Electric heating collaborative virtual energy storage flexibility quantitative calculation method and system based on coupling space

Abstract

The invention discloses an electric heating collaborative virtual energy storage flexibility quantitative calculation method and system based on a coupling space. The method comprises the following steps: constructing a DHS electric heating collaborative virtual energy storage model in a multi-space coupling scene based on a flexibility index; establishing an adjustment constraint of a CHP device in the DHS and an adjustment constraint of a heat supply network in the DHS in a constant current-variable temperature mode; and performing electric-heating cooperative virtual energy storage flexibility boundary point search on DHS electric-heating cooperative virtual energy storage to obtain a DHS electric-heating cooperative virtual energy storage flexibility quantification result. The method can efficiently and accurately represent the flexibility of the electric-heating collaborative virtual energy storage.

Description

Technical Field

[0001] The present invention belongs to the technical field of integrated energy system optimization and regulation, and relates to a method and system for quantitatively calculating the flexibility of electric-thermal coordinated virtual energy storage based on coupling space. Background Art

[0002] In the process of achieving carbon-neutral energy transformation, with the rapid growth of intermittent renewable energy access, the demand for flexible resources in the power system (EPS) is increasing. District heating systems (DHS) can provide a lot of flexibility to the power system by using thermal energy storage in the heating network through coupling devices such as combined heat and power (CHP).

[0003] In the study of quantifying the flexibility provided by DHS to the power system, some studies have proposed methods to quantify the maximum flexibility of DHS and discussed the impact of factors such as pipeline length, heating temperature and ambient temperature on the flexibility of DHS; other studies have proposed a flexibility provision mechanism to quantify the real-time operation flexibility of DHS; in addition, heuristic methods have also been used to estimate the power flexibility of DHS, and the quantitative results are used to reduce the phenomenon of wind power abandonment. At the same time, some studies have proposed a set of indicators to evaluate the power, energy and regulation flexibility of DHS. However, these flexibility quantification methods are mainly aimed at DHS with a single energy source, but many DHS have multi-point coupling characteristics with the power system through various coupling forms, which makes the flexibility quantification results inaccurate. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and system for quantitative calculation of the flexibility of electric-thermal synergistic virtual energy storage based on coupling space, which characterizes the electric-thermal synergistic virtual energy storage flexibility of DHS within the feasible operating area of ​​the DHS control variable space, and can efficiently and accurately characterize the quantitative results of the adjustable range of flexibility expressed by DHS to the power system.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of the present invention provides a method for quantitatively calculating the flexibility of electric-thermal coordinated virtual energy storage based on coupled space, comprising:

[0007] Based on the flexibility index, a DHS electric-thermal synergistic virtual energy storage model was constructed under multi-space coupling scenarios;

[0008] Establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode;

[0009] Based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints, a search is performed on the DHS electric-thermal synergistic virtual energy storage for the flexibility boundary points of the electric-thermal synergistic virtual energy storage to obtain a quantitative result of the DHS electric-thermal synergistic virtual energy storage flexibility.

[0010] Preferably, the DHS electric-thermal synergistic virtual energy storage model in a multi-space coupling scenario constructed based on the flexibility index is as follows:

[0011]

[0012] Among them, φ FR is the set of feasible domains projected onto the control variable space of DHS;

[0013] P CHP,flex is the control variable space of DHS; x is the state variable of DHS;

[0014] X is the boundary constraint; f is the operation model of DHS;

[0015] is the flexibility index of CHP unit g in DHS.

[0016] Preferably, the flexibility index of the CHP device g is:

[0017]

[0018]

[0019] in, The adjustable power flexibility that the CHP device can express;

[0020] represents the actual output power of CHP device g during time period t;

[0021] represents the unchanged power of CHP unit g during time period t;

[0022] t 0 Provide DHS with flexibility in the timing of the start of the process;

[0023] t f Indicates that flexibility provides duration.

[0024] Preferably, the state variables of the DHS are:

[0025]

[0026] in, represents the heat output of CHP unit g in time period t;

[0027] represents the actual output power of CHP device g in time period t;

[0028] represents the heat output of GB unit w in time period t;

[0029] and are the inlet and outlet water temperatures of the water supply pipe l of the heating network in the DHS in time period t, respectively;

[0030] and are the inlet and outlet temperatures of the return pipe z of the heating network in DHS during time period t.

