Regional integrated energy system station network collaborative planning method considering quantity-mass balance

Abstract

The invention discloses a regional integrated energy system station network collaborative planning method considering quantity-mass balance, and belongs to the technical field of integrated energy system planning. The method comprises the following steps: establishing an energy flow steady-state model of an electric power, natural gas and thermal subsystem; establishing a carbon flow model based on the energy flow model; establishing a flow calculation model based on an energy flow solving result; constructing an energy flow-carbon flow-flow unified steady-state model; a double-layer optimization framework based on energy-carbon-multiple objectives is established, the upper layer decides energy station equipment type selection and network capacity expansion, and the lower layer optimizes typical daily multi-stream distribution and feeds back; and introducing a quantity-mass balance planning criterion, and solving an equilibrium solution based on Pareto frontier solution set fitting and a Hessian discrimination method. According to the method, unified representation and collaborative optimization of energy'quantity 'and'quality' attributes are realized, and the economical efficiency, the low-carbon property and the energy quality of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system planning, and in particular to a method for coordinated planning of regional integrated energy system stations and networks that considers the balance between quantity and quality. Background Technology

[0002] Against the backdrop of "dual carbon" goals and the accelerated construction of new power systems, Regional Integrated Energy Systems (RIES) have become crucial for energy transition due to their multi-energy complementarity, high efficiency, and clean energy characteristics. However, traditional RIES planning methods have the following shortcomings:

[0003] Imbalance in energy properties: Traditional planning focuses only on the conservation of energy "quantity" and economy, ignoring the differences in energy "quality", and cannot accurately assess the actual energy consumption level of the system.

[0004] Solving multiple flows separately: Existing studies mostly model and optimize energy flow, carbon flow, and carbon flow separately, lacking unified coordination, making it difficult to achieve the overall system optimization.

[0005] Insufficient station-network coordination: Energy stations and energy networks are often planned separately or sequentially, lacking in-depth coordination, which limits overall performance.

[0006] Lack of balance between quantity and quality: Existing methods have failed to achieve a balance between economy, low carbon emissions and energy quality, and planning schemes often focus on a single objective.

[0007] Therefore, there is an urgent need for a planning method that can uniformly represent the "quantity" and "quality" of energy, achieve deep coordination between stations and networks, and achieve a balance among multiple objectives. Summary of the Invention

[0008] To address the existing problems, this invention provides a regional integrated energy system station-network collaborative planning method that considers quantity and quality balance. The specific scheme is as follows:

[0009] A method for coordinated planning of regional integrated energy system stations and networks considering quantity and quality balance, the method comprising the following steps:

[0010] S1. Establish a steady-state energy flow model for the electricity, natural gas and heat subsystems in a regional integrated energy system;

[0011] S2. Based on the energy flow steady-state model, establish a system carbon flow model;

[0012] S3. Based on the solution results of the energy flow steady-state model, establish a system energy flow calculation model;

[0013] S4. Construct a unified steady-state model of energy flow, carbon flow, and carbon dioxide flow in a regional integrated energy system, covering energy transmission, carbon emissions, and carbon dioxide flow distribution in the power, natural gas, and heat subsystems;

[0014] S5. Establish a two-layer optimization framework based on multiple objectives of "energy-carbon-energy": the upper layer takes quantity and quality balance as the objective and makes decisions on the selection of energy station equipment and the expansion plan of energy network; the lower layer optimizes the distribution of typical daily energy flow-carbon flow-energy flow under the constraints of the upper layer plan to achieve optimal operating economy and feeds back to the upper layer.

[0015] S6. Introduce the quantitative-quality balance planning criterion, and solve the "energy-carbon-energy" equilibrium solution based on Pareto front solution set fitting and Hessian discriminant method to determine the optimal planning scheme.

[0016] The present invention also discloses a computer-readable storage medium and a computer system, wherein the computer-readable storage medium stores a computer program, and the computer program, when executed, performs the method described in any of the preceding claims. A computer system includes a processor and a storage medium, the storage medium storing a computer program, and the processor reading from the storage medium and running the computer program to perform the method described in any of the preceding claims.

[0017] The beneficial effects of this invention are as follows:

[0018] By employing a unified energy flow-carbon flow-energy flow model, a unified representation and synergistic optimization of the "quantity" and "quality" attributes of energy are achieved. The two-layer optimization framework enables deep collaboration between energy stations and energy networks, improving overall system performance. The quantity-quality balance criterion achieves equilibrium among multiple objectives, demonstrating engineering applicability. Numerical examples show that the proposed solution reduces average annual operating costs by approximately 34.85%, carbon emissions by approximately one-fifth, energy efficiency by 0.11, and overall economic efficiency by approximately 17.82%. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 Flowchart for regional integrated energy system planning that considers quantity and quality balance;

[0021] Figure 2 Load parameters;

[0022] Figure 3 System topology diagram;

[0023] Figure 4 Electricity and natural gas prices;

[0024] Figure 5 Pareto front solution set for multi-objective optimization;

[0025] Figure 6 Typical daily operational results under the quantity-quality coordinated planning scheme;

[0026] Figure 7 EH2 natural gas input;

[0027] Figure 8 Heat supply for EH1 and EH2. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0029] like Figure 1-8 A method for coordinated planning of regional integrated energy system stations and networks considering quantity and quality balance, the method comprising the following steps:

[0030] S1. Establish a steady-state energy flow model for the electricity, natural gas and heat subsystems in the regional integrated energy system.

[0031] Specifically, establishing an energy flow steady-state model includes the following steps:

[0032] S11. Establish a steady-state model of energy flow in the power subsystem.

[0033] The integrated energy system includes various energy forms such as electricity, gas, cooling, and heating. The steady-state energy flow model of the power subsystem is mainly based on the three-phase unbalanced line model. With node voltage, current, active power, and reactive power as variables, an active and reactive power balance model of the node is established to obtain the power distribution within the region.

[0034] The power subsystem energy flow steady-state model includes nodal active power balance equations and reactive power balance equations:

[0035]

[0036] In the formula: and These are nodes Injected active and reactive power; , It is a node , The voltage amplitude; It is a node , The susceptance between them; It is a node , The electrical conductance between them; It is a node , The voltage phase angle difference between them.

[0037] S12. The natural gas subsystem establishes a steady-state energy flow model by analyzing gas pressure and flow rate, including a pipeline gas flow rate model, a node pressure and flow rate model, and a natural gas balance model.

[0038] The gas flow model states that the natural gas flow rate through the pipeline has a non-linear relationship with the squared difference in natural gas pressure at both ends of the pipeline, as shown in the following equation:

[0039]

[0040]

[0041] In the formula: For pipelines steady-state flow; It is a function symbol; and They are nodes and nodes Square of pressure; These are parameters related to the direction of natural gas flow. For pipelines The drag coefficient;

[0042] The nodal pressure-flow model—constructed using the flow continuity equation of the natural gas network—is as follows:

[0043]

[0044] In the formula: To remove the node-branch correlation matrix of a natural gas network containing compressor pipelines; This represents the flow vector of the natural gas pipeline. This represents the flow vector of natural gas outflow from each natural gas node;

[0045] The natural gas balance model states that, to ensure the reliability of long-distance energy transmission in the natural gas network, a corresponding compressor needs to be configured in the gas system to compensate for pressure losses caused by friction; the compressor outlet is designated as a node. The entrance is a node. Then, the natural gas balance model for a pipeline containing a compressor can be expressed as:

[0046]

[0047] In the formula: This represents the natural gas flow rate at the compressor inlet pipe. This refers to the flow rate of natural gas passing through the compressor. The amount of natural gas consumed by the compressor; This represents the natural gas flow rate of the outlet pipeline. This refers to the compression ratio of the compressor. and These are the pipe constants for the inlet and outlet pipes, respectively. The calorific value of natural gas; The temperature of the natural gas; It is a highly variable index;

[0048] S13. The heating and cooling system, or thermal subsystem, uses water and steam as media. Through hydraulic and thermal models, it characterizes the supply and return water temperatures and flow rates. The hydraulic model consists of a flow continuity equation and a loop head equation, as shown in the following equation:

[0049]

[0050]

[0051]

[0052] In the formula: This is a connection matrix for the branch nodes of the heating network. This represents the working fluid flow vector in the thermal pipeline. Inject a flow scalar vector into the thermal nodes; The circuit-branch correlation matrix of the heating network; This is the head loss vector for the thermal pipeline; The pipeline resistance matrix;

[0053] The thermal model consists of a heat equation, a temperature drop equation, and a mixing temperature equation, as shown below:

[0054]

[0055]

[0056]

[0057] In the formula: For the node thermal power vector, This is the specific heat capacity of water; and These are the node heating temperature vector and the node output temperature vector, respectively. and These are the end-point temperature vector and the beginning-point temperature vector of the pipeline, respectively. The ambient temperature; The heat transfer coefficient of the heat pipe; This refers to the length of the pipe. The flow of traffic into the node; This refers to the flow of traffic leaving the node; and These are the node inflow temperature and the node outflow temperature, respectively.

[0058] S14. Through energy conversion and distribution, energy hubs can achieve mutual complementarity and benefit from each other's energy resources, and improve their economy, flexibility, and reliability. Based on the energy hub model, a multi-energy conversion model for energy stations is established.

[0059] The multi-energy conversion model of the energy station is as follows:

[0060] In the formula: This is the output vector; This is the coupling matrix; The input vector.

[0061] S2. Based on the energy flow steady-state model, establish a system carbon flow model.

[0062] Specifically, the carbon flow model, based on the energy flow steady-state model, defines indices such as carbon flow density, carbon flow rate, and carbon potential. A nodal carbon potential balance model is established, and then the carbon flow rate is calculated iteratively. The carbon emission flow rate, or simply carbon flow rate, represents the amount of carbon emissions passing through a node or branch per unit time, expressed in tCO2 / h.

[0063]

[0064] In the formula: Carbon flow rate, For carbon emission flow, For time.

[0065] Carbon flux density refers to the amount of carbon dioxide emissions generated at the energy supply end for each unit of energy transmitted along a branch of an energy network, denoted by the symbol [symbol missing]. It is expressed in units of tCO2 / kWh. This concept can also be described as the ratio of the carbon flow rate of a branch in an integrated energy system's energy network to the energy power it transmits.

[0066]

[0067] In the formula: This refers to active power.

[0068] The carbon emissions on the energy production side resulting from a unit of energy consumption at a node are defined as the nodal carbon potential, denoted by the symbol [symbol missing]. It indicates that the unit is tCO2 / kWh.

[0069]

[0070] In the formula: For branch street number, It is the set of all branches in IES that can flow into the node.

[0071] S3. Based on the solution results of the energy flow steady-state model, establish a system energy flow calculation model.

[0072] Integrated energy system current calculation models fall into two categories: one is a direct current calculation method based on non-equilibrium node current, applicable to scenarios where the IES non-equilibrium node current is known; the other is an indirect current calculation method based on non-equilibrium node power, applicable to scenarios where the IES non-equilibrium node power is known. This invention, based on energy flow calculation results, establishes current equations for power lines, current balance and pressure-current equations for natural gas systems, and nodal current potential and thermal current for thermal systems.

[0073] Specifically, the flow calculation model in S3 includes:

[0074] S31. Current calculation of power subsystem:

[0075] In energy quality analysis, electrical energy can be entirely converted into work or other forms of energy, thus belonging to high-quality energy and can be entirely considered as non-active (V) energy. Therefore, active power flow can be considered as V current, and active power loss as V loss. The V current and V loss of a power branch can be expressed as:

[0076]

[0077] In the formula: This indicates taking the real part of a complex number; The line voltage phasor at the beginning of the power line; The conjugate of the line current phasor flowing through the line; and These are the line voltage phasors at both ends of the line.

[0078] S32. Flow calculation of natural gas subsystem:

[0079] Natural gas systems share similar characteristics with power systems, following Kirchhoff-like pressure laws. Loads generate heat energy through natural gas combustion. The calorific value can be used to analyze the energy characteristics of the pipeline network, and fuel sludge is used to quantitatively characterize the amount of sludge supplied to users. The distribution of fuel sludge consumed by the load within the pipeline network constitutes the sludge flow of the natural gas system, which is equivalent to the heat sludge released during combustion, providing a core basis for sludge flow analysis. This sludge flow can be calculated by multiplying the nodal sludge potential by the gas flow rate, where the nodal sludge potential is defined as the product of the natural gas energy quality coefficient and the calorific value. Under steady-state operating conditions, the change in this sludge flow is equivalent to pipeline sludge loss, and this sludge loss value is equal to the product of the difference in sludge potential between the nodes at both ends of the pipeline and the gas flow rate. Therefore, the sludge flow, sludge loss, gas source sludge, and gas load sludge in a natural gas system can all be expressed as:

[0080]

[0081] In the formula: For the nodal potential of the natural gas system, This is the theoretical combustion temperature of natural gas; This represents the calorific value of natural gas. Carbon flow calculations are performed for each system. , , , These are pipeline flow rate, flow loss, gas source flow rate, and gas load flow rate, respectively. , For the two ends of the natural gas pipeline; , , These are the pipeline airflow rate, the gas source airflow rate, and the gas load airflow rate, respectively.

[0082] S33, Calculation of flow in the thermal subsystem:

[0083] The potential difference between a node temperature and the ambient temperature in a thermal system is defined as the potential at temperature T. As shown in the following formula:

[0084]

[0085] When the node water temperature is T, the vortex flow near the node can be expressed as the product of the node vortex potential and the water mass flow rate. The vortex flows in the supply and return water pipes, supply and return water nodes, and outlet nodes of the thermal system can be expressed as follows:

[0086]

[0087] In the formula: , , , , These are respectively: water supply pipeline flow, return water pipeline flow, inflow load flow, outflow load flow, and inflow heat source flow; , , These are the water supply node conditions, return water node conditions, and outlet node conditions, respectively. , , These are the pipeline flow rate, the load flow rate, and the heat source flow rate, respectively.

[0088] The heat source and the load are represented as follows:

[0089]

[0090] In the formula: , and are the heat source and load , respectively.

[0091] The losses in various parts of a heating system include losses in the supply water pipes, losses in the return water pipes, and load losses, which are represented as follows:

[0092]

[0093] In the formula: , , These are losses in the water supply and return pipelines and load losses, respectively. , The potential of the nodes at both ends of the water supply pipeline; , This refers to the potential of the nodes at both ends of the return water pipe.

[0094] S4. Construct a unified steady-state model of energy flow, carbon flow, and carbon dioxide flow in a regional integrated energy system, covering energy transmission, carbon emissions, and carbon dioxide flow distribution in the power, natural gas, and heat subsystems.

[0095] Specifically, the RIES energy station is a crucial link in energy coupling and conversion, and its energy conversion model is based on the energy hub model, as shown in the following equation:

[0096]

[0097]

[0098] In the formula: For the output (load) vector, Input power vector to the upper-level energy grid; Extended input vectors for power output from wind turbines, solar PV, and other distributed energy facilities; This is the charging power vector; This is the power vector. This is the transformation matrix; , , These refer to the electricity, heat, and gas loads supplied to the energy station, respectively. For transformer conversion efficiency; and These are the electrical and thermal efficiencies of CHP, respectively. For EB heat transfer efficiency; HP heat transfer efficiency; GB heat transfer efficiency; and These are the power allocation coefficients for EB and HP, respectively. and These are the natural gas allocation coefficients for CHP and GB, respectively; For power input to the energy station, Natural gas is supplied to the energy station.

[0099] By calculating the nodal energy flow of each energy subsystem through energy flow conversion and distribution, and then incorporating carbon and galvanic flow calculations, since both carbon and galvanic flow calculations are based on the energy flow calculation results of non-equilibrium nodes, a unified power flow solution is established. This incorporates carbon and galvanic flow into the unified energy flow equations, treating the multi-energy energy flow-carbon flow-galvanic flow model as a unified whole, thus establishing a unified model. Figure 1 The middle part of the steps and the following formula:

[0100] In the formula: , These are the active power deviation and reactive power deviation of the power system, respectively. , , and These are the nodal thermal power deviation of the thermal system, the head loss deviation of the heating network loop, the heating temperature deviation, and the regeneration temperature deviation; For natural gas system node flow deviation; This refers to the carbon potential deviation at the node. For node deviation; , , , and The system is given active power, reactive power, thermal power, natural gas load, and nodal carbon potential. , It is a node , The voltage amplitude; It is a node , The susceptance between them; It is a node , The electrical conductance between them; It is a node , The voltage phase angle difference between them. This is the specific heat capacity of water; For the working fluid flow rate phase of the thermal pipeline; Inject flow scalars into thermal nodes; The reduced-order correlation matrix of the thermal network after removing the compressor branch; The circuit-branch correlation matrix of the heating network; and These are the node heating temperature vector and the node output temperature, respectively. The pipeline resistance matrix; , These are matrices related to the structure and flow rate of the heating network and the regenerating network, respectively. , These are column vectors related to the heating temperature and the output temperature, respectively. The reduced-order correlation matrix formed after removing compressor branches from the natural gas network; It is a function symbol; To remove the node-branch correlation matrix of a natural gas network containing compressor pipelines; The square of the natural gas node pressure. The node flux matrix; This is the branch power flow distribution matrix; Inject the distribution matrix into the source end; This is the source-end carbon emission factor vector; and They are nodes Flow to Node mass flow rate and nodes Flow to Node mass flow rate; and They are nodes and nodes The node's momentum; and They are nodes The source and load.

[0101] The core idea of ​​the unified solution method for the energy flow-carbon flow-thermal flow unified model is to treat the equations of the electric-gas-thermal network as a unified whole and simultaneously solve the state variables of these three types of networks using a unified Jacobian matrix iterative method. During the iteration process, carbon flow and thermal flow are calculated. When the power flow iteration solution converges, the carbon flow and thermal flow solutions are also completed. Based on this method, the unified energy-carbon-thermal-gas RIES iterative formula is as follows:

[0102]

[0103] In the formula: This is the adjustment amount for the state variable; denoted as the deviation of the electric-thermal-gas network; (k) represents the k-th iteration; For the energy flow, carbon flow, and flow state variables of the electric-thermal-gas network. To unify the iteration matrix, it can be specifically expressed as follows:

[0104]

[0105] In the formula: all elements are the derivatives of the power network equation, thermal network equation, natural gas network equation, carbon flow network equation, and sludge flow network equation with respect to the state variables of the power system, thermal system, natural gas system, carbon flow, and sludge flow, respectively.

[0106] S5. Establish a two-layer optimization framework based on multiple objectives of "energy-carbon-energy".

[0107] (1) Two-level optimization model

[0108] 1) Upper-level optimization objective function

[0109] The upper-level planning aims to achieve a balance between the quantity and quality of energy, carbon, and energy resources, determining the system planning scheme, namely the capacity configuration of energy station equipment and the expansion plan of the energy network. Therefore, the upper-level objective function includes three objectives: economic efficiency, etc. Carbon emissions Efficiency Economic cost of integrated energy system The following formula includes the investment cost of equipment and pipeline planning. Energy operating costs Maintenance costs of equipment and pipelines .

[0110]

[0111]

[0112]

[0113]

[0114]

[0115]

[0116] In the formula, This indicates the cost of planning energy network pipelines; This represents the planned cost of the energy station equipment; m and n represent the total number of energy pipelines and energy equipment, respectively. , Let $i$ represent the cost of the i-th energy pipeline and the construction cost, respectively. , Let these represent the cost and installation cost of the j-th energy device, respectively. and These represent the unit length (capacity) cost of energy pipelines and equipment, respectively. , These represent the model (capacity) of the i-th energy pipeline (j-th energy device); and These represent the length (capacity) of the i-th energy pipeline (the j-th energy device); , These represent the construction and installation costs of energy pipelines and equipment, respectively. , This indicates the model (capacity) of the i-th energy pipeline (j-th energy device) before planning; s indicates the typical day type (s=1, 2, 3 represent typical days in winter, summer, and transition season, respectively); The number of days in different seasons; the number of types of typical days in smax; , , and These represent the electricity load, electricity price, natural gas load, and natural gas price on a typical day, respectively. and These represent the unit maintenance costs for pipelines and equipment, respectively; ΔT represents the operating time of the energy pipelines or equipment.

[0117] Regarding carbon emissions, the carbon emissions are calculated by analyzing changes in electricity and natural gas inputs at the source side using carbon flow optimization results, as shown in the following formula:

[0118]

[0119] In the formula, Carbon emissions; and Carbon potential of electricity and gas at the source nodes of electricity and natural gas, respectively; and To supply electricity and natural gas to the power and natural gas systems.

[0120] The overall efficiency of the integrated energy system in the entire region is calculated based on the input efficiency and output efficiency, as shown in the following formula:

[0121]

[0122] In the formula, To improve the efficiency of integrated energy systems; , These are the input and output, respectively.

[0123] 2) Lower-level optimization objective function

[0124] The lower-level optimization is a single objective, aiming to maximize the equivalent operational economics of a typical day. This objective includes three aspects: energy operation and maintenance costs, carbon emission costs, and energy efficiency equivalent economic value. Based on the energy flow, carbon flow, and energy flow calculation results, the operational economics of a typical day are optimized.

[0125]

[0126] In the formula, Carbon tax payable per unit of carbon emissions; To improve the efficiency of the integrated energy system before planning; The equivalent economic value of efficiency savings per unit.

[0127] (2) Constraints

[0128] 1) Upper-level optimization constraints

[0129] The upper-level optimization constraints mainly consist of equipment and pipeline deployment constraints, as well as energy balance constraints. Equipment planning constraints primarily include equipment commissioning constraints and equipment capacity constraints, as shown in the following formula:

[0130]

[0131]

[0132]

[0133] In the formula, , These represent the upper and lower limits of the planned capacity of the j-th device in the energy station; It is a 0-1 variable used to indicate whether the j-th device is planned to be put into operation; Let J be the operating power of the j-th device in the energy station; , These are the minimum and maximum power required for the equipment to operate, respectively.

[0134] Pipeline planning constraints limit the planned pipeline model to no lower than the original model before planning, as shown in the following formula:

[0135]

[0136] 2) Lower-level optimization constraints

[0137] The lower-level optimization constraints are based on the energy balance of a typical day and are modeled using this balance. Energy flow constraints stipulate that the optimized energy flow must not exceed the pipeline's carrying capacity limit, as shown in the following equation:

[0138]

[0139] In the formula, , , These represent the electricity, natural gas, and heat flow of the i-th energy pipeline, respectively. , , They represent The maximum power / flow capacity of the energy pipeline model.

[0140] Carbon flow and carbon emission constraints define thresholds for regional carbon emissions and carbon emission efficiency, as shown in the following formula:

[0141]

[0142]

[0143] In the formula, This is the upper limit for carbon emissions; The energy station efficiency threshold; , For the input / output of the energy station.

[0144] S6. Introduce the quantitative-quality balance planning criterion, and solve the "energy-carbon-energy" equilibrium solution based on Pareto front solution set fitting and Hessian discriminant method to determine the optimal planning scheme.

[0145] Specifically, the upper layer employs the NSGA-II optimization algorithm for multi-objective optimization, while the lower layer solves for energy flow, carbon flow, and energy efficiency through Jacobi matrix iteration, and optimizes based on the Gurobi solver. The upper layer of the model performs multi-objective optimization of energy station equipment and energy network pipeline planning schemes. The optimized energy station equipment and energy pipelines serve as constraints for the lower layer of the planning model, influencing the optimization results of energy flow, carbon flow, and energy efficiency. The lower layer feeds back the optimized energy operation and maintenance costs, carbon emission costs, and energy efficiency to the upper layer for further optimization, ultimately forming a Pareto front solution set. To balance the dual attributes of energy and energy quality, a polynomial fitting solution set is used, and the partial derivative of the curve is calculated, as shown in the following equation:

[0146]

[0147] In the formula, It is the Pareto solution set.

[0148] Based on the Hessian discriminant method, the equilibrium solution of "energy-carbon-energy" is obtained, thereby obtaining a regional integrated energy system station-network coordinated planning scheme that considers the balance of quantity and quality.

[0149] The following analysis uses examples to illustrate scene generation; see the description below for details:

[0150] This example improves and studies the topology of the example, including loads of various business types such as industry, commerce, and residential. Typical daily load per unit values ​​are shown in Tables 1 and 2.

[0151] The RIES system comprises a 26-node power distribution system, a 9-node natural gas system, and a 22-node district heating system. The district heating system has supply and return water temperatures of 70°C and 40°C, respectively, and the medium-pressure natural gas system has a gas source pressure of 0.4 MPa. The heating system currently has a centralized heat source at node H07. It is proposed to connect the electrothermal coupling energy station EH2 at node E20 of the power distribution system to supply the H22 heating node, and to connect the energy station EH1 at node G07 of the natural gas system to supply the H01 heating node and node E12. Potential energy station equipment includes electric boilers (EB), gas boilers (GB), combined heat and power (CHP), heat pumps (HP), and photovoltaic (PV). Energy equipment parameters are shown in Table 2. Due to power backfeed limitations, the photovoltaic system within the energy station can only be consumed locally. This invention sets the current year as 2025 and conducts an 8-year medium-to-long-term energy system expansion plan, with a planning target of 2032. In 2025, the equivalent annual average loads for electricity, natural gas, and heat are projected to be 3.44MW, 8.60MW, and 3.10MW, respectively. The energy system in the example is already saturated, with loads in a period of slow and stable growth. Therefore, based on an equal annual growth rate, the estimated loads for electricity, gas, and heat by 2032 are projected to reach 6.73MW, 15.86MW, and 5.27MW, respectively, representing growth rates of 195.64%, 184.42%, and 170.00%. Electricity and natural gas prices are as follows... Figure 4 As shown in the table. Some parameters are shown in Tables 1 and 2.

[0152] surface Example parameters

[0153]

[0154] surface Energy equipment parameters

[0155]

[0156] against Figure 3 Using the RIES example, this paper analyzes its economic efficiency, carbon emissions, and cost efficiency. Based on the multi-objective programming model described in Section 4, the Pareto front solution set for multi-objective optimization is calculated as follows: Figure 5 As shown in the figure, the red circles represent the quantity-quality synergy planning schemes determined according to the quantity-quality synergy criterion. To compare their changing trends, the first and last two planning schemes, namely Scheme 1 (green circle) and Scheme 2 (yellow circle), are selected for comparison.

[0157] from Figure 5 The data shows that the economic efficiency, carbon emissions, and energy efficiency of the planning schemes exhibit an approximately linear relationship in some regions, but a non-linear relationship in others. This indicates that some planning schemes within the RIES system are mutually exclusive in terms of economic efficiency, low carbon emissions, and energy efficiency. Furthermore, towards the end of the planning process, as the cost of the scheme increases, its carbon emissions and energy efficiency essentially reach saturation.

[0158] The differences in configuration among the three planning schemes are shown in Table 3 below.

[0159] surface Three different planning schemes

[0160]

[0161] Regarding the energy station equipment configuration in the quantity-quality coordinated planning scheme, due to the high energy efficiency ratio and capacity limitations of HP, EH1 is configured with 150 kW HP and 817 kW EB to meet the surrounding heat load demand of node H22. Since EH1 is an all-electric energy station, 400 kW PV is planned to achieve on-site consumption of new energy, while simultaneously improving the energy station's economic, low-carbon, and energy efficiency goals. EH2, as a non-all-electric energy station, can only transmit electricity externally, therefore no EB or HP are planned. CHP is also not planned due to its high cost and low energy efficiency. Due to the restriction that new energy must be consumed locally and cannot be transmitted back, considering the factors of curtailment and cost, EH2 is planned with 284 kW PV to match the power demand of E13. Simultaneously, to ensure the main heat supply, EH2 is also planned to be equipped with a 1695 kW gas-fired boiler.

[0162] Option 1, due to its lower economic cost, primarily relies on the H07 heat source for heat supply. EH1 is planned with 150 kW of HP and 231 kW of photovoltaic power, while EH2 only plans for a 438 kW gas-fired boiler for heat supply. Because the ratio of HP to PV capacity output is higher compared to other options, Option 1 energy station has higher efficiency.

[0163] Option 2, without considering economic costs, allocates all heat supply to the HP and EB units of EH1 and the GB and CHP units of EH2. The planned two 400 kW PV units and 1169 kW CHP unit provide some operational benefits and reduce carbon emissions. However, due to the lower energy efficiency of the CHP unit, the overall efficiency of the energy station is reduced.

[0164] The economic efficiency, carbon emissions, and energy efficiency of the quantity-quality coordinated planning scheme and the uncoupled scheme without energy stations are shown in Table 4.

[0165] Table 4 Comparison between the quantity-quality coordinated planning scheme and the uncoupled scheme without energy stations

[0166]

[0167] In terms of economics, although configuring energy station equipment will incur a planned investment cost of 1.2806 million yuan, the quantity-quality synergy planning scheme will save 4.4019 million yuan in energy costs annually, reducing operating costs by 34.85%. The average annual expansion cost of the energy network is basically the same for both schemes. Overall, the quantