A method for joint recovery of electric and thermal loads of an integrated energy system based on double-target optimization

Abstract

The application discloses a kind of based on double-target optimization's comprehensive energy system electric-thermal load joint recovery method, it is related to the field of comprehensive energy system operation;Including the following steps: S1, establishes comprehensive energy system electric load recovery model;S2, establishes comprehensive energy system thermal load recovery model;S3, based on double-target optimization method establishes comprehensive energy system electric-thermal load joint recovery model;S4, NormalizedNormalConstraint method is used to solve model and obtains Pareto frontier;S5, electric-thermal load recovery scheme is obtained using TOPSIS method;The Pareto frontier of electric, thermal load recovery scheme is obtained, and the final scheme is selected by TOPSIS method, and the coupling complementary action between electric and thermal energy flow can be comprehensively obtained by the method, the conflict and contradiction between electric load recovery and thermal load recovery of comprehensive energy system are effectively solved, the efficient collaborative recovery of electric-thermal load is realized, and the ability of comprehensive energy system to deal with extreme events is improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system operation, specifically a method for the joint restoration of electrical and thermal loads in integrated energy systems based on dual-objective optimization. Background Technology

[0002] Integrated energy systems combine multiple energy subsystems such as gas, electricity, heating, and transportation. Through the synergy between these subsystems, they can achieve cascaded utilization of primary energy, improve the utilization rate of renewable energy, and coordinate and complement multiple energy loads, thereby achieving the energy system's energy conservation and emission reduction goals. In engineering applications, integrated energy systems can fully utilize the thermal inertia characteristics of heating networks and buildings, improve the operational flexibility of the power system, promote the absorption of wind power, and reduce energy costs, thus attracting increasing attention.

[0003] The ability to cope with extreme events such as typhoons and cyberattacks (i.e., resilience or elasticity) is an important indicator for measuring the performance of energy systems. In integrated energy systems, the coupling relationships between various energy flows are very complex, making system elasticity extremely complicated. On the one hand, due to the complementary effects between different energy flows, the mutual support between multiple subsystems can reduce the risk of subsystem failure. On the other hand, in some cases, cascading failures may occur between coupled systems. Therefore, improving the operational elasticity of integrated energy systems under extreme events, especially the load recovery capability after a failure, is an urgent problem to be solved in engineering applications, given the complex energy flow coupling relationships. However, existing technologies often consider the load recovery problem of power systems or heating systems in isolation, failing to consider the coupling relationships and mutual influences between electrical and thermal energy flows. They cannot utilize the complementary effects between electrical and thermal energy flows to improve system elasticity, nor can they resolve the contradictions and conflicts that exist in the process of electrical and thermal load recovery, resulting in inefficient or even ineffective load recovery schemes. Summary of the Invention

[0004] To address the shortcomings mentioned in the background art, the present invention aims to provide a method for joint restoration of electric and thermal loads in integrated energy systems based on dual-objective optimization. The present invention obtains the Pareto front of the electric and thermal load restoration schemes, selects the final scheme through the TOPSIS method, and comprehensively addresses the coupling and complementary effects between electric and thermal energy flows through the proposed method. This effectively resolves the conflict and contradiction between electric load restoration and thermal load restoration in integrated energy systems, achieves efficient and coordinated restoration of electric and thermal loads, and improves the ability of integrated energy systems to cope with extreme events.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] A method for joint restoration of electrical and thermal loads in an integrated energy system based on dual-objective optimization includes the following steps:

[0007] S1. Establish a comprehensive energy system power load recovery model;

[0008] S2. Establish a comprehensive energy system heat load recovery model;

[0009] S3. Establish a joint recovery model for the electric and thermal loads of a comprehensive energy system based on a dual-objective optimization method;

[0010] S4. The Pareto front is obtained by solving the model using the Normalized Normal Constraint method.

[0011] S5. Obtain an electrothermal load recovery scheme using the TOPSIS method.