[0031] Preferably, the regulation constraints of the CHP device in the DHS include:

[0032] 1) Power output and heat output constraints of CHP devices:

[0033]

[0034] in, represents the actual output power of CHP device g in time period t;

[0035] represents the heat output of CHP unit g in time period t;

[0036] and represents the power output and heat output of the CHP unit g at the vertex of the nth operating region in time period t;

[0037] α g,η represents the auxiliary variable describing the CHP unit g at the vertex of the nth operating region;

[0038] Indicates the number of vertices in the running area; represents a collection of CHP units;

[0039] 2) Climbing constraints of CHP devices:

[0040]

[0041] in, and are the ascending and descending ramp limits of the CHP unit g, respectively;

[0042] represents the actual output power of CHP device g in time period t, t-1;

[0043] Represents a collection of CHP units.

[0044] Preferably, the regulation constraints of the heating network in the DHS include:

[0045] 1) Temperature fluctuations in the pipeline:

[0046]

[0047]

[0048] in, is the supply and return pipe set; ρ is the fluid density;

[0049] A l , A z is the internal cross-sectional area of ​​the water supply pipe l and the return pipe z;

[0050] is the average water supply temperature of water supply pipe l in time period t;

[0051] is the average return water temperature of return pipe z in time period t;

[0052] is the average water supply temperature of water supply pipe l in time period t-1;

[0053] is the average return water temperature of return pipe z in time period t-1;

[0054] Δt is the time step of flexible scheduling;

[0055] and are the mass flow rates of the water supply pipe l and the return pipe z in time period t respectively; and are the inlet and outlet temperatures of the water supply pipe l in time period t; L l is the length of the water supply pipe l; z is the length of the return pipe z;

[0056] T t a is the ambient temperature during time period t; c p is the specific heat capacity of water;

[0057] and are the inlet and outlet temperatures of the return pipe z in time period t; R l is the thermal resistance of the water supply pipe l; R z is the thermal resistance of the return pipe z;

[0058] 2) Temperature mixing constraint description:

[0059]

[0060] in, is the set of pipes through which the working fluid flows out from node n;

[0061] is the set of pipelines from which the working fluid flows into node n;

[0062] is a collection of pipeline mixing nodes;

[0063] is the mass flow rate of the water supply pipe l in time period t;

[0064] is the mass flow rate of the return pipe z in time period t;

[0065] and are the inlet and outlet water temperatures of the water supply pipe l in time period t, respectively;

[0066] and are the inlet and outlet water temperatures of the return pipe z in time period t, respectively;

[0067] 3) The energy balance constraints of the heat source node are as follows:

[0068]

[0069] in, is the set of all CHP devices connected to node n;

[0070] is the set of all GB units connected to node n;

[0071] is the set of heat source nodes;

[0072] is the inlet water temperature of the water supply pipe l in time period t;

[0073] is the outlet water temperature of return pipe z in time period t;

[0074] represents the heat output of CHP unit g and GB unit w in time period t;

[0075] c p is the specific heat capacity of water; Assemble the water supply and return pipes;

[0076] is the mass flow rate of the water supply pipe l in time period t;

[0077] is the mass flow rate of the return pipe z in time period t;

[0078] 4) The energy balance constraints of the heat exchange station node are as follows:

[0079]

[0080] in, is the heat load of node n in time period t;

[0081] c p is the specific heat capacity of water;

[0082] is the mass flow rate in pipeline l during time period t;

[0083] is the inlet water temperature of the water supply pipe l in time period t;

[0084] is the outlet water temperature of return pipe l in time period t;

[0085] is the set of nodes with heat loads.

[0086] Preferably, based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints, the electric-thermal synergistic virtual energy storage is searched for the DHS electric-thermal synergistic virtual energy storage flexibility boundary points to obtain the DHS electric-thermal synergistic virtual energy storage flexibility quantification results, including:

[0087] The following electric-thermal synergistic virtual energy storage flexibility boundary point search problem model is established based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints as constraints:

[0088]

[0089] In the formula, σ(P CHP,flex ) is the measurement function;

[0090] By setting the measurement function and solving the problem model, the flexibility boundary of the DHS electric-thermal synergistic virtual energy storage is obtained as the quantification result of the DHS electric-thermal synergistic virtual energy storage flexibility.

