Integrated planning optimization method for power system considering incentive demand response

Abstract

The present application relates to the technical field of power system expansion planning, and particularly relates to a power system integrated planning optimization method considering incentive demand response, steps of which are as follows: an incentive demand response technical economy model containing demand response operation constraints and incentive cost is established; based on the model, a fast operation simulation constraint set suitable for power system integrated planning is established by taking into account power and energy balance and flexibility balance; based on the model and the constraint set, a power system integrated planning model is established; the integrated planning model is linearly solved to obtain an optimal scheme of economic cost considering demand response mechanism. The present application embeds the demand response mechanism in the integrated planning model to form an integrated planning model considering incentive demand response, which can effectively evaluate demand side flexibility and response potential and take into account the influence of response mechanism on power grid planning, thereby reducing power grid investment cost and providing technical support for power system planning problem under demand response resource participation.

Description

Technical Field

[0001] This invention relates to the field of power system extended planning technology, and more specifically, to an integrated planning and optimization method for power systems that takes into account incentive-driven demand response. Background Technology

[0002] Against the backdrop of building a new power system dominated by new energy sources, the large-scale clustering and grid connection of renewable energy with strong temporal and spatial uncertainties, along with the high-penetration decentralized access, will significantly change the power system's form. The main contradiction in power system planning will gradually shift from the balance between electricity supply and demand to the contradiction between limited system operational flexibility and the highly uncertain and random output of renewable energy. On the other hand, the state has clearly proposed improving the electricity demand response mechanism, promoting the marketization of electricity demand response, and supporting load aggregators to participate in electricity market transactions and system operation regulation. In the future, electricity load will widely participate as a flexibility resource in the source-end volatility and randomness interactions of the new power system.

[0003] In the transformation to a new type of power system, demand response participation in power dispatch has become an inevitable trend. Most existing integrated power system planning and optimization methods have not yet considered the impact of incentive-driven demand response on power system planning. In fact, demand response will become one of the effective means to ensure the safe and stable operation of the power system after large-scale grid integration of renewable energy and to increase the penetration rate of renewable energy. Therefore, it is necessary to address how to consider demand-side response in the planning of a new type of power system dominated by new energy sources. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated planning and optimization method for power systems that considers incentive-driven demand response. This method extends the traditional power system planning problem into an integrated planning and optimization problem that considers incentive-driven demand response. It can accurately assess the response potential of demand-side flexibility and incorporate it into the system flexibility supply balance, thereby reducing investment in power generation capacity and supporting grid construction during the power planning stage and providing a decision-making basis for the formulation of demand response mechanisms.

[0005] The embodiments of the present invention are achieved through the following technical solution: an integrated planning and optimization method for power systems considering incentive-driven demand response, comprising the following steps:

[0006] By analyzing the flexible adjustment capability of demand response on the load side, an incentive-based demand response techno-economic model is established, which includes demand response operation constraints and incentive costs.

[0007] Based on the aforementioned incentive-based demand response techno-economic model, taking into account power balance and flexibility balance, a rapid operation simulation constraint set suitable for integrated power system planning is established.

[0008] Based on the incentive-based demand response techno-economic model and the fast-running simulation constraint set, an integrated power system planning model is established.

[0009] The integrated planning model of the power system is solved linearly to obtain the integrated planning optimization configuration scheme with optimal economic cost that takes into account the demand response mechanism.

[0010] According to a preferred embodiment, the incentive-based demand response techno-economic model includes: establishing a demand response incentive cost model and establishing a set of demand response operational constraints.

[0011] According to a preferred embodiment, the expression for the demand response incentive cost model is as follows:

[0012]

[0013] In the above formula, N represents the demand response incentive cost, t represents time t, and N represents the time t. T N represents the total number of time points, n represents the nth node, and N represents the total number of time points. n N represents the total number of nodes, d represents the demand response load of type d, and N represents the total number of nodes. D Indicates the total demand response category. This indicates the increased cost of participating in load demand response. This indicates the cost reduction associated with participating in load demand response. This indicates the increase in load demand response. This indicates the amount of reduction in load demand response.

[0014] According to a preferred embodiment, the expression for the set of demand response operational constraints is as follows:

[0015]

[0016] In the above formula, This indicates the upper limit corresponding to the reduction and increase in load demand response. This indicates the time period during which load of type d located at node n can participate in load demand response. This represents the set of transferable loads located at node n.