[0012] Furthermore, step S1, establishing the integrated energy system electrical load recovery model, further includes:

[0013] S11. Establish the objective function for the power load recovery problem of the integrated energy system. The objective function for the power load recovery problem is:

[0014] min f e =ω e1 f e1 +ω e2 f e2 +ω e3 f e3

[0015]

[0016]

[0017]

[0018] Where t is the scheduling period; Δt is the scheduling period interval; Τ is the set of scheduling periods; E b E is a set of grid node indices. br f is a set of power grid line indexes; e The objective function for electrical load restoration is f; e1 f e2 f e3 These are respectively: off-load cost, fuel cost and electricity purchase cost, and grid energy loss penalty cost; ω e1 ω e2 ω e3 These are the weighting coefficients for off-load costs, fuel costs and electricity purchase costs, and grid energy loss penalty costs, respectively. Let be the three-phase electrical load variable restored at node i during time period t; c is the priority coefficient for the electrical load at node i; gas For gas cost; ηgt For gas turbine power generation efficiency; Let c be the three-phase power generation variable of the gas turbine at node i during time period t; grid The purchase price of electricity from the power grid; For three-phase power purchases, r is the variable. j Let J be the resistance of line j; Let be the squared variable of the three-phase current of line j during time period t; 1 is a column vector in which all elements are 1.

[0019] S12. Establish constraints for the restoration of electrical load in the integrated energy system;

[0020] S13. Establish grid interconnection lines and equipment operation constraints for integrated energy systems;

[0021] S14. Establish constraints on the three-phase unbalanced distribution network of the integrated energy system.

[0022] Furthermore, step S13, establishing integrated energy system grid interconnections and equipment operation constraints, further includes:

[0023] S131. Establish tie-line power constraints;

[0024] S132. Establish reactive power constraints for compensation capacitors;

[0025] S133. Establish constraints on renewable energy output;

[0026] S134. Establish operating constraints for combined heat and power units;

[0027] S135. Establish operating constraints for electric boilers.

[0028] Furthermore, step S14, establishing constraints for the three-phase unbalanced distribution network of the integrated energy system, further includes:

[0029] S141. Establish voltage constraints;

[0030] S142. Establish line current constraints;

[0031] S143. Establish operating constraints for the voltage regulator;

[0032] S144. Establish constraints for the power flow equations of the line;

[0033] S145. Establish node power balance constraints.

[0034] Furthermore, step S2, establishing the integrated energy system heat load recovery model, further includes:

[0035] S21. Establish the objective function for the heat load recovery problem of the integrated energy system. The objective function for the heat load recovery problem is:

[0036]

[0037] Among them, f h The objective function for the heat load recovery problem is Φ. ln This is a set of indexes for the load nodes of the heating network. τ represents the indoor temperature variable of the building at node k during time period t; opt This represents the ideal indoor temperature value for a building.

[0038] S22. Establish constraints for the heat load recovery problem of the integrated energy system.

[0039] Furthermore, the constraints for establishing the integrated energy system heat load recovery problem in step S22 further include:

[0040] S221. Establish operating constraints for the heating network;

[0041] S222. Establish building heat load constraints.

[0042] Furthermore, the joint restoration model for the electrical and thermal loads of the integrated energy system established in step S3 based on the bi-objective optimization method has the following mathematical form:

[0043] min{f e ,f h}

[0044] Constraints of the ST load restoration problem

[0045] Constraints on the heat load recovery problem.

[0046] Furthermore, step S5, which uses the TOPSIS method to obtain an electrothermal load recovery scheme, further includes:

[0047] S51. For a point k, k = 1, 2, ..., K on the Pareto front, calculate its distance to the Utopia point and the Negative Ideal point, respectively. and

[0048]

[0049]

[0050] in, Let p be the coordinates of the k-th point on the Pareto front; utp The coordinates of the Utopia point; p nip The coordinates are the negative ideal point coordinates.

[0051] S52. For points k, k = 1, 2, ..., K on the Pareto front, calculate their similarity:

[0052]

[0053] S53. Sort the similarity of points k, k = 1, 2, ..., K on the Pareto front, and select the point with the highest similarity and its corresponding decision variable value as the final load recovery scheme.