[0091] Preferably, the process of obtaining the DHS electric-thermal synergistic virtual energy storage flexibility boundary by setting the measurement function and solving the problem model is:

[0092] 1) The measurement function σ(P CHP,flex ) is set to σ 1 (PCHP,flex )=(-1) θ e T P CHP,flex , by solving the problem model, the maximum upward and downward flexibility of any CHP in DHS is obtained as the starting boundary point; where e is a standard unit vector, when θ is 0, the maximum upward flexibility of any CHP is obtained; when θ is 1, the maximum downward flexibility of any CHP is obtained;

[0093] 2) The measurement function σ(P CHP,flex ) is set to σ 2 (P CHP,flex ) = d T P CHP,flex , where d is the search direction vector, and the direction of the angle bisector between the adjacent boundary points and the initial operating point of the CHP unit is taken as the search direction. By solving the problem model, the boundary points of the feasible region under any CHP power output combination in the DHS are obtained as new boundary points, and the distance Δd between the new boundary point and the connecting line of the original adjacent boundary point and the distance d between the new boundary point and the initial operating point of the CHP unit are obtained;

[0094] 3) If all boundary points satisfy Δd / d<ε and reach the fitting accuracy of the boundary, stop searching and construct the convex hull of all boundary points to characterize the DHS electric-thermal synergistic virtual energy storage flexibility boundary, otherwise return to 2).

[0095] The second aspect of the present invention provides a coupled space-based electric-thermal collaborative virtual energy storage flexibility quantitative calculation system, comprising:

[0096] Model building module, used to build a DHS electric-thermal synergistic virtual energy storage model in multi-space coupling scenarios based on flexibility index;

[0097] A constraint building module, used to establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode;

[0098] The boundary point search module is used to search the boundary points of the DHS electric-thermal synergistic virtual energy storage flexibility based on the DHS electric-thermal synergistic virtual energy storage model and the adjustment constraints to obtain the quantitative results of the DHS electric-thermal synergistic virtual energy storage flexibility.

[0099] A third aspect of the present invention provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0100] A fourth aspect of the present invention is a computer-readable storage medium, on which a computer program is stored, and the program implements the steps of the method when executed by a processor.

[0101] Compared with the prior art, the beneficial effects of the present invention include at least:

[0102] The present invention constructs a DHS electric-thermal collaborative virtual energy storage model under a multi-space coupling scenario based on the flexibility index, which can accurately quantify the power regulation flexibility expressed by DHS to the power system; and establishes the regulation constraints of the CHP device in the DHS and the regulation constraints of the heating network in the DHS under the constant current-variable temperature mode, accurately considering various operating constraints inside the DHS; the electric-thermal collaborative virtual energy storage of the DHS is searched for the boundary points of the electric-thermal collaborative virtual energy storage flexibility, and the flexibility boundary of the DHS electric-thermal collaborative virtual energy storage is obtained by setting a measurement function and solving the boundary point search problem model, and the quantification result of the DHS electric-thermal collaborative virtual energy storage flexibility is obtained, which can quickly and accurately solve the power regulation flexibility expressed by DHS to the power system, and the DHS feeds back the quantification result to the power grid, and the power grid dispatcher can adjust the power of the CHP device within its constraints to ensure that the safe operation of the DHS is not affected. BRIEF DESCRIPTION OF THE DRAWINGS

[0103] Figure 1 Schematic diagram of the DHS electric-thermal synergistic virtual energy storage flexibility characterization method;

[0104] Figure 2 is a flowchart of the iterative approximation method;

[0105] Figure 3 For 32-node DHS test case;

[0106] Figure 4 A comparison between the method of the present invention and the Monte Carlo sampling method;

[0107] Figure 5 The flexibility size of DHS electric-thermal synergy virtual energy storage under different flexibility durations;

[0108] Figure 6 The flexibility size of DHS electric-thermal synergy virtual energy storage under different water supply temperatures;

[0109] Figure 7 This is a flow chart of the quantification calculation of electric-thermal coordinated virtual energy storage based on multi-space coupling in the present invention. DETAILED DESCRIPTION

[0110] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0111] Embodiment 1 of the present invention provides a method for calculating the quantization of electric-thermal coordinated virtual storage energy based on multi-space coupling, such as Figure 7 As shown, the following steps are included:

[0112] Step 1: Construct a DHS electric-thermal synergistic virtual energy storage model under multi-space coupling scenarios based on the flexibility index;

[0113] Further preferably, the present invention constructs a DHS electric-thermal synergistic virtual energy storage model in a multi-space coupling scenario: DHS flexibility is defined as the ability of DHS to inject or absorb electricity into EPS within a period of time.

[0114] Generally, DHS flexibility can be defined as the ability of DHS to maintain power over a period of time by injecting or absorbing power into EPS. Therefore, the flexibility of DHS is defined in the control variable space consisting of the power of the coupled devices.

