Power system scheduling method and device considering source-load cooperation, and terminal equipment

Abstract

The invention discloses a power system scheduling method and device considering source-load cooperation and terminal equipment, and belongs to the field of power systems. The method comprises the following steps: constructing a source side constraint and a load side constraint according to obtained day-ahead load data and day-ahead output data; constructing an optimization scheduling model by taking the minimization of the difference value between the total unit scheduling cost and the photovoltaic consumption reward as a target; the total unit scheduling cost is composed of the operation cost of the thermal power generating unit, the hydroelectric generating unit and the energy storage unit, and the new energy power abandoning cost, the water abandoning cost and the load shedding cost; solving the optimization scheduling model under the constructed constraint, obtaining an intra-day operation plan, and regulating and controlling a thermal power generating unit, a hydroelectric generating unit and an energy storage unit of the target power system according to the intra-day operation plan; the intraday operation plan comprises starting and stopping of the thermal power generating unit and intraday output of the thermal power generating unit, the hydroelectric generating unit, the new energy unit and the energy storage unit. By implementing the application, the problem of unbalanced supply and demand of operation of the power system caused by a single-dimensional scheduling mode in the prior art can be solved.

Description

Technical Field

[0001] The present invention relates to the field of power systems, and in particular to a power system scheduling method, apparatus and terminal equipment considering source-load coordination. Background Art

[0002] In the field of power system dispatch, traditional optimization dispatch methods mostly focus only on source-side factors, such as the operating costs and output limits of thermal and hydropower units. This one-dimensional dispatch model leads to numerous problems in power system operation. On the one hand, because it fails to fully consider the real-time changes and demand characteristics of the load side, it can easily lead to an imbalance between power supply and demand, resulting in power shortages during peak hours and large amounts of energy wasted during off-peak hours, significantly reducing the overall operational efficiency of the power system. On the other hand, as the proportion of renewable energy power generation continues to increase, traditional dispatch methods ignore the volatility and intermittency of renewable energy and lack effective strategies for its absorption, resulting in frequent curtailment of renewable energy power. Summary of the Invention

[0003] The present invention provides a power system scheduling method, device and terminal equipment that take source-load coordination into consideration. The method can solve the problem of imbalance between supply and demand in power system operation caused by a single-dimensional scheduling mode in the prior art.

[0004] An embodiment of the present invention provides a power system scheduling method considering source-load coordination, including:

[0005] Obtaining day-ahead load data and day-ahead output data of a target power system;

[0006] Based on the day-ahead load data and the day-ahead output data, source-side constraints and load-side constraints are established, and an optimization scheduling model is constructed with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward; wherein the total unit scheduling cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of hydropower curtailment, and the cost of load shedding;

[0007] Under the constructed source-side constraints and load-side constraints, the optimization scheduling model is solved to obtain a daily operation plan for the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units;

[0008] According to the daily operation plan of the target power system, the thermal power units, hydropower units and energy storage units in the target power system are regulated.

[0009] Furthermore, the optimization scheduling model includes:

[0010] min[cost fire,t +cost hydro,t +cost ES,t -cost pv,t +cost curt,t ];

[0011]

[0012] Where cost fire,t represents the operating cost of the thermal power unit in period t, cost hydro,t represents the operating cost of the hydropower unit in period t, cost ES,t represents the operating cost of the energy storage unit in period t, cost pv,t represents the photovoltaic consumption reward during period t, cost curt,t represents the sum of the cost of curtailed electricity, water and load shedding in period t, f(P fire,i,t ) represents the coal consumption cost of the i-th thermal power unit in period t, T represents the total number of time segments for daily scheduling, P fire,i,t represents the output of the i-th thermal power unit in period t, n represents the total number of thermal power units, represents the startup cost of the i-th thermal power unit, represents the shutdown cost of the i-th thermal power unit, a i , b i and c fire,i is the preset coefficient of the secondary cost function of the thermal power unit; C hydro,h represents the variable operating cost of the h-th hydropower unit under unit output, P hydro,h,t represents the output of the h-th hydropower unit in period t, m represents the total number of hydropower units; C ESc,e represents the cost per unit output when charging the e-th energy storage unit, P ESc,e,t represents the charging output of the e-th energy storage unit in period t, C ESdc,e represents the cost per unit output when the e-th energy storage unit is discharged; P ESdc,e,t represents the discharge output of the e-th energy storage unit in period t, l represents the total number of energy storage units; C PvS represents the unit economic reward for photovoltaic consumption, P v S t represents the photovoltaic absorption capacity in period t; C LS Indicates the unit economic loss of load shedding, LS t Indicates the load shedding amount in period t, C NS Indicates the unit economic loss of renewable energy power abandonment, NS t represents the amount of renewable energy wasted during period t, C HS The unit economic loss of abandoned water, HSt Indicates the amount of water discarded during period t.