[0017] According to a preferred embodiment, the fast operation simulation constraint set includes demand response operation constraints, system node power balance constraints, transmission network constraints, thermal power unit operation constraints, wind farm operation constraints, photovoltaic power station operation constraints, and energy storage operation constraints.

[0018] According to a preferred embodiment, the expression for the system node power balance constraint is as follows:

[0019]

[0020] In the above formula, g represents the g-th thermal power unit. This represents the set of thermal power units located at node n. Let represent the power generation of the thermal power unit at time t, and w represent the w-th wind turbine unit. This represents the set of wind turbine units located at node n. Let represent the power generation of the wind turbine at time t, and pv represent the pv-th photovoltaic power station. This represents the set of photovoltaic power stations located at node n. Let represent the power generation of the photovoltaic power station at time t, and b represent the b-th energy storage device. This represents the set of energy storage devices located at node n. This represents the discharge power of the energy storage device at time t. This represents the charging power of the energy storage device at time t, where l represents the l-th line. This represents the set of routes starting from node n. This represents the power flow of the line at time t. Let D represent the set of routes terminating at node n. n,t express, This represents the load shedding power at node n at time t. This represents the change in node load at time t due to load participation in demand response at node n;

[0021] The expression for the power transmission network constraint is as follows:

[0022]

[0023] In the above formula, M is a constant. Let θ represent the reactance of the l-th line. l(+),t θ indicates that the node phase angle of the l-th line at time t is positive. l(-),t This indicates that the node phase angle of the l-th line at time t is negative, F l Max This represents the maximum transmission capacity of the l-th line, when... When, it indicates that no line has been built. "Time" indicates the construction of a railway line;

[0024] The expression for the operating constraints of the thermal power unit is as follows:

[0025]

[0026] In the above formula, This represents the installed capacity of the g-th thermal power unit. This indicates the rate at which the g-th thermal power unit adjusts its ramp rate downwards. This represents the power generation capacity of the thermal power unit at time t-1. This indicates the minimum output ratio of thermal power. This represents the online operating capacity of the thermal power unit at time t. This represents the online operating capacity of the thermal power unit at time t-1. This represents the operating capacity of the thermal power unit at time t. This represents the shutdown capacity of the thermal power unit at time t, where time τ represents the difference between the current time t and the shortest start-up or shutdown time of the corresponding unit. This indicates the shortest start-up time for a thermal power unit. This represents the operating capacity of the thermal power unit at time τ. This indicates the downtime of the thermal power unit. This represents the shutdown capacity of the thermal power unit at time τ;

[0027] The expressions for the operating constraints of the wind farm and photovoltaic power station are as follows:

[0028]

[0029] In the above formula, This represents the per-unit predicted output of the w-th wind turbine at time t. This represents the installed capacity of the w-th wind turbine unit. This represents the per-unit predicted output of the pv-th photovoltaic power station at time t. This indicates the installed capacity of the PV-th photovoltaic power station;

[0030] The expression for the operating constraints of the energy storage is as follows:

[0031]

[0032] In the above formula, This represents the installed capacity of the b-th energy storage device. This represents the energy storage capacity status of the b-th energy storage device at time t. η represents the energy storage capacity status of the b-th energy storage device at time t-1. b This represents the charging and discharging efficiency of the b-th energy storage device. This indicates the energy storage duration of the energy storage device.

[0033] According to a preferred embodiment, establishing an integrated power system planning model based on the incentive-based demand response techno-economic model and the fast-running simulation constraint set includes:

[0034] Based on the aforementioned incentive-based demand response techno-economic model, an objective function for an integrated power system planning model considering incentive-based demand response is constructed.

[0035] Based on the fast-running simulation constraint set, a constraint set for an integrated power system planning model considering incentive-driven demand response is constructed.

[0036] According to a preferred embodiment, the objective function of the integrated power system planning model is expressed as follows:

[0037]

[0038] In the above formula, This represents the total system cost. Indicates the cost of power supply investment. Indicates the cost of power grid investment. Indicates system operating costs. N represents the demand response incentive cost. G Indicates the number of thermal power units. N represents the unit investment cost of a thermal power unit. W Indicates the number of wind turbine units. N represents the unit investment cost of wind turbine units. PV Indicates the number of photovoltaic power plants. N represents the unit investment cost of photovoltaics. L Indicates the number of planned routes. This represents the unit investment cost of a power transmission line. This indicates the start-up cost of a thermal power unit. This indicates the downtime cost of thermal power units. C represents the variable generation cost of thermal power units. VoLL This represents the load shedding penalty cost for thermal power units.