[0115] For a single combined heat and power (CHP) unit, the flexibility can be expressed by the DHS electric-heat synergistic virtual energy storage model under the spatial coupling scenario:

[0116]

[0117] in, represents the unchanged power of CHP unit g during time period t; represents the actual output power of CHP device g during time period t;

[0118] During the period of providing flexibility of electric heat and virtual energy storage, Remain unchanged.

[0119] The flexibility index is expressed as:

[0120]

[0121] in, The adjustable power flexibility that the CHP device can express;

[0122] t 0 Provide DHS with flexibility in the timing of the start of the process;

[0123] t f Indicates the duration of the provision of flexibility based on market rules or EPS settings.

[0124] The control variables of DHS can be derived as:

[0125]

[0126] The flexibility provided by DHS electric-thermal collaborative virtual energy storage is a set of variables defined in the control variable space P CHP,flexThe feasible operating point in .

[0127] If the operating point is within the flexibility region, flexibility provision is feasible and the DHS temperature constraints are not violated during flexibility provision.

[0128] The electric-thermal synergistic virtual energy storage can be expressed as the feasible operation area of ​​DHS in P CHP,flex The projection on space is expressed as:

[0129]

[0130] Where, f is the operation model of DHS; φ FR is the feasible domain set projected onto the control variable space of DHS; P CHP ,flex is the control variable space of DHS, is the coupling space parameter; x is the state variable of DHS; X is the boundary constraint condition.

[0131] In specific implementation, first, the DHS operator can generate an operation plan to provide a baseline for the flexibility provision process; then, the DHS operator calculates the feasible domain and submits it to the EPS; then, based on the feasible domain and the flexibility requirements of the EPS, an adjustment signal will be sent to the DHS, and the DHS operator can adjust its adjustable flexible resources accordingly.

[0132] Step 2: Establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode;

[0133] Further preferably, after the concept of the feasible domain of the conceptual virtual energy storage is proposed in step 1, the present invention further establishes the DHS constraints to be considered in the calculation boundary, that is, the regulation model of DHS in the constant current-variable temperature mode: including the coupling device and the heating network DHS model, focusing on the DHS in the constant current-variable temperature (CF-VT) regulation mode, and the specific establishment process is:

[0134] In order to characterize the impact of DHS operation constraints on virtual energy storage flexibility, a detailed DHS model including coupling devices and heating network is established.This paper focuses on DHS in constant flow-variable temperature (CF-VT) regulation mode, which is widely used in China due to its stable hydraulic state.

[0135] 1) CHP unit modeling

[0136] A CHP plant generates both electricity and heat. The power output and heat output of a CHP plant can be modeled by a convex combination of the vertices of its power-heat operating region:

[0137]

[0138] in, is the heat output of CHP unit g in time period t; and is the power output and heat output of the CHP unit g at the vertex of the nth operating region in time period t, which can be determined according to the feasible domain of the electric and thermal output of the CHP unit; is the number of vertices in the running area; α g,η is an auxiliary variable describing the CHP operating point; Represents a collection of CHP units.

[0139] The flexibility of CHP is affected by its ramp rate limit. Therefore, when quantifying the reserve power capacity of CHP units, it is necessary to consider the ramp constraint, which is expressed as:

[0140]

[0141] in, and are the ascending and descending ramp limits of the CHP unit g respectively.

[0142] 2) Modeling of heating network

[0143] Under CF-VT regulation, the hydraulic state is stable, so the hydraulic variables can be simplified to constants. Assuming that the radial temperature distribution and heat conduction along the pipeline can be ignored, a thermodynamic model is established to describe the temperature fluctuation in the pipeline:

[0144]

[0145] in, is the supply and return pipe set; ρ is the fluid density; A l , A z is the internal cross-sectional area of ​​the supply pipe l and the return pipe z; is the average water supply temperature of water supply pipe l in time period t; is the average return water temperature of return pipe z in time period t; and are the average supply water temperature and return water temperature of the water supply pipe l and the return water pipe z in the time period t-1 respectively; Δt is the time step of flexible scheduling; and are the mass flow rates of the water supply pipe l and the return pipe z in time period t respectively; and are the inlet and outlet temperatures of the water supply pipe l in time period t; L l is the length of the water supply pipe l; z is the length of the return pipe z; T t a is the ambient temperature during time period t; and are the inlet and outlet temperatures of the return pipe z in time period t; c p is the specific heat capacity of water; R l is the thermal resistance of the water supply pipe l; R z is the thermal resistance of the return pipe z.