[0013] Furthermore, the day-ahead output data includes the day-ahead actual output data of the wind turbine generator set and the day-ahead actual output data of the photovoltaic generator set; the source-side constraints include the wind turbine generator set output constraints and the photovoltaic generator set output constraints;

[0014] The constructing of source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data includes:

[0015] Adding a preset first fluctuation error to the actual output data of the wind turbine generator set on the previous day, and simulating to obtain the maximum output of the wind turbine generator set on the predicted day;

[0016] The preset second fluctuation error is added to the actual output data of the photovoltaic unit on the previous day, and the maximum output of the photovoltaic unit on the predicted day is obtained by simulation;

[0017] The wind turbine output constraint and the photovoltaic unit output constraint are constructed according to the predicted maximum output of the wind turbine generator set within the day and the predicted maximum output of the photovoltaic unit within the day.

[0018] Furthermore, the wind turbine output constraint is specifically:

[0019]

[0020] Where, represents the output of the αth wind turbine in period t, It represents the maximum output of the wind turbine of the αth wind turbine in the predicted day of period t;

[0021] The photovoltaic unit output constraints are specifically:

[0022]

[0023] Where, represents the output of the β-th photovoltaic unit in period t, It represents the predicted maximum output of the β-th photovoltaic unit in the day during period t.

[0024] Furthermore, the load side constraints include load type component summation constraints and transferable load constraints;

[0025] The load type component summation constraint is specifically:

[0026] P t load =P t f +P t re +P tsh ;

[0027] Where, P t load is the total load during period t, P t f is the basic load during period t, P t sh is the transferable load in period t, P t re is the load that can be reduced during period t;

[0028] The transferable load constraints are specifically:

[0029]

[0030]

[0031] Where, τ sh Indicates the transfer status of a certain period within the day, τ sh When it is 0, it means no transfer, τ sh When 1 is taken, it means that the transfer has been completed; sh Indicates the starting period of the load transferable within the day, T sh Indicates the duration of the load that can be transferred within the day; Indicates the lower limit of load power that can be transferred; Indicates the upper limit of load power that can be transferred; represents the total dispatch cost of the transferable load in period t; Indicates the transferable load; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; τ t Indicates the transfer status of the transferable load during period t; Indicates the minimum continuous transfer time.

[0032] Furthermore, the load-side constraint also includes a reducible load constraint;

[0033] The load constraints that can be reduced are specifically:

[0034]

[0035] Where ΔP t re P represents the actual load reduction amount that can be reduced during period t; t re Indicates the maximum load reduction that can be achieved during period t; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; represents the total dispatch cost of curtailable load in period t; The excitation elastic coefficient that can reduce the load is expressed as related; represents the compensation amount for load reduction during period t; γ t Indicates whether the period t is a reducible interval, γ t Taking 0 means that the period t cannot be cut, γ t Taking 1 means that the time period t can be reduced; N max Indicates the maximum number of times the load can be reduced; Indicates the first electricity price boundary value of the time-sharing load segment when the user performs load reduction; Indicates the second electricity price boundary value of the time-of-use load segment when the user performs load reduction; represents the incentive price sequence that can reduce load; max The maximum sensitivity index of load reduction, λ min Minimum sensitivity index for load reduction; N peak is the preset peak load length; is the sequence of incentive electricity price processing, i(·) indicates that the incentive electricity price sequence within the interval is arranged from small to large; Indicates the maximum value of the excitation elastic coefficient.

[0036] Furthermore, the source-side constraint also includes a power balance constraint;

[0037] The power balance constraint is specifically:

[0038]

[0039] P ES,e,t =P ESdc,e,t -P ESc,e,t

[0040] Where, P ES,e,t It represents the output of the e-th energy storage unit at time t, where charging is negative and discharging is positive.

[0041] Furthermore, after solving the optimization scheduling model to obtain the daily operation plan of the target power system, the method further includes:

[0042] Calculating the power generation of the new energy generator set based on the daily output of the new energy generator set;

[0043] Calculating the amount of abandoned new energy power according to the daily output of the new energy unit, the predicted maximum output of the wind turbine unit on the day, and the predicted maximum output of the photovoltaic unit on the day;

[0044] The new energy power abandonment rate and the total new energy power abandonment rate are calculated based on the new energy power abandonment amount and the power generation amount of the new energy unit.