[0039] According to a preferred embodiment, the constraint set of the integrated power system planning model considering incentive-based demand response consists of an investment decision constraint set and a rapid operation simulation constraint set, wherein the investment decision constraint set includes investment budget constraints, maximum installed capacity constraints, and renewable energy output penetration rate constraints.

[0040] According to a preferred embodiment, the expression for the investment budget constraint is as follows:

[0041]

[0042] In the above formula, Γ Gen Indicates the upper limit of the unit investment budget, Γ Line This indicates the upper limit of the line investment budget;

[0043] The expression for the maximum installed capacity constraint is as follows:

[0044]

[0045] In the above formula, This indicates the upper limit of the installed capacity of thermal power units. This indicates the upper limit of the installed capacity of wind turbine units. This indicates the upper limit of the installed capacity of a photovoltaic power station. This indicates the upper limit of the installed capacity of energy storage equipment;

[0046] The expression for the renewable energy output penetration rate constraint is as follows:

[0047]

[0048] In the above formula, β RE This indicates the lower limit of renewable energy power output penetration rate.

[0049] The technical solution of the present invention has at least the following advantages and beneficial effects: The integrated planning and optimization method for power systems that considers incentive-based demand response provided by the present invention embeds the demand response mechanism into the power system source-grid-load-storage collaborative planning model, forming an integrated planning model for power systems that considers incentive-based demand response. This model can effectively assess the flexibility and response potential of the demand side and take into account the impact of the response mechanism on grid planning, reduce grid investment costs, and provide technical support for power system planning problems involving demand response resources. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating the integrated planning and optimization method for power systems that considers incentive-driven demand response, as provided in Embodiment 1 of the present invention. Detailed Implementation

[0051] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0052] Example 1

[0053] See Figure 1 As shown, Figure 1 This is a flowchart illustrating the integrated planning and optimization method for power systems that considers incentive-driven demand response, as provided in an embodiment of the present invention.

[0054] The integrated planning and optimization method for power systems that considers incentive-driven demand response provided in this invention includes the following steps:

[0055] A. Based on the load characteristics of the system, and by analyzing the flexible adjustment capability of demand response on the load side, an incentive-based demand response techno-economic model is established, incorporating demand response operational constraints and incentive costs. Specifically, this includes:

[0056] A01. Establish a demand response incentive cost model. Specifically, in this embodiment of the invention, the expression of the demand response incentive cost model is:

[0057]

[0058] In the above formula, N represents the demand response incentive cost, which characterizes the price the power system pays to encourage users to participate in demand response. t represents time t, and N represents the time in time t. T N represents the total number of time points, n represents the nth node, and N represents the total number of time points. n N represents the total number of nodes, d represents the demand response load of type d, and N represents the total number of nodes. D Indicates the total demand response category. This indicates the increased cost of participating in load demand response. This represents the cost reduction associated with participating in demand response, in k$ / MWh. This indicates the increase in load demand response. This indicates the amount of load demand response reduction, in MW.

[0059] A02. Establish a set of operational constraints for demand response. Specifically, in this embodiment of the invention, the expression for the set of operational constraints for demand response is as follows:

[0060]

[0061] In the above formula, This indicates the upper limit corresponding to the reduction and increase in load demand response. This indicates the time period during which load of type d located at node n can participate in load demand response. This represents the set of transferable loads located at node n. It should be noted that the first row in equation (2) represents the constraints on the reduction and increase of load demand response, which are within the range... The second row represents the constraint that the reduction and increase of transferable load at node n are equal during the demand response period.

[0062] After obtaining the incentive-based demand response technology economic model, this embodiment of the invention further executes subsequent step B.