[0146] Assuming that the water flowing into the node from different pipes is well mixed, energy conservation can be described by the following temperature mixing constraint:

[0147]

[0148] in, is the set of pipes that flow out of node n, that is, the set of pipes through which the working fluid flows out of node n; is the set of pipelines flowing into node n, that is, the set of pipelines from which the working fluid flows into node n; n is the nth pipeline mixing node; is a collection of pipeline mixing nodes; is the mass flow rate of the water supply pipe l in time period t; is the mass flow rate of the return pipe z in time period t; and are the inlet and outlet water temperatures of the water supply pipe l in time period t, respectively; and are the inlet and outlet temperatures of the return pipe z in time period t.

[0149] The energy balance constraints of the heat source node are as follows:

[0150]

[0151] in, is the set of all CHP devices connected to node n; is the set of all GB units connected to node n; is the set of heat source nodes; Assemble the water supply and return pipes;

[0152] is the inlet water temperature of the water supply pipe l in time period t; is the outlet water temperature of return pipe z in time period t; represents the heat output of CHP unit g and GB unit w in time period t; c p is the specific heat capacity of water; is the mass flow rate of the water supply pipe l in time period t; is the mass flow rate of the return pipe z in time period t; in NSOURCE, GB and CHP are both heat source points.

[0153] The energy balance constraints of the heat exchange station node are as follows:

[0154]

[0155] in, is the heat load of node n in time period t; c p is the specific heat capacity of water; is the mass flow rate in pipeline l during time period t; is the inlet water temperature of the water supply pipe l in time period t; is the outlet water temperature of return pipe l in time period t; is the set of nodes with heat loads; is the set of pipes that flow out of node n.

[0156] In summary, the detailed model of DHS has been established.

[0157] In order to construct the feasible region of virtual energy storage flexibility in formula (4), the composition of the state variable x is defined as shown in formula (15).

[0158]

[0159] in, represents the heat output of CHP unit g in time period t; represents the actual output power of CHP device g in time period t; represents the heat output of GB unit w in time period t; and are the inlet and outlet water temperatures of the water supply pipe l of the heating network in the DHS in time period t, respectively; and are the inlet and outlet temperatures of the return pipe z of the heating network in DHS during time period t.

[0160] The DHS model consists of constraints (1)-(3) and (5)-(14).

[0161] The above variables all have upper and lower limit constraints, expressed as constraint X in formula (4), which are the technical and safety constraints required for DHS operation.

[0162] Step 3: Based on the model established in step 1 and the constraints established in step 2, the flexibility boundary points of the DHS electric-thermal collaborative virtual energy storage are searched to obtain the quantitative results of the DHS electric-thermal collaborative virtual energy storage flexibility.

[0163] Further preferably, the present invention proposes a DHS electric-thermal synergistic virtual energy storage boundary point search method based on optimization, which is solved by an iterative approximation method, specifically including:

[0164] 1) Formulation of the boundary point search problem

[0165] The flexibility of DHS electric-heat synergistic virtual energy storage is reflected in the impact of operating constraints on the flexibility boundary. In order to obtain the boundary points of the feasible domain, an optimization-based method is proposed. The compact form of the DHS model and the electric-heat synergistic virtual energy storage flexibility model in formula (4) are defined, and the boundary point search problem model is expressed as:

[0166]

[0167] The above model also uses the model established in step 1 and the constraints established in step 2 (i.e., formulas (1)-(15)) as constraints, where formulas (3)-(4) define the decision variables and feasible domain constraints of formula (16);

[0168] The measurement function σ() aims to maximize the flexibility provided by multiple CHPs. By properly setting the measurement function and solving equation (16), the characteristics of the feasible domain can be obtained:

[0169] ①σ 1 (P CHP,flex )=(-1) θ e T P CHP,flex , where e is the standard unit vector, and this measurement function can obtain the maximum flexibility of any CHP (referring to the flexibility index of CHP device g, respectively When θ is 0, the upward flexibility is calculated; when θ is 1, the downward flexibility is calculated.

[0170] ②σ 2 (P CHP,flex ) = d T P CHP,flex , where d is the direction vector. This measurement function can obtain the boundary points of the feasible domain under any CHP power output combination (the boundary points are the visual representation of the flexibility index in the CHP power space).

[0171] 2) Characterization method of DHS electric-thermal synergistic virtual energy storage flexibility

[0172] The DHS electric-thermal synergistic virtual energy storage is constructed by iteratively searching for boundary points. An iterative approximation method is used to solve a series of optimization problems. Figure 2 The search sequence of boundary points in two-dimensional space and the approximate process of quantifying the flexibility of DHS electric-thermal synergistic virtual energy storage are demonstrated. Figure 1 The horizontal and vertical axes in represent the flexibility indexes of CHP1 and CHP2, respectively.