[0054] The beneficial effects of this invention are as follows: This invention obtains the Pareto frontier of the electric and thermal load recovery schemes, selects the final scheme through the TOPSIS method, and can fully realize the coupling and complementary effect between electric and thermal energy flows through the proposed method. It effectively solves the conflict and contradiction between electric load recovery and thermal load recovery in the integrated energy system, realizes efficient and coordinated recovery of electric and thermal loads, and improves the ability of the integrated energy system to cope with extreme events. Attached Figure Description

[0055] The invention will now be further described with reference to the accompanying drawings.

[0056] Figure 1 This is a structural diagram of the integrated energy system of the present invention;

[0057] Figure 2 This is a flowchart of a method for joint restoration of electrothermal load in an integrated energy system based on dual-objective optimization, according to the present invention.

[0058] Figure 3 This is a structural diagram of the integrated energy system of Embodiment 2 of the present invention;

[0059] Figure 4 This is the Pareto front of the electrothermal synergistic recovery result in Embodiment 2 of the present invention;

[0060] Figure 5 This is the electrical load result of Embodiment 2 of the present invention;

[0061] Figure 6 This is the heating power result of the heating network in Embodiment 2 of the present invention. Detailed Implementation

[0062] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1

[0064] This embodiment is applied to an integrated energy system, the structure of which is as follows: Figure 1 As shown.

[0065] A method for joint restoration of electrical and thermal loads in an integrated energy system based on dual-objective optimization, such as... Figure 2 As shown, it includes the following steps:

[0066] S1. Establish a comprehensive energy system load recovery model:

[0067] S11. Establish the objective function for the power load recovery problem of the integrated energy system:

[0068] min f e =ω e1 f e1 +ω e2 f e2 +ω e3 f e3

[0069]

[0070]

[0071]

[0072] Where t is the scheduling period; Δt is the scheduling period interval; Τ is the set of scheduling periods; E b E is a set of grid node indices. br f is a set of power grid line indexes; e The objective function for electrical load restoration is f; e1 f e2 f e3 These are respectively: off-load cost, fuel cost and electricity purchase cost, and grid energy loss penalty cost; ω e1 ω e2 ω e3 These are the weighting coefficients for off-load costs, fuel costs and electricity purchase costs, and grid energy loss penalty costs, respectively. Let be the three-phase electrical load variable restored at node i during time period t; c is the priority coefficient for the electrical load at node i; gas For gas cost; η gt For gas turbine power generation efficiency; Let c be the three-phase power generation variable of the gas turbine at node i during time period t; grid The purchase price of electricity from the power grid; For three-phase power purchases, r is the variable. j Let J be the resistance of line j; Let be the squared variable of the three-phase current of line j during time period t; 1 is a column vector in which all elements are 1.

[0073] S12. Establish constraints for the restoration of electrical load in the integrated energy system:

[0074]

[0075] in, Let be the active power load constant at node i during time period t; Let i be the active power load constant at node i during time period t; These are the active and reactive load variables restored at node i during time period t, respectively. Let t be the 0-1 variable representing the electrical load recovery state at node i during time period t.

[0076] S13. Establish integrated energy system grid interconnection lines and equipment operation constraints:

[0077] S131. Establish tie-line power constraints:

[0078]

[0079] in, The active power capacity of the substation during time period t; The reactive power capacity of the substation during time period t; These are the three-phase active and reactive power variables of the tie line, respectively.

[0080] S132. Establish reactive power constraints for compensation capacitors:

[0081]

[0082] in, Let be the reactive power capacity of the compensation capacitor at node i during time period t; Let be the reactive power output variable of the compensation capacitor at node i during time period t.

[0083] S133. Establish renewable energy output constraints:

[0084]

[0085]

[0086] in, Let be the converter capacity at node i; Let be the active and reactive power output variables of renewable energy at node i during time period t; Let be the predicted active power of renewable energy at node i during time period t.