[0063] B. Based on the aforementioned incentive-based demand response techno-economic model, and taking into account power balance and flexibility balance, establish a rapid operation simulation constraint set suitable for integrated source-grid-load-storage planning of the power system. Specifically, this includes:

[0064] The rapid operation simulation constraint set includes demand response operation constraints, system node power balance constraints, transmission network constraints, thermal power unit operation constraints, wind farm operation constraints, photovoltaic power plant operation constraints, and energy storage operation constraints. Each constraint is specifically represented as follows:

[0065] The expression for the system node power balance constraint considering the demand response mechanism is as follows:

[0066]

[0067] The above formula indicates that the active power of each node in the system must be balanced at each time, and the load shedding power should not exceed the required capacity of that node, where g represents the g-th thermal power unit. This represents the set of thermal power units located at node n. Let represent the power generation of the thermal power unit at time t, and w represent the w-th wind turbine unit. This represents the set of wind turbine units located at node n. Let represent the power generation of the wind turbine at time t, and pv represent the pv-th photovoltaic power station. This represents the set of photovoltaic power stations located at node n. Let represent the power generation of the photovoltaic power station at time t, and b represent the b-th energy storage device. This represents the set of energy storage devices located at node n. This represents the discharge power of the energy storage device at time t. This represents the charging power of the energy storage device at time t, where l represents the l-th line. This represents the set of routes starting from node n. This represents the power flow of the line at time t. Let D represent the set of routes terminating at node n. n,t express, This represents the load shedding power at node n at time t. This represents the change in node load at time t due to load participation in demand response at node n.

[0068] The expressions for transmission network constraints are as follows:

[0069]

[0070] In the above formula, The reactance of the l-th line is expressed in ohms, θ. l(+),t θ indicates that the node phase angle of the l-th line at time t is positive. l(-),t This indicates that the node phase angle of the l-th line at time t is negative, F l MaxThe maximum transmission capacity of the l-th line is expressed in MW; the first line of the above formula represents the power flow constraint of the line to be built, where M is a sufficiently large constant. When, it indicates that no line has been built. and No direct correlation; when When, it indicates the construction of the route. The second line imposes constraints on the power flow of the line. When no line has been built, the power flow on the line must not exceed the line's transmission capacity.

[0071] The expressions for the operating constraints of thermal power units are as follows:

[0072]

[0073] It should be noted that in equation (5), the first row is the minimum and maximum output constraints of the thermal power unit; the second row is the constraint on the upward and downward ramping capabilities of the thermal power unit; the third row is the constraint on the output and online start-up capacity of the thermal power unit; the fourth row gives the relationship between the online capacity, start-up capacity and shutdown capacity of the thermal power unit; and the fifth row is the constraint on the minimum start-up time and minimum shutdown time of the thermal power unit.

[0074] In the above formula, This represents the installed capacity of the g-th thermal power unit, in MW. This indicates the rate at which the g-th thermal power unit adjusts its ramp rate downwards. This represents the power generation capacity of the thermal power unit at time t-1. This indicates the minimum output ratio of thermal power. This represents the online operating capacity of the thermal power unit at time t. This represents the online operating capacity of the thermal power unit at time t-1. This represents the operating capacity of the thermal power unit at time t. This represents the shutdown capacity of the thermal power unit at time t, where time τ represents the difference between the current time t and the shortest start-up or shutdown time of the corresponding unit. This indicates the shortest start-up time for a thermal power unit. This represents the operating capacity of the thermal power unit at time τ. This indicates the downtime of the thermal power unit. This represents the shutdown capacity of the thermal power unit at time τ.

[0075] The expressions for the operating constraints of wind farms and photovoltaic power plants are as follows:

[0076]

[0077] It should be noted that Equation (6) means that the output of any wind farm and photovoltaic power station shall not exceed the predicted wind power value and the predicted photovoltaic power value at that moment.

[0078] In the above formula, This represents the per-unit predicted output of the w-th wind turbine at time t. This represents the installed capacity of the w-th wind turbine, in MW. This represents the per-unit predicted output of the pv-th photovoltaic power station at time t. This indicates the installed capacity of the pv-th photovoltaic power station, in MW.

[0079] The expression for the operating constraints of electric energy storage is as follows:

[0080]

[0081] It should be noted that in equation (7), the first and second lines require that the charge and discharge rates of the energy storage device do not exceed the installed capacity; the third line is the energy balance equation of the energy storage device; and the fourth line requires that the energy storage level of the energy storage device does not exceed its energy capacity.

[0082] In the above formula, This represents the installed capacity of the b-th energy storage device, in MW. This represents the energy storage capacity status of the b-th energy storage device at time t, in MWh. η represents the energy storage capacity status of the b-th energy storage device at time t-1. b This represents the charging and discharging efficiency of the b-th energy storage device. This indicates the energy storage duration of the energy storage device.

[0083] After obtaining a fast-running simulation constraint set suitable for integrated power system planning, this embodiment of the invention further executes subsequent step C.