[0173] The DHS electric-thermal synergistic virtual energy storage flexibility calculation method in the present invention can be divided into the following steps:

[0174] First, use σ 1 () Find the four starting boundary points (1 to 4) on the axis. For example, when θ = 0, e = (1,0) T The corresponding solution is point 1.

[0175] Secondly, using the objective function σ 2 () Generate a new search direction. Select the direction of the angle bisector between the adjacent boundary points and the origin (the initial operating point of the CHP unit) as the search direction. For example, point 1 and point 4 generate search direction 5, which is d = (1, -1) T The distance between the new boundary point and the connecting line of the two original adjacent boundary points is Δd, and the distance between the new boundary point and the origin is d. Continue to generate new search directions until all boundary points satisfy Δd / d<ε and the boundary fitting accuracy is achieved.

[0176] Finally, stop searching and construct the convex hull of the above boundary points to characterize the size of virtual energy storage flexibility. Figure 2 The above calculation process is given.

[0177] Example parameters such as Figure 3 As shown in Figure 1, a 32-node multi-space coupled DHS is used to verify the effectiveness of the proposed method. There are two CHPs (combined heat and power units) as coupling units in the system, and a GB (gas boiler) is deployed at the H32 node.

[0178] The heat output of GB is fixed, and the maximum and minimum water supply temperatures are set at 100℃ / 70℃.

[0179] The time step is 15 minutes, that is, re-quantization is performed every 15 minutes; the tolerance of the iterative approximation is set to 1%.

[0180] t f The time is set to 2 hours, and the DHS electric-thermal synergistic virtual energy storage flexibility boundary is calculated using the method proposed in the present invention and the Monte Carlo sampling method, as shown in Figure 4 As shown, DHS feeds it back to the grid, and the grid dispatcher can adjust the power of the CHP device within this constraint, thus ensuring that the safe operation of DHS is not affected.

[0181] The proposed method searches for 59 boundary points to meet the approximate accuracy requirements. A random sampling of 10,000 points was performed, of which a total of 4,050 points fell within the virtual energy storage flexibility boundary constraints. Compared with the Monte Carlo method, the method proposed in the present invention can accurately characterize the size of the electric-thermal synergistic virtual energy storage flexibility, while the Monte Carlo method takes 5896.40 seconds, which is very time-consuming. The method proposed in the present invention only takes 3.59 seconds, which is significantly better than the traditional sampling method.

[0182] like Figure 5 As shown, the present invention analyzes t f The impact of DHS electric-thermal synergy virtual energy storage flexibility is calculated and compared with different t f The flexibility range of electric-thermal synergy virtual energy storage under the value of t f As t increases, the impact of power flexibility on DHS thermal conditions becomes more significant, and the flexibility range of electric-thermal synergy virtual energy storage continues to narrow. f When P increases from 15 minutes to 4 hours, CHP2 From 5.33MW to 3.47MW, while P CH1P From 4.65MW to 2.95MW.

[0183] like Figure 6 As shown, the present invention analyzes the influence of water supply temperature on the flexibility of DHS electric-thermal synergistic virtual energy storage. A higher water supply temperature has more downward flexibility, while a lower water supply temperature has more upward flexibility. CHP1 +P CHP2 ) calculates the total power injected into the power system. It is observed in the feasible region at 80℃ that (P CHP1 +P CHP2 ) with an upward flexibility of 9.63MW; while in the feasible region at 90℃, (P CHP1 +P CHP2 ), with a downward flexibility of -8.23MW.

[0184] Embodiment 2 of the present invention provides a coupled space-based electric-thermal collaborative virtual energy storage flexibility quantitative calculation system, including:

[0185] Model building module, used to build a DHS electric-thermal synergistic virtual energy storage model in multi-space coupling scenarios based on flexibility index;

[0186] A constraint building module, used to establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode;

[0187] The boundary point search module is used to search the flexibility boundary points of the DHS electric-thermal collaborative virtual energy storage based on the established model and established constraints, and obtain the quantitative results of the flexibility of the DHS electric-thermal collaborative virtual energy storage.

[0188] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0189] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when executed by a processor.