[0087] S134. Establish operating constraints for combined heat and power units:

[0088]

[0089] in, Let i be the upper and lower limits of the rated power generation capacity of the gas turbine at node i; Let i be the upper and lower limits of the reactive power output of the gas turbine at node i; Let be the three-phase active power and reactive power output variables of the gas turbine at node i during time period t; Let i be the gas turbine ramp rate at node i; Let be the heat loss rate of the gas turbine at node i; Let i be the efficiency of the waste heat recovery boiler at node i; Let be the output thermal power variable of the cogeneration unit at node i during time period t.

[0090] S135. Establish operating constraints for electric boilers:

[0091]

[0092] in, Let i be the upper and lower limits of the power output of the electric boiler at node i; Let i be the electric power variable of the electric boiler at node i in time period t; Let i be the efficiency of the electric boiler at node i; Let be the output thermal power variable of the electric boiler at node i during time period t.

[0093] S14. Establish constraints for the three-phase unbalanced distribution network of the integrated energy system:

[0094] S141. Establish voltage constraints:

[0095]

[0096] Among them, V max V min These are the upper and lower limits of the voltage. Let be the squared variable of the three-phase voltage at node i during time period t.

[0097] S142. Establish line current constraints:

[0098]

[0099] in, Let be the upper limit of the current in line j; For time period t, at point j on the line The squared variable of phase current.

[0100] S143. Establish voltage regulator operating constraints:

[0101]

[0102] in, These are the setting parameters for the voltage regulator; Let be the squared variables of the three-phase voltage at nodes i and j during time period t.

[0103] S144. Establish constraints for the power flow equations of the line:

[0104]

[0105]

[0106] in, Let J be the equivalent three-phase resistance and reactance of line j; Let be the equivalent impedance of line j; Let be the squared variable of the three-phase current of line j during time period t; For time period t, line j Phase active power and reactive power variables; For node i in time period t Phase voltage squared variable; For time period t, line j The squared variable of phase current. L i This is the set of line indices connected to node i.

[0107] S145. Establish node power balance constraints:

[0108]

[0109] in, Let be the three-phase active power and reactive power variables of line i during time period t; Let be the three-phase active and reactive load variables restored at node i during time period t; Let be the three-phase active power and reactive power variables of line j during time period t; Let i be the three-phase active and reactive power variables of the gas turbine at node i during time period t; Let be the three-phase active and reactive power variables of renewable energy at node i during time period t; Let be the three-phase active power variable of the electric boiler at node i during time period t; Let r be the three-phase reactive power variable of the capacitor compensator at node i during time period t; i x i Let t be the resistance and reactance of line i during time period t; Let be the squared variable of the three-phase current of line i during time period t.

[0110] S2. Establish a comprehensive energy system heat load recovery model:

[0111] S21. Establish the objective function for the heat load recovery problem of the integrated energy system:

[0112]

[0113] Among them, f hThe objective function for the heat load recovery problem is Φ. ln This is a set of indexes for the load nodes of the heating network. τ represents the indoor temperature variable of the building at node k during time period t; opt This represents the ideal indoor temperature value for a building.

[0114] S22. Establish constraints for the heat load recovery problem of a comprehensive energy system:

[0115] S221. Establish operating constraints for the heating network:

[0116] Establish heat source power balance constraints:

[0117]

[0118] in, Injecting thermal power variables into the heating network during time period t; E chp E eb This is a set of indexes for combined heat and power units and electric boilers; Let be the heat output power variable of cogeneration unit i during time period t; Let be the heat output power variable of electric boiler i during time period t.

[0119] Establish the power and temperature equations for the heat source and heat load nodes:

[0120]

[0121] in, Φ represents the set of pipe indices for the outflow / inflow node k; sn Φ ln These are the sets of indices for the source nodes and load nodes in the heating network, respectively; c w The specific heat capacity of water; m j For the mass flow rate of the heat medium in pipeline j; Let be the temperature variable of the heat medium at node k in the water supply and return network at time t; Let be the heat load power variable at node k during time period t.

[0122] Establish constraints on pipeline transmission delay and heat loss:

[0123]

[0124] Where, Φ p This is a set of indexes for heating network pipelines; β is the coefficient related to the transmission delay of pipe j; j Let be the thermal insulation coefficient of pipe j; Let t be the ambient temperature of the pipeline. Let t be the temperature variables of the heat transfer medium at the inlet and outlet of the water supply pipe j at time t; Let be the temperature variables of the heat transfer medium at the inlet and outlet of the return water pipe j at time t.