[0084] C. Based on the aforementioned incentive-based demand response techno-economic model and the fast-operation simulation constraint set for integrated power system source-grid-load-storage planning, establish an integrated power system source-grid-load-storage planning model considering incentive-based demand response. Specifically, this includes:

[0085] C01. Based on the aforementioned incentive-based demand response techno-economic model, an objective function is constructed for an integrated power system source-grid-load-storage planning model that considers incentive-based demand response, aiming to minimize the total system cost; specifically, in this embodiment of the invention, the expression of the objective function is as follows:

[0086]

[0087] It should be noted that in equation (8), the second row indicates that the types of units considered in the model include traditional thermal power units, wind power units, and photovoltaic power plants, and the unit investment cost is composed of the investment costs of these three types of units; the third row is the construction investment cost of transmission lines; the fourth row is the expected operating cost of the system, including the start-up cost of thermal power units, the cost of sudden power generation, the cost of shutdown, the operating cost, and the penalty cost of load shedding; the fifth row is the demand response incentive cost of the system.

[0088] In the above formula, This represents the total system cost. Indicates the cost of power supply investment. Indicates the cost of power grid investment. Indicates system operating costs. N represents the demand response incentive cost. G Indicates the number of thermal power units. N represents the unit investment cost of a thermal power unit. W Indicates the number of wind turbine units. N represents the unit investment cost of wind turbine units. PV Indicates the number of photovoltaic power plants. N represents the unit investment cost of photovoltaics. L Indicates the number of planned routes. This represents the unit investment cost of a power transmission line. This indicates the start-up cost of a thermal power unit. This indicates the downtime cost of thermal power units. C represents the variable generation cost of thermal power units. VoLL This represents the load shedding penalty cost for thermal power units.

[0089] C02. Based on the aforementioned fast-running simulation constraint set, a constraint set for an integrated power system planning model considering incentive-driven demand response is constructed. Specifically, in this embodiment of the invention, the constraint set of the integrated power system planning model considering incentive-driven demand response consists of an investment decision constraint set and a fast-running simulation constraint set, wherein the investment decision constraint set includes investment budget constraints, maximum installed capacity constraints, and renewable energy output penetration rate constraints. Details are as follows:

[0090] The expression for the investment budget constraint is as follows:

[0091]

[0092] It should be noted that there are investment budget restrictions for power system planning. Equation (9) requires that the investment in the construction of generating units and lines shall not exceed the investment budget for generating units and lines.

[0093] In the above formula, Γ Gen Indicates the upper limit of the unit investment budget, ΓLine This indicates the upper limit of the line investment budget.

[0094] The expression for the maximum installed capacity constraint is as follows:

[0095]

[0096] It should be noted that, due to limitations in technology and geographical environment, there is an upper limit to the installed capacity of each unit; the first to fourth inequalities in equation (10) are the limits on the installed capacity of traditional thermal power units, wind power units, photovoltaic power stations and energy storage devices, respectively.

[0097] In the above formula, This indicates the upper limit of the installed capacity of thermal power units. This indicates the upper limit of the installed capacity of wind turbine units. This indicates the upper limit of the installed capacity of a photovoltaic power station. This indicates the upper limit of the installed capacity of energy storage equipment.

[0098] The expression for the renewable energy output penetration rate constraint is as follows:

[0099]

[0100] In the above formula, β RE This represents the lower limit of renewable energy output penetration rate set in this embodiment, requiring β... RE A proportion of the load needs to be supplied by renewable energy generation.

[0101] After obtaining the integrated planning model of the power system, this embodiment of the invention further executes the subsequent step D.

[0102] D. Solve the integrated power system planning model linearly to obtain the optimal configuration scheme of power source, grid, load, and energy storage that takes into account the economic cost of demand response mechanism. Specifically, the integrated power system planning model is composed of equations (2) to (11), and the optimization problem therein is a large-scale mixed integer optimization problem. It can be solved by calling one of the CPLEX solvers or the IPOPT solver, or other solvers, to obtain the optimal planning scheme of the power system (economic).

[0103] The integrated planning and optimization method for power systems that considers incentive-driven demand response provided by this invention embeds the demand response mechanism into the power system source-grid-load-storage collaborative planning model, forming an integrated planning model for power systems that considers incentive-driven demand response. This model can effectively assess demand-side flexibility and response potential and take into account the impact of the response mechanism on grid planning, thereby reducing grid investment costs and providing technical support for power system planning problems involving demand response resources.

[0104] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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.

Claims

1. A power system integrated planning and optimization method considering incentive-driven demand response, characterized in that, Includes the following steps: By analyzing the flexible adjustment capability of demand response on the load side, an incentive-based demand response techno-economic model is established, which includes demand response operation constraints and incentive costs. Based on the aforementioned incentive-based demand response techno-economic model, taking into account power balance and flexibility balance, a fast operation simulation constraint set suitable for integrated power system planning is established. The fast operation simulation constraint set includes demand response operation constraints, system node power balance constraints, transmission network constraints, thermal power unit operation constraints, wind farm operation constraints, photovoltaic power station operation constraints, and energy storage operation constraints. Based on the aforementioned incentive-based demand response techno-economic model and rapid operation simulation constraint set, an integrated power system planning model is established with the goal of minimizing the total system cost. The total system cost includes power source investment cost and grid investment cost. The constraint set of the integrated power system planning model includes an investment decision constraint set, which includes investment budget constraints, maximum installed capacity constraints, and renewable energy output penetration rate constraints. The integrated power system planning model is solved linearly to obtain the integrated planning optimization configuration scheme that takes into account the economic cost of the demand response mechanism. The incentive-based demand response techno-economic model includes: establishing a demand response incentive cost model and establishing a set of demand response operational constraints. The expression for the demand response incentive cost model is as follows: （1） In the above formula, Indicates the cost of incentives for demand response. t Indicates the first t time, Indicates the total number of moments. n Indicates the first n Node No. Represents the total number of nodes. d Indicates the first d Demand response load, Indicates the overall demand response category. This indicates the increased cost of participating in load demand response. This indicates the cost reduction associated with participating in load demand response. This indicates the increase in load demand response. This represents the amount of load demand response reduction; the set of demand response operating constraints includes: Constraints on the reduction and increase of load demand response; The reduction and increase of transferable load within a node during the demand response period are equal; The establishment of an integrated power system planning model based on the incentive-based demand response techno-economic model and the fast-running simulation constraint set includes: Based on the aforementioned incentive-based demand response techno-economic model, an objective function is constructed to minimize the total system cost of an integrated power system planning model that considers incentive-based demand response. The total system cost also includes system operating costs and demand response incentive costs. Based on the fast-running simulation constraint set, a constraint set for an integrated power system planning model considering incentive-driven demand response is constructed.

2. The integrated planning and optimization method for power systems considering incentive-driven demand response as described in claim 1, characterized in that, The system node power balance constraint is as follows: the active power of each node in the system must be balanced at all times, and the load shedding power should not be greater than the load demand of that node. The power transmission network constraints include power flow constraints on the lines to be built and constraints on the power flow of the lines. The constraints on the power flow of the lines are: when no lines are built, the power flow on the lines is 0; when lines are being built, the power flow on the lines should not exceed the transmission capacity of the lines. The operating constraints of the thermal power units include: minimum and maximum output constraints of the thermal power units, upward and downward ramping capacity constraints of the thermal power units, output and online start-up capacity constraints of the thermal power units, minimum start-up time and minimum shutdown time constraints of the thermal power units, and the relationship constraints between online capacity, start-up capacity and shutdown capacity of the thermal power units. The operating constraints for wind farms and photovoltaic power stations are as follows: the output of any wind farm or photovoltaic power station should not exceed the predicted wind power output and the predicted photovoltaic power output at that moment. The constraints for the operation of the energy storage are: the charging and discharging efficiency of the energy storage device does not exceed the installed capacity, the energy balance of the energy storage device, and the energy storage level of the energy storage device does not exceed its electrical capacity.

3. The integrated planning and optimization method for power systems considering incentive-driven demand response as described in claim 2, characterized in that, The investment budget constraint is that the investment in the construction of generating units and transmission lines should not exceed the investment budget for the generating units and transmission lines. The maximum installed capacity constraint is that the installed capacity of thermal power units, wind power units, photovoltaic power stations and energy storage equipment should not exceed their corresponding upper limits. The renewable energy output penetration rate constraint is: the load with a renewable energy output penetration rate ratio needs to be supplied by renewable energy power generation.

4. The integrated planning and optimization method for power systems considering incentive-driven demand response as described in claim 3, characterized in that, The solver used includes, but is not limited to, the CPLEX solver or the IPOPT solver.

Images