[0190] Compared with the prior art, the beneficial effects of the present invention include at least:

[0191] The present invention constructs a DHS electric-thermal collaborative virtual energy storage model under a multi-space coupling scenario based on the flexibility index, which can accurately quantify the power regulation flexibility expressed by DHS to the power system; and establishes the regulation constraints of the CHP device in the DHS and the regulation constraints of the heating network in the DHS under the constant current-variable temperature mode, accurately considering various operating constraints inside the DHS; the electric-thermal collaborative virtual energy storage of the DHS is searched for the boundary points of the electric-thermal collaborative virtual energy storage flexibility, and the flexibility boundary of the DHS electric-thermal collaborative virtual energy storage is obtained by setting a measurement function and solving the boundary point search problem model, and the quantification result of the DHS electric-thermal collaborative virtual energy storage flexibility is obtained, which can quickly and accurately solve the power regulation flexibility expressed by DHS to the power system, and the DHS feeds back the quantification result to the power grid, and the power grid dispatcher can adjust the power of the CHP device within its constraints to ensure that the safe operation of the DHS is not affected.

[0192] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0193] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0194] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0195] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0196] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A quantitative calculation method for the flexibility of electric-thermal synergistic virtual energy storage based on coupling space, characterized in that: include: Based on the flexibility index, a DHS electric-thermal synergistic virtual energy storage model was constructed under multi-space coupling scenarios; Establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode; Based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints, a search is performed on the DHS electric-thermal synergistic virtual energy storage for the flexibility boundary points of the electric-thermal synergistic virtual energy storage to obtain a quantitative result of the DHS electric-thermal synergistic virtual energy storage flexibility.

2. According to claim 1, a method for quantitatively calculating the flexibility of electric-thermal synergistic virtual energy storage based on coupling space, characterized in that: The DHS electric-thermal synergistic virtual energy storage model in the multi-space coupling scenario based on the flexibility index is as follows: Among them, φ FR is the feasible domain set projected onto the control variable space of DHS; P CHP,flex is the control variable space of DHS; x is the state variable of DHS; X is the boundary constraint; f is the operation model of DHS; is the flexibility index of CHP unit g in DHS.

3. The method for quantitatively calculating the flexibility of electric-thermal synergistic virtual energy storage based on coupling space according to claim 2 is characterized in that: The flexibility index of the CHP unit g is: in, The adjustable power flexibility that the CHP device can express; represents the actual output power of CHP device g during time period t; represents the unchanged power of CHP unit g during time period t; t0 is the starting time when DHS starts to provide flexibility; t f Indicates that flexibility provides duration.

4. According to claim 2, a method for quantitatively calculating the flexibility of electric-thermal synergistic virtual energy storage based on coupling space is characterized in that: The state variables of the DHS are: in, represents the heat output of CHP unit g in time period t; represents the actual output power of CHP device g in time period t; represents the heat output of GB unit w in time period t; and are the inlet and outlet water temperatures of the water supply pipe l of the heating network in the DHS in time period t, respectively; and are the inlet and outlet temperatures of the return pipe z of the heating network in DHS during time period t.

5. According to claim 1, a method for quantitative calculation of electric-thermal synergistic virtual energy storage flexibility based on coupled space, characterized in that: The regulation constraints of the CHP unit in the DHS include: 1) Power output and heat output constraints of CHP devices: in, represents the actual output power of CHP device g in time period t; represents the heat output of CHP unit g in time period t; and represents the power output and heat output of the CHP unit g at the vertex of the nth operating region in time period t; α g,η represents the auxiliary variable describing the CHP unit g at the vertex of the nth operating region; Indicates the number of vertices in the running area; represents a collection of CHP units; 2) Climbing constraints of CHP devices: in, and are the ascending and descending ramp limits of the CHP unit g, respectively; represents the actual output power of CHP device g in time period t, t-1; Represents a collection of CHP units.