[0125] Establish power balance constraints for heating network nodes:

[0126]

[0127] Where, Φ in It is the set of converging nodes in the heating network; Let t be the temperature of the heat transfer medium at node k in the water supply and return network during time period t.

[0128] Establish water temperature mixing constraints at heating network nodes:

[0129]

[0130] Where, Φ in It is the set of converging nodes in the heating network; Let t be the temperature of the heat transfer medium at node k in the water supply and return network during time period t.

[0131] Establish upper and lower limit constraints for the supply and return water temperatures of the heating network:

[0132]

[0133] in, t s The upper and lower limits of the water supply temperature for the heating network; t r These are the upper and lower limits of the return water temperature of the heating network.

[0134] S222. Establish building heat load constraints:

[0135]

[0136]

[0137] in, The building parameters at load node k of the heating network; The indoor temperature variable of the building at the heating network load node k during time period t; Let be the building heat load and heating power variable at the heat network load node k during time period t; The outdoor temperature of the building is N; the scheduling cycle length is N. τ in These are the upper and lower limits for building room temperature.

[0138] S3. A joint recovery model for the electrical and thermal loads of a comprehensive energy system is established based on a dual-objective optimization method. Its mathematical form is as follows:

[0139] min{f e ,fh}

[0140] Constraints of the ST load restoration problem

[0141] Constraints of heat load recovery problem

[0142] S4. The Pareto front is obtained by solving the model using the Normalized Normal Constraint method.

[0143] S5. Obtain an electrothermal load recovery scheme using the TOPSIS method:

[0144] S51. For a point k, k = 1, 2, ..., K on the Pareto front, calculate its distance to the Utopia point and the Negative Ideal point, respectively. and

[0145]

[0146]

[0147] in, Let p be the coordinates of the k-th point on the Pareto front; utp The coordinates of the Utopia point; p nip The coordinates are the negative ideal point coordinates.

[0148] S52. For points k, k = 1, 2, ..., K on the Pareto front, calculate their similarity:

[0149]

[0150] S53. Sort the similarity of points k, k = 1, 2, ..., K on the Pareto front, and select the point with the highest similarity and its corresponding decision variable value as the final load recovery scheme.

[0151] Example 2

[0152] The multi-energy flow system in this embodiment consists of a 33-node three-phase unbalanced power distribution system and a 51-node heating system, such as Figure 3 As shown, the system includes one 4MW gas turbine, one 5MW electric boiler, one 300kW photovoltaic (PV1), one 500kW photovoltaic (PV2), and one 500kW wind turbine (Wind Turbine 1). The optimization cycle is 3 hours, the scheduling interval is 5 minutes, the upper and lower limits of the building's indoor temperature are set to 26℃ and 18℃ respectively, the ideal indoor temperature is set to 22℃, and the number of Pareto front points in the Normalized Normal Constraint method is set to 200.

[0153] According to the steps of the present invention, the combined recovery of electrothermal load is performed, and the Pareto front obtained by solving is as follows: Figure 4 As shown, the final recovery scheme selected using the TOPSIS method is as follows: Figure 4 As shown, Figure 4 The results of electrical load recovery for different solutions are as follows: Figure 5 As shown, the heating power of the heating network is as follows: Figure 6 As shown, the recovery processes of electrical and thermal loads differ significantly under different schemes. The proposed method can effectively coordinate the recovery processes of electrical and thermal loads, providing a relatively balanced solution. On the one hand, it can fully utilize the complementary effect between electrical and thermal energy flows; on the other hand, it can keep both the grid recovery time and the user's thermal comfort within a reasonable range.

[0154] Therefore, this method can comprehensively consider the constraints of three-phase unbalanced power grid, heating network and building heat load of integrated energy system, accurately characterize the user thermal comfort during the power load recovery process, heat load recovery process and heat load recovery process, and resolve the contradiction between power load recovery and heat load recovery of integrated energy system, realize the coordinated recovery of power and heat load, and improve the ability of integrated energy system to cope with extreme events.

[0155] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0156] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A method for joint recovery of electrical and thermal loads of an integrated energy system based on double-objective optimization, characterized in that, Includes the following steps: S1. Establish a comprehensive energy system power load recovery model; S2. Establish a comprehensive energy system heat load recovery model; S3. Establish a joint recovery model for the electric and thermal loads of a comprehensive energy system based on a dual-objective optimization method; S4. The Pareto front is obtained by solving the model using the Normalized Normal Constraint method. S5. Obtain an electric heating load recovery scheme using the TOPSIS method; Step S1, establishing the integrated energy system load recovery model, further includes: S11. Establish the objective function for the power load recovery problem of the integrated energy system. The objective function for the power load recovery problem is: Where t is the scheduling period; Δt is the scheduling period interval; and Τ is the set of scheduling periods; A set of indexes for power grid nodes; A set of indexes for power grid lines; The objective function for electrical load recovery is... , , These are respectively: off-load cost, fuel cost and electricity purchase cost, and grid energy loss penalty cost; , , These are the weighting coefficients for off-load costs, fuel costs and electricity purchase costs, and grid energy loss penalty costs, respectively. Let be the three-phase electrical load variable restored at node i during time period t; Let i be the electrical load priority coefficient at node i; For gas costs; For gas turbine power generation efficiency; Let be the variable of three-phase power generation of the gas turbine at node i during time period t; The purchase price of electricity from the power grid; For three-phase power purchase variables; Let J be the resistance of line j; Let be the squared variable of the three-phase current of line j during time period t; 1 is a column vector with all elements being 1; S12. Establish constraints for the restoration of electrical load in the integrated energy system; S13. Establish grid interconnection lines and equipment operation constraints for integrated energy systems; S14. Establish constraints on the three-phase unbalanced distribution network of the integrated energy system; Step S13, establishing integrated energy system grid interconnections and equipment operation constraints, further includes: S131. Establish tie-line power constraints; S132. Establish reactive power constraints for compensation capacitors; S133. Establish constraints on renewable energy output; S134. Establish operating constraints for combined heat and power units; S135. Establish operating constraints for electric boilers; Step S14, establishing constraints for the three-phase unbalanced distribution network of the integrated energy system, further includes: S141. Establish voltage constraints; S142. Establish line current constraints; S143. Establish operating constraints for the voltage regulator; S144. Establish constraints for the power flow equations of the line; S145. Establish node power balance constraints.

2. The method of claim 1, wherein, Step S2, establishing the integrated energy system heat load recovery model, further includes: S21. Establish the objective function for the heat load recovery problem of the integrated energy system. The objective function for the heat load recovery problem is: wherein, is an objective function for the heat load recovery problem; is a set of heat network load node indices; is a building indoor temperature variable at node k at time period t; is an ideal value of the building indoor temperature; S22. Establish constraints for the heat load recovery problem of the integrated energy system.

3. The method of claim 2, wherein, The constraints for establishing the integrated energy system heat load recovery problem in step S22 further include: S221. Establish operating constraints for the heating network; S222. Establish building heat load constraints.

4. The method of claim 3, wherein, The joint recovery model for the electrothermal load of the integrated energy system established in step S3, based on the dual-objective optimization method, has the following mathematical form: Constraints of the ST load restoration problem Constraints on the heat load recovery problem.

5. The method of claim 1, wherein, Step S5, which uses the TOPSIS method to obtain an electrothermal load recovery scheme, further includes: S51, for the point k on the Pareto front, k = 1, 2, …, K, respectively calculate its distance with the utopia point and the negative ideal point and ; wherein, is the coordinate of the kth point on the Pareto frontier; is the coordinate of the Utopia point; is the negative ideal point coordinate; S52. For points k on the Pareto front, k = 1, 2, ..., K, calculate their similarity: S53. Sort the similarity of points k, k = 1, 2, ..., K on the Pareto front, and select the point with the highest similarity and its corresponding decision variable value as the final load recovery scheme.

Images