various power generation technologies; The emission costs of power generation technology, including CO2, SO2, NO x Cost; Energy storage operating costs, including energy storage losses, maintenance, labor costs, etc. is the demand-side response cost; p is the planning period.

[0117] 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.

[0118] Furthermore, 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 after convergence, an optimal planning scheme is obtained. The installed capacity of each power generation technology in the power system is planned based on the optimal planning scheme, specifically as follows:

[0119] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0120] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0121] 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;

[0122] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, calculating the upper and lower bounds of the total cost of the power system based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, iteratively reducing each first installed capacity according to a preset reduction amount until the upper and lower bounds of the total cost converge, and outputting a second planning scheme;

[0123] The second planning scheme is determined as the optimal planning scheme.

[0124] 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:

[0125] Obtain status data of the power system;

[0126] The state data is input into a preset first constraint model for solution to obtain a 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.

[0127] As an example of the first embodiment of the present invention, see Figure 3, 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 in the planning of the new power system. The two-sided planning model of the new power system is explained below 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 dual carbon target constraints and the lower limit of wind power and photovoltaic installed capacity under the renewable energy penetration constraint. 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.

[0128] In the process of solving the bi-level programming model, the upper and lower limits of the installed capacity of each power generation technology are first obtained by using other preset constraints, including the upper and lower limits of the power system's installed capacity, the annual power generation curve, the planned annual load curve, investment costs, operating costs, and emission costs. These limits are then 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 operational demand can be met, the investment cost of energy storage, the operating costs of the power generation technology and energy storage, and the emission costs can be calculated, thereby calculating the upper and lower bounds of the total cost. If the upper and lower bounds of the total cost do not converge, the installed capacity of the power generation technology can be reduced until the upper and lower bounds of the total cost converge, resulting in the optimal solution. This optimal solution is determined as the optimal planning scheme, and based on this optimal planning scheme, the installed capacity of each power generation technology in the power system is planned, thereby improving the economy, safety, and low-carbon nature of the new power system.

[0129] 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 status data of the power system, dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates 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.

[0130] Example 2

[0131] See also Figure 4 , is a schematic structural diagram of an embodiment of a novel multi-objective collaborative planning system for a 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;

[0132] The constraint establishment module 201 is used to establish dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints based on the state data of the power system;

[0133] The source-side constraint association module 202 is configured to establish a source-side constraint based on the constraint association between the renewable energy penetration constraint and the curtailed solar and wind power rate constraint;

[0134] 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;

[0135] The source-load-storage constraint association module 204 is configured to establish a source-load-storage coordination constraint based on the coordination association between the source-load-storage coordination constraint and the demand response constraint;

[0136] 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;

[0137] The model building module 206 is used to combine the dual carbon target constraints, the renewable energy penetration rate constraints, the curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control 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;

[0138] 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.

[0139] Furthermore, in a second embodiment of the present invention, based on the state data of the power system, dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints are established, including:

[0140] The dual carbon target constraints are specifically:

[0141]

[0142] P Fire (n)≤φ Fire (n)

[0143] 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;

[0144] The renewable energy penetration rate constraint is specifically:

[0145]

[0146] 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;

[0147] The constraints on the abandoned solar and wind power rates are specifically:

[0148]

[0149] Where, P wind_cur is the wind power curtailment; wind_cur is the wind curtailment rate; P wind is the installed capacity of wind power; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; P PV is the installed capacity of photovoltaic power generation;

[0150] The demand response constraints are specifically:

[0151]

[0152] 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;

[0153] The energy storage capacity configuration constraints are specifically:

[0154]

[0155] 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; PD For the entire load demand.

[0156] Furthermore, in a second embodiment of the present invention, based on the constraint correlation between the renewable energy penetration rate constraint and the curtailment rate constraint, a source-side constraint is established, including:

[0157] The source-side constraints are specifically:

[0158] φ wind_per =α·φ wind_cur

[0159] φ PV_per =α·φ PV_per

[0160] Where, φ wind_per is the wind power penetration rate; φ wind_cur is the wind curtailment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage regulation capability value.

[0161] Furthermore, in a 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:

[0162] The source-reservoir synergy constraints are specifically:

[0163]

[0164] Where, P wind_1 is the actual power generated by wind power; P wind is the installed capacity of wind power; P wind_cur is the wind power curtailment; wind_cur is the wind curtailment 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 power; 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.

[0165] Furthermore, in a 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:

[0166] The source-load-storage coordination constraints are specifically:

[0167]

[0168] Where, 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 maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

[0169] Furthermore, in a second embodiment of the present invention, 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, including:

[0170] The source-grid-load-storage constraints are specifically:

[0171]

[0172] Where, is the maximum thermal power generation capacity; is the equivalent power generation of the power system excluding thermal power; P D is the total load demand; P import The power import power.

[0173] Furthermore, in a second embodiment of the present invention, a source-grid-load-storage optimization model is established with the goal of minimizing the total cost of the power system by combining 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, including:

[0174] The objective function of the source-grid-load-storage optimization model is:

[0175]

[0176]

[0177] Where 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 of energy storage; operating costs for power generation technologies; emission costs for power generation technologies; The operating cost of energy storage; is the demand-side response cost; p is the planning period.

[0178] Furthermore, in a 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 an 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, specifically as follows:

[0179] Obtaining the first installed capacity of each power generation technology in the power system and forming a first planning scheme;

[0180] Substituting each first installed capacity in the first planning scheme into each constraint in the source-grid-load-storage optimization model;

[0181] 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;

[0182] If each first installed capacity in the first planning scheme satisfies all constraints in the source-grid-load-storage optimization model, calculating the upper and lower bounds of the total cost of the power system based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, iteratively reducing each first installed capacity according to a preset reduction amount until the upper and lower bounds of the total cost converge, and outputting a second planning scheme;

[0183] The second planning scheme is determined as the optimal planning scheme.

[0184] Furthermore, in a second embodiment of the present invention, a first installed capacity of each power generation technology in the power system is obtained to form a first planning scheme, specifically:

[0185] Obtain status data of the power system;

[0186] The state data is input into a preset first constraint model for solution to obtain a 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.

[0187] 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, and respectively establishes dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates, demand response constraints, and energy storage capacity configuration constraints; based on the constraint correlation and collaborative correlation between the constraints, it generates source-side constraint constraints, source-storage collaborative constraints, source-load-storage collaborative constraints, and source-grid-load-storage constraints, and establishes a source-grid-load-storage optimization model with the goal of minimizing the total cost of the power system; based on the source-grid-load-storage optimization model, it iteratively calculates the installed capacity of each power generation technology in the power system, obtains the optimal planning scheme after convergence, and plans the installed capacity of each power generation technology in the power system 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.

[0188] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A novel multi-objective collaborative planning method for power systems, characterized by: include: Based on the status data of the power system, dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints are established respectively; Establishing a source-side constraint based on the constraint correlation between the renewable energy penetration rate constraint and the curtailed solar and wind power rate constraint; Establishing a source-storage synergy constraint based on the synergy correlation between the source-side constraint and the energy storage capacity configuration constraint; Establishing a source-load-storage coordination constraint based on the coordination correlation between the source-storage coordination constraint and the demand response 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 constraints, the renewable energy penetration rate constraints, the curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control 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. Based on the source-grid-load-storage optimization model, iteratively calculate the installed capacity of each power generation technology in the power system, obtain an 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; The establishing of the source-side constraint based on the constraint correlation between the renewable energy penetration constraint and the curtailed solar and wind power rate constraint includes: The source-side constraints are specifically: f wind_per =a·f wind_cur f PV_per =a·f PV_per Where, φ wind_per is the wind power penetration rate; wind_cur is the wind curtailment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage adjustment capability value; The establishing of source-storage collaborative constraints based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint includes: The source-reservoir synergy constraints are specifically: Where, P wind_1 is the actual power generated by wind power; P wind is the installed capacity of wind power; P wind_cur is the wind power curtailment; wind_cur is the wind curtailment 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 power; 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.

2. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: Based on the power system status data, dual carbon target constraints, renewable energy penetration constraints, curtailment of solar and wind power rates constraints, demand response constraints, and energy storage capacity configuration constraints are established, including: The dual carbon target constraints are specifically: P Fire (n)≤φ Fire (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 the abandoned solar and wind power rates are specifically: Where, P wind_cur is the wind power curtailment; wind_cur is the wind curtailment rate; P wind is the installed capacity of wind power; P PV_cur is the abandoned optical power; PV_cur is the light abandonment rate; P PV is the installed capacity of photovoltaic power generation; 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 entire load demand.

3. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: The establishing of the 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: Where, 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 maximum; The actual photovoltaic power generation power when the energy storage regulation capacity is maximum; is the maximum energy storage capacity; is the maximum response load for demand response.

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

5. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: The dual-carbon target constraint, the renewable energy penetration 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: The objective function of the source-grid-load-storage optimization model is: Where 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 of energy storage; operating costs for power generation technologies; emission costs for power generation technologies; The operating cost of energy storage; is the demand-side response cost; p is the planning period.

6. The multi-objective collaborative planning method for a new power system according to claim 1 is characterized in that: The installed capacity of each power generation technology in the power system is iteratively calculated based on the source-grid-load-storage optimization model, and an 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, specifically as follows: 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, calculating the upper and lower bounds of the total cost of the power system based on the first planning scheme; if the upper and lower bounds of the total cost do not converge, iteratively reducing each first installed capacity according to a preset reduction amount until the upper and lower bounds of the total cost converge, and outputting a second planning scheme; The second planning scheme is determined as the optimal planning scheme.

7. The multi-objective collaborative planning method for a new power system according to claim 6 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 as follows: Obtain status data of the power system; The state data is input into a preset first constraint model for solution to obtain a 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.

8. A new multi-objective collaborative planning system for power systems, characterized by: 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, curtailment of solar and wind power rates 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 curtailment 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 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-load-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 curtailment rate constraints of solar and wind power, the demand response constraints, the energy storage capacity configuration constraints, the source-side control 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 an 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; The establishing of the source-side constraint based on the constraint correlation between the renewable energy penetration constraint and the curtailed solar and wind power rate constraint includes: The source-side constraints are specifically: f wind_per =a·f wind_cur f PV_per =a·f PV_per Where, φ wind_per is the wind power penetration rate; wind_cur is the wind curtailment rate; PV_per is the photovoltaic penetration rate; φ PV_cur is the abandoned light rate; α is the preset energy storage adjustment capability value; The establishing of source-storage collaborative constraints based on the collaborative association between the source-side constraint and the energy storage capacity configuration constraint includes: The source-reservoir synergy constraints are specifically: Where, P wind_1 is the actual power generated by wind power; P wind is the installed capacity of wind power; P wind_cur is the wind power curtailment; wind_cur is the wind curtailment 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 power; 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.

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