6. The method for quantitative calculation of electric-thermal synergistic virtual energy storage flexibility based on coupling space according to claim 1 is characterized by: The regulation constraints of the heating network in the DHS include: 1) Temperature fluctuations in the pipeline: in, is the supply and return pipe set; ρ is the fluid density; A l , A z is the internal cross-sectional area of ​​the water supply pipe l and the return pipe z; is the average water supply temperature of water supply pipe l in time period t; is the average return water temperature of return pipe z in time period t; is the average water supply temperature of water supply pipe l in time period t-1; is the average return water temperature of return pipe z in time period t-1; Δt is the time step of flexible scheduling; and are the mass flow rates of the water supply pipe l and the return pipe z in time period t respectively; and are the inlet and outlet water temperatures of the water supply pipe l in time period t, respectively; L l is the length of the water supply pipe l; z is the length of the return pipe z; T t a is the ambient temperature during time period t; c p is the specific heat capacity of water; and are the inlet and outlet temperatures of the return pipe z in time period t; R l is the thermal resistance of the water supply pipe l; R z is the thermal resistance of the return pipe z; 2) Temperature mixing constraint description: in, is the set of pipes through which the working fluid flows out from node n; is the set of pipelines from which the working fluid flows into node n; is a collection of pipeline mixing nodes; is the mass flow rate of the water supply pipe l in time period t; is the mass flow rate of the return pipe z in time period t; and are the inlet and outlet water temperatures of the water supply pipe l in time period t, respectively; and are the inlet and outlet temperatures of the return pipe z in time period t; 3) The energy balance constraints of the heat source node are as follows: in, is the set of all CHP devices connected to node n; is the set of all GB units connected to node n; is the set of heat source nodes; is the inlet water temperature of the water supply pipe l in time period t; is the outlet water temperature of return pipe z in time period t; represents the heat output of CHP unit g and GB unit w in time period t; c p is the specific heat capacity of water; is the collection of water supply and return pipes; is the mass flow rate of the water supply pipe l in time period t; is the mass flow rate of the return pipe z in time period t; 4) The energy balance constraints of the heat exchange station node are as follows: in, is the heat load of node n in time period t; c p is the specific heat capacity of water; is the mass flow rate in pipeline l during time period t; is the inlet water temperature of the water supply pipe l in time period t; is the outlet water temperature of return pipe l in time period t; is the set of nodes with heat loads.

7. The method for quantitatively calculating the flexibility of electric-thermal synergistic virtual energy storage based on coupling space according to claim 2 is characterized by: Based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints, the DHS electric-thermal synergistic virtual energy storage is searched for the electric-thermal synergistic virtual energy storage flexibility boundary points to obtain the DHS electric-thermal synergistic virtual energy storage flexibility quantitative results, including: The following electric-thermal synergistic virtual energy storage flexibility boundary point search problem model is established based on the DHS electric-thermal synergistic virtual energy storage model and the regulation constraints as constraints: In the formula, σ(P CHP,flex ) is the measurement function; By setting the measurement function and solving the problem model, the flexibility boundary of the DHS electric-thermal synergistic virtual energy storage is obtained as the quantification result of the DHS electric-thermal synergistic virtual energy storage flexibility.

8. The method for quantitatively calculating the flexibility of electric-thermal synergistic virtual energy storage based on coupling space according to claim 7 is characterized in that: The process of obtaining the DHS electric-thermal synergistic virtual energy storage flexibility boundary by setting the measurement function and solving the problem model is: 1) The measurement function σ(P CHP,flex ) is set to σ1(P CHP,flex )=(-1) θ e T P CHP,flex , by solving the problem model, the maximum upward and downward adjustment flexibility of any CHP in the DHS is obtained as the starting boundary point; Where e is the standard unit vector. When θ is 0, the maximum upward flexibility of any CHP is obtained; when θ is 1, the maximum downward flexibility of any CHP is obtained. 2) The measurement function σ(P CHP,flex ) is set to σ2(P CHP,flex ) = d T P CHP,flex , where d is the search direction vector, and the direction of the angle bisector between the adjacent boundary points and the initial operating point of the CHP unit is taken as the search direction. By solving the problem model, the boundary points of the feasible region under any CHP power output combination in the DHS are obtained as new boundary points, and the distance Δd between the new boundary point and the connecting line of the original adjacent boundary point and the distance d between the new boundary point and the initial operating point of the CHP unit are obtained; 3) If all boundary points satisfy Δd / d<ε and reach the fitting accuracy of the boundary, stop searching and construct the convex hull of all boundary points to characterize the DHS electric-thermal synergistic virtual energy storage flexibility boundary, otherwise return to 2).

9. A coupled space-based electric-thermal synergistic virtual energy storage flexibility quantitative calculation system, using the method described in any one of claims 1 to 8, characterized in that: The system comprises: Model building module, used to build a DHS electric-thermal synergistic virtual energy storage model in multi-space coupling scenarios based on flexibility index; A constraint building module, used to establish the regulation constraints of the CHP unit in the DHS and the regulation constraints of the heating network in the DHS under the constant flow-variable temperature mode; The boundary point search module is used to search the boundary points of the DHS electric-thermal synergistic virtual energy storage flexibility based on the DHS electric-thermal synergistic virtual energy storage model and the adjustment constraints to obtain the quantitative results of the DHS electric-thermal synergistic virtual energy storage flexibility.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1-8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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