[0045] An embodiment of the present invention further provides a power system dispatching device considering source-load coordination, comprising: a data acquisition module, an optimization dispatching model construction module, an optimization dispatching model solving module, and a control module;

[0046] The data acquisition module is used to acquire the day-ahead load data and the day-ahead output data of the target power system;

[0047] The optimization scheduling model construction module is used to construct source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data, and to construct an optimization scheduling model with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward; wherein the total unit scheduling cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of water curtailment, and the cost of load shedding;

[0048] The optimization scheduling model solving module is used to solve the optimization scheduling model under the constructed source-side constraints and load-side constraints to obtain the daily operation plan of the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units;

[0049] The control module is used to control the thermal power units, hydropower units and energy storage units in the target power system according to the daily operation plan of the target power system.

[0050] The present application also provides a terminal device, including:

[0051] one or more processors;

[0052] a memory, coupled to the processor, for storing one or more programs;

[0053] When the one or more programs are executed by the one or more processors, the one or more processors implement the power system scheduling method considering source-load coordination as described in the above-mentioned embodiment of the invention.

[0054] The following beneficial effects are achieved by implementing the present invention:

[0055] The present invention provides a power system scheduling method, device and terminal equipment considering source-load coordination. The method combines source-side data and load-side data to construct constraint conditions, and constructs an optimization scheduling model with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward. Therefore, by comprehensively considering the two-way constraints on the source side and the load side, it is possible to more accurately match power supply and demand, effectively reduce power supply shortages and power waste, and greatly improve the operating efficiency of the power system. Incorporating new energy absorption rewards into the objective function can effectively stimulate the use of new energy, reduce the phenomenon of new energy power abandonment, and promote the absorption of clean energy. In addition, the optimization scheduling model comprehensively considers the operating costs and various constraints of multiple units, and can achieve coordinated optimization scheduling of multiple energy sources such as thermal power units, hydropower units, and energy storage units, give full play to the complementary advantages of different energy sources, and reduce the system operating costs while ensuring the reliability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the technical solution of the present application, the following is a brief introduction to the drawings required for use in the implementation. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0057] Figure 1 This is a flowchart of a power system dispatching method considering source-load coordination provided by a certain embodiment of the present application;

[0058] Figure 2 This is a structural diagram of a power system dispatching device considering source-load coordination provided by a certain embodiment of the present application;

[0059] Figure 3 This is a schematic diagram of the structure of a terminal device provided in a certain embodiment of the present application. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this application.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned figure descriptions are intended to cover non-exclusive inclusions.

[0062] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0063] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0064] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0065] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0066] In the description of the embodiments of the present application, unless otherwise expressly specified or limited, technical terms such as "installed," "connected," "connected," and "fixed" should be understood in a broad sense. For example, they can refer to fixed connections, detachable connections, or integration; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; internal connections between two components or interactions between two components. Those skilled in the art can understand the specific meanings of the above terms in the embodiments of the present application based on specific circumstances.

[0067] See also Figure 1To address the problem of imbalanced supply and demand in power system operation caused by a single-dimensional dispatching mode in the prior art, an embodiment of the present invention provides a power system dispatching method that considers source-load coordination, including:

[0068] S1. Obtaining the day-ahead load data and the day-ahead output data of the target power system;

[0069] Specifically, the day-ahead load data and the day-ahead output data of the target power system are obtained. In order to optimize the power system scheduling more accurately, the topology of the target power system is pre-set, including the distribution of thermal power units, hydropower units, energy storage units, and new energy units, as well as the load distribution, load characteristics, key node locations, and connection line parameters between the load and power supply in each area on the load side.

[0070] S2. Based on the day-ahead load data and the day-ahead output data, construct source-side constraints and load-side constraints, and build an optimal dispatch model with the goal of minimizing the difference between the total unit dispatch cost and the photovoltaic absorption reward; wherein the total unit dispatch cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of hydropower curtailment, and the cost of load shedding;

[0071] In a preferred embodiment, the day-ahead output data includes the day-ahead actual output data of the wind turbine generator set and the day-ahead actual output data of the photovoltaic generator set; the source-side constraints include the wind turbine generator set output constraints and the photovoltaic generator set output constraints;

[0072] The constructing of source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data includes:

[0073] Adding a preset first fluctuation error to the actual output data of the wind turbine generator set on the previous day, and simulating to obtain the maximum output of the wind turbine generator set on the predicted day;

[0074] The preset second fluctuation error is added to the actual output data of the photovoltaic unit on the previous day, and the maximum output of the photovoltaic unit on the predicted day is obtained by simulation;

[0075] Constructing the wind turbine output constraint and the photovoltaic unit output constraint according to the predicted maximum output of the wind turbine on the day and the predicted maximum output of the photovoltaic unit on the day;

[0076] Specifically, the actual output data of the wind turbine on the previous day is superimposed with a random fluctuation error to simulate the predicted maximum output of the wind turbine on the same day. Assuming that the first fluctuation error fluctuates by 30% and conforms to the normal distribution, the expression for the predicted maximum output of the wind turbine on the same day is as follows:

[0077]

[0078] Where, represents the predicted maximum output of the wind turbine for the αth wind turbine in period t; represents the actual output data of the wind turbine of the αth wind turbine in the t-1 period; N(0,1) represents a random number that obeys the standard normal distribution with a mean of 0 and a standard deviation of 1;

[0079] Specifically, the actual output data of the photovoltaic unit on the previous day is superimposed with a random fluctuation error to simulate the predicted maximum output of the photovoltaic unit within the day. Assuming that the second fluctuation error fluctuates at 30% and conforms to the normal distribution, the expression for predicting the maximum output of the wind turbine unit within the day is as follows:

[0080]

[0081] Where, represents the predicted maximum output of the β-th photovoltaic unit in the day during period t; represents the actual output data of the β-th photovoltaic unit in the day-ahead period t-1; N(0,1) represents a random number that follows a standard normal distribution with a mean of 0 and a standard deviation of 1;

[0082] In an illustrative manner, the wind turbine output constraint and the photovoltaic unit output constraint are constructed based on the predicted maximum output of the wind turbine in a day and the predicted maximum output of the photovoltaic unit in a day:

[0083] In a preferred embodiment, the wind turbine output constraint is specifically:

[0084]

[0085] Where, represents the output of the αth wind turbine in period t, It represents the maximum output of the wind turbine of the αth wind turbine in the predicted day of period t;

[0086] The photovoltaic unit output constraints are specifically:

[0087]

[0088] Where, represents the output of the β-th photovoltaic unit in period t, represents the predicted maximum output of the β-th photovoltaic unit in the day during period t;

[0089] Specifically, the source-side constraint also includes a thermal power unit output ramp constraint, and the specific formula is as follows:

[0090]

[0091] Where, P fire,i,t represents the output of the i-th thermal power unit in period t; u i,t-1 Indicates the start and stop status of the i-th thermal power unit in period t (1 means start, 0 means shutdown); R u represents the upward ramp rate of the i-th thermal power unit in period t; S i,u represents the maximum ramp rate of the i-th thermal power unit; R d represents the downward ramp rate of the i-th thermal power unit in period t; S i,d represents the maximum drop rate of the i-th thermal power unit; P fire,i,min represents the lower limit of the output of the i-th thermal power unit; P fire,i,max represents the output upper limit of the i-th thermal power unit;

[0092] Specifically, the source-side constraints also include energy storage capacity constraints, energy storage charge and discharge constraints, and SOC state constraints;

[0093] The specific formula for the energy storage capacity constraint is as follows:

[0094] E ES,min ≤E ES,e,t ≤E ES,max ;

[0095] Where, E ES,e,t represents the capacity of the energy storage unit e in period t; E ES,min Indicates the minimum capacity of the energy storage unit; E ES,max Indicates the maximum capacity of the energy storage unit;

[0096] The specific formula for the energy storage charge and discharge constraint is as follows:

[0097]

[0098] Where, P ESc,min Indicates the lower limit of the energy storage unit charging power; P ESc,e,t represents the charging power of the energy storage unit e during period t; P ESc,max Indicates the upper limit of the energy storage unit charging power; P ESdc,min Indicates the lower limit of the energy storage unit discharge power; P ESdc,e,t represents the discharge power of the energy storage unit e during period t; P ESdc,max Indicates the upper limit of the energy storage unit's discharge power;

[0099] The specific formula of the SOC state constraint is as follows:

[0100]

[0101] Where, SOC trepresents the SOC state of the energy storage unit e during period t; λ e,ES Represents the self-discharge rate of the energy storage unit e; η e,ESc represents the charging efficiency of the energy storage unit e; Δt represents the difference between two time periods, that is, the time step;

[0102] In a preferred embodiment, the source-side constraint further comprises a power balance constraint;

[0103] The power balance constraint is specifically:

[0104]

[0105] P ES,e,t =P ESdc,e,t -P ESc,e,t ;

[0106] Where, P ES,e,t It represents the output of the e-th energy storage unit at time t, where charging is negative and discharging is positive;

[0107] Specifically, the source side constraints also include hot standby constraints, thermal power unit output constraints and line power flow constraints;

[0108] The specific formula of the hot standby constraint is as follows:

[0109]

[0110] Where u h,t Indicates the start and stop status of the hth hydropower unit in time period t (1 means start, 0 means shutdown) P hydro,h,max represents the upper limit of the output of the i-th hydropower unit; ρ is the system hot standby rate; P t represents the hot standby power requirement during period t;

[0111] The specific formula for the output constraint of the thermal power unit is as follows:

[0112] u i,t P fire,i,min ≤P fire,i,t ≤u i,t P fire,i,max

[0113] The specific formula for the line power flow constraint is as follows:

[0114] P xl,min ≤P xl ≤P xl,max ;

[0115] Where, P xl,min Indicates the minimum power of line xl; P xl,max Indicates the maximum power of line xl; P xlrepresents the power of line xl in period t;

[0116] In a preferred embodiment, the load-side constraints include load type component summation constraints and transferable load constraints;

[0117] The load type component summation constraint is specifically:

[0118] P t load =P t f +P t re +P t sh ;

[0119] Where, P t load is the total load during period t, P t f is the basic load during period t, P t sh is the transferable load in period t, P t re is the load that can be reduced during period t;

[0120] The transferable load constraints are specifically:

[0121]

[0122]

[0123] Where, τ sh Indicates the transfer status of a certain period within the day, τ sh When it is 0, it means no transfer, τ sh When 1 is taken, it means that the transfer has been completed; sh Indicates the starting period of the load transferable within the day, T sh Indicates the duration of the load that can be transferred within the day; Indicates the lower limit of load power that can be transferred; Indicates the upper limit of load power that can be transferred; represents the total dispatch cost of the transferable load in period t; Indicates the transferable load; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; τ t Indicates the transfer status of the transferable load during period t; Indicates the minimum continuous transfer time;

[0124] In a preferred embodiment, the load-side constraint further comprises a reducible load constraint;

[0125] The load constraints that can be reduced are specifically:

[0126]

[0127] Where, It represents the actual amount of load reduction that can be achieved during period t; Indicates the maximum load reduction that can be achieved during period t; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; represents the total dispatch cost of curtailable load in period t; The excitation elastic coefficient that can reduce the load is expressed as related; represents the compensation amount for load reduction during period t; γ t Indicates whether the period t is a reducible interval, γ t Taking 0 means that the period t cannot be cut, γ t Taking 1 means that the time period t can be reduced; N max Indicates the maximum number of times the load can be reduced; Indicates the first electricity price boundary value of the time-sharing load segment when the user performs load reduction; Indicates the second electricity price boundary value of the time-of-use load segment when the user performs load reduction; represents the incentive price sequence that can reduce load; max The maximum sensitivity index of load reduction, λ min Minimum sensitivity index for load reduction; N peak is the preset peak load length; is the sequence of incentive electricity price processing, i(·) indicates that the incentive electricity price sequence within the interval is arranged from small to large; Indicates the maximum value of the excitation elastic coefficient;

[0128] In a preferred embodiment, the optimization scheduling model includes:

[0129] min[cost fire,t +cost hydro,t +cost ES,t -cost pv,t +cost curt,t ];

[0130]

[0131] Where cost fire,t represents the operating cost of the thermal power unit in period t, cost hydro,t represents the operating cost of the hydropower unit in period t, cost ES,t represents the operating cost of the energy storage unit in period t, costpv,t represents the photovoltaic consumption reward during period t, cost curt,t represents the sum of the cost of curtailed electricity, water and load shedding in period t, f(P fire,i,t ) represents the coal consumption cost of the i-th thermal power unit in period t, T represents the total number of time segments for daily scheduling, P fire,i,t represents the output of the i-th thermal power unit in period t, n represents the total number of thermal power units, represents the startup cost of the i-th thermal power unit, represents the shutdown cost of the i-th thermal power unit, a i , b i and c fire,i is the preset coefficient of the secondary cost function of the thermal power unit; C hydro,h represents the variable operating cost of the h-th hydropower unit under unit output, P hydro,h,t represents the output of the h-th hydropower unit in period t, m represents the total number of hydropower units; C ESc,e represents the cost per unit output when charging the e-th energy storage unit, P ESc,e,t represents the charging output of the e-th energy storage unit in period t, C ESdc,e represents the cost per unit output when the e-th energy storage unit is discharged; P ESdc,e,t represents the discharge output of the e-th energy storage unit in period t, l represents the total number of energy storage units; C PvS represents the unit economic reward for photovoltaic consumption, P v S t represents the photovoltaic absorption capacity in period t; C LS Indicates the unit economic loss of load shedding, LS t Indicates the load shedding amount in period t, C NS Indicates the unit economic loss of renewable energy power abandonment, NS t represents the amount of renewable energy wasted during period t, C HS The unit economic loss of abandoned water, HS t Indicates the amount of water discarded during period t.

[0132] S3. Solve the optimization scheduling model under the constructed source-side constraints and load-side constraints to obtain a daily operation plan for the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units;

[0133] Specifically, after completing the construction of source-side constraints and load-side constraints, the optimization scheduling model is solved with the help of a preset optimization algorithm, and finally the intraday operation plan of the target power system is output, wherein the intraday operation plan includes the start and stop of thermal power units, the intraday output of thermal power units, the intraday output of hydropower units, the intraday output of energy storage units and the intraday output of new energy units.

[0134] In a preferred embodiment, after solving the optimization scheduling model to obtain the daily operation plan of the target power system, the method further includes:

[0135] Calculating the power generation of the new energy generator set based on the daily output of the new energy generator set;

[0136] Calculating the amount of abandoned new energy power according to the daily output of the new energy unit, the predicted maximum output of the wind turbine unit on the day, and the predicted maximum output of the photovoltaic unit on the day;

[0137] Calculating the new energy curtailment rate and the total new energy curtailment rate based on the curtailed amount of new energy and the power generation of the new energy unit;

[0138] Specifically, the new energy units include wind turbine units and photovoltaic units; the power generation of the new energy units includes the power generation EW of the wind turbine units and the power generation EPV of the wind turbine units;

[0139] The calculation formula for the wind turbine generator's power generation EW is as follows:

[0140]

[0141] The calculation formula for the wind turbine's power generation EPV is as follows:

[0142]

[0143] Specifically, the amount of new energy curtailment is calculated based on the daily output of the new energy unit, the predicted maximum output of the wind turbine unit, and the predicted maximum output of the photovoltaic unit. The amount of new energy curtailment includes the amount of wind turbine curtailment (AEW) and the amount of photovoltaic curtailment (AEPV).

[0144] The calculation formula for the abandoned power AEW of wind turbines is as follows:

[0145]

[0146] Among them, the calculation formula of the abandoned power AEPV of the photovoltaic unit is as follows:

[0147]

[0148] Specifically, the new energy curtailment rate is calculated based on the amount of curtailed new energy and the power generation of the new energy unit; the new energy curtailment rate includes the curtailment rate η of the wind turbine unit. W and the power abandonment rate η of the photovoltaic unit PV ;

[0149] Specifically, the curtailment rate η of wind turbines is W The calculation formula is as follows:

[0150]

[0151] Specifically, the power abandonment rate η of the photovoltaic unit PV The calculation formula is as follows:

[0152]

[0153] Specifically, the total power abandonment rate η of new energy is calculated based on the amount of abandoned new energy and the power generation of the new energy unit. new , the calculation formula is:

[0154]

[0155] S4. Regulating the thermal power units, hydropower units, and energy storage units in the target power system according to the daily operation plan of the target power system;

[0156] Specifically, according to the intraday operation plan of the target power system, the thermal power units, hydropower units and energy storage units in the target power system are regulated and controlled. For thermal power units, the start-up and shutdown operations are accurately performed according to the start-up and shutdown timings determined by the plan, and during the unit operation period, the power generation power is dynamically adjusted in strict accordance with the intraday output curve to ensure that the power output matches the system demand; for hydropower units, combined with the intraday output adjustment plan, full consideration is given to factors such as water inflow conditions and reservoir water storage scheduling, and the power generation power in different time periods is reasonably allocated to meet the power supply while taking into account the comprehensive utilization of water resources; for energy storage units, the charging and discharging output strategy is followed. During the low power load period, the set charging power is used to absorb surplus electricity to improve energy storage efficiency. During the peak load period or when the output of new energy is insufficient, the stored electricity is released according to the discharge plan.

[0157] See Figure 2 , is a power system dispatching device considering source-load coordination provided by an embodiment of the present invention, comprising: a data acquisition module, an optimization dispatching model construction module, an optimization dispatching model solving module, and a control module;

[0158] The data acquisition module is used to acquire the day-ahead load data and the day-ahead output data of the target power system;

[0159] The optimization scheduling model construction module is used to construct source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data, and to construct an optimization scheduling model with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward; wherein the total unit scheduling cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of water curtailment, and the cost of load shedding;

[0160] The optimization scheduling model solving module is used to solve the optimization scheduling model under the constructed source-side constraints and load-side constraints to obtain the daily operation plan of the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units;

[0161] The control module is used to control the thermal power units, hydropower units and energy storage units in the target power system according to the daily operation plan of the target power system.

[0162] See also Figure 3 , an embodiment of the present application further provides a terminal device, including:

[0163] one or more processors;

[0164] a memory, coupled to the processor, for storing one or more programs;

[0165] When the one or more programs are executed by the one or more processors, the one or more processors implement the power system dispatching method considering source-load coordination as described above.

[0166] The processor is used to control the overall operation of the terminal device to complete all or part of the steps of the above-mentioned power system scheduling method considering source-load coordination. The memory is used to store various types of data to support the operation of the terminal device. These data may include, for example, instructions for any application or method used to operate on the terminal device, as well as application-related data. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0167] In an exemplary embodiment, the terminal device can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to execute the power system dispatching method considering source-load coordination as described in any of the above embodiments, and achieve technical effects consistent with the above methods.

[0168] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A power system dispatching method considering source-load coordination, characterized in that: include: Obtaining day-ahead load data and day-ahead output data of a target power system; Based on the day-ahead load data and the day-ahead output data, source-side constraints and load-side constraints are established, and an optimization scheduling model is constructed with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward; wherein the total unit scheduling cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of hydropower curtailment, and the cost of load shedding; Under the constructed source-side constraints and load-side constraints, the optimization scheduling model is solved to obtain a daily operation plan for the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units; According to the daily operation plan of the target power system, the thermal power units, hydropower units and energy storage units in the target power system are regulated.

2. The power system dispatching method considering source-load coordination according to claim 1, characterized in that: The optimization scheduling model includes: min[cost fire,t +cost hydro,t +cost ES,t -cost pv,t +cost curt,t ]; Where cost fire,t represents the operating cost of the thermal power unit in period t, cost hydro,t represents the operating cost of the hydropower unit in period t, cost ES,t represents the operating cost of the energy storage unit in period t, cost pv,t represents the photovoltaic consumption reward during period t, cost curt,t represents the sum of the cost of curtailed electricity, water and load shedding in period t, f(P fire,i,t ) represents the coal consumption cost of the i-th thermal power unit in period t, T represents the total number of time segments for daily scheduling, P fire,i,t represents the output of the i-th thermal power unit in period t, n represents the total number of thermal power units, represents the startup cost of the i-th thermal power unit, represents the shutdown cost of the i-th thermal power unit, a i , b i and c fire,i is the preset coefficient of the secondary cost function of the thermal power unit; C hydro,h represents the variable operating cost of the h-th hydropower unit under unit output, P hydro,h,t represents the output of the h-th hydropower unit in period t, m represents the total number of hydropower units; C ESc,e represents the cost per unit output when charging the e-th energy storage unit, P ESc,e,t represents the charging output of the e-th energy storage unit in period t, C ESdc,e represents the cost per unit output when the e-th energy storage unit is discharged; P ESdc,e,t represents the discharge output of the e-th energy storage unit in period t, l represents the total number of energy storage units; C PvS represents the unit economic reward for photovoltaic consumption, P v S t represents the photovoltaic absorption capacity in period t; C LS Indicates the unit economic loss of load shedding, LS t Indicates the load shedding amount in period t, C NS Indicates the unit economic loss of renewable energy power abandonment, NS t represents the amount of renewable energy wasted during period t, C HS The unit economic loss of abandoned water, HS t Indicates the amount of water discarded during period t.

3. The power system dispatching method considering source-load coordination according to claim 2, characterized in that: The day-ahead output data includes the day-ahead actual output data of the wind turbine generator set and the day-ahead actual output data of the photovoltaic generator set; the source-side constraints include the wind turbine generator set output constraints and the photovoltaic generator set output constraints; The constructing of source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data includes: Adding a preset first fluctuation error to the actual output data of the wind turbine generator set on the previous day, and simulating to obtain the maximum output of the wind turbine generator set on the predicted day; The preset second fluctuation error is added to the actual output data of the photovoltaic unit on the previous day, and the maximum output of the photovoltaic unit on the predicted day is obtained by simulation; The wind turbine output constraint and the photovoltaic unit output constraint are constructed according to the predicted maximum output of the wind turbine generator set within the day and the predicted maximum output of the photovoltaic unit within the day.

4. The power system dispatching method considering source-load coordination according to claim 3, characterized in that: The wind turbine output constraints are specifically: Where, represents the output of the αth wind turbine in period t, It represents the maximum output of the wind turbine of the αth wind turbine in the predicted day of period t; The photovoltaic unit output constraints are specifically: Where, represents the output of the β-th photovoltaic unit in period t, It represents the predicted maximum output of the β-th photovoltaic unit in the day during period t.

5. The power system dispatching method considering source-load coordination according to claim 4, characterized in that: The load side constraints include load type component summation constraints and transferable load constraints; The load type component summation constraint is specifically: P t load =P t f +P t re +P t sh ; Where, P t load is the total load during period t, P t f is the basic load during period t, P t sh is the transferable load in period t, P t re is the load that can be reduced during period t; The transferable load constraints are specifically: Where, τ sh Indicates the transfer status of a certain period within the day, τ sh When it is 0, it means no transfer, τ sh When the value is 1, it means that the transfer has been completed; t sh Indicates the starting period of the load transferable within the day, T sh Indicates the duration of the load that can be transferred within the day; Indicates the lower limit of load power that can be transferred; Indicates the upper limit of load power that can be transferred; represents the total dispatch cost of the transferable load in period t; P t sh Indicates the transferable load; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; τ t Indicates the transfer status of the transferable load during period t; Indicates the minimum continuous transfer time.

6. The power system dispatching method considering source-load coordination according to claim 5, characterized in that: The load side constraints also include reducible load constraints; The load constraints that can be reduced are specifically: Where ΔP t re P represents the actual load reduction amount that can be reduced during period t; t re Indicates the maximum load reduction that can be achieved during period t; ΔC t represents the cost caused by the incentive during period t and the time-of-use electricity price difference; represents the total dispatch cost of curtailable load in period t; The excitation elastic coefficient that can reduce the load is expressed as related; represents the compensation amount for load reduction during period t; γ t Indicates whether the period t is a reducible interval, γ t Taking 0 means that the period t cannot be cut, γ t Taking 1 means that the time period t can be reduced; N max Indicates the maximum number of times the load can be reduced; Indicates the first electricity price boundary value of the time-sharing load segment when the user performs load reduction; Indicates the second electricity price boundary value of the time-of-use load segment when the user performs load reduction; represents the incentive price sequence that can reduce load; max The maximum sensitivity index of load reduction, λ min Minimum sensitivity index for load reduction; N peak is the preset peak load length; is the sequence of incentive electricity price processing, i(·) indicates that the incentive electricity price sequence within the interval is arranged from small to large; Indicates the maximum value of the excitation elastic coefficient.

7. The power system dispatching method considering source-load coordination according to claim 6, characterized in that: The source side constraints also include power balance constraints; The power balance constraint is specifically: PES,e,t=PESdc,e,t-PESc,e,t Where, P ES,e,t It represents the output of the e-th energy storage unit at time t, where charging is negative and discharging is positive.

8. The power system dispatching method considering source-load coordination according to claim 3, characterized in that: After solving the optimization scheduling model to obtain the daily operation plan of the target power system, the method further includes: Calculating the power generation of the new energy generator set based on the daily output of the new energy generator set; Calculating the amount of abandoned new energy power according to the daily output of the new energy unit, the predicted maximum output of the wind turbine unit on the day, and the predicted maximum output of the photovoltaic unit on the day; The new energy power abandonment rate and the total new energy power abandonment rate are calculated based on the new energy power abandonment amount and the power generation amount of the new energy unit.

9. A power system dispatching device considering source-load coordination, characterized in that: include: Data acquisition module, optimization scheduling model construction module, optimization scheduling model solution module and control module; The data acquisition module is used to acquire the day-ahead load data and the day-ahead output data of the target power system; The optimization scheduling model construction module is used to construct source-side constraints and load-side constraints based on the day-ahead load data and the day-ahead output data, and to construct an optimization scheduling model with the goal of minimizing the difference between the total unit scheduling cost and the photovoltaic absorption reward; wherein the total unit scheduling cost is composed of the operating cost of the thermal power unit, the operating cost of the hydropower unit, the operating cost of the energy storage unit, the cost of renewable energy power curtailment, the cost of water curtailment, and the cost of load shedding; The optimization scheduling model solving module is used to solve the optimization scheduling model under the constructed source-side constraints and load-side constraints to obtain the daily operation plan of the target power system; wherein the daily operation plan includes the start and stop of thermal power units, the daily output of thermal power units, the daily output of hydropower units, the daily output of energy storage units, and the daily output of new energy units; The control module is used to control the thermal power units, hydropower units and energy storage units in the target power system according to the daily operation plan of the target power system.

10. A terminal device, characterized in that: include: one or more processors; a memory, coupled to the processor, for storing one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the power system dispatching method considering source-load coordination as described in any one of claims 1-8.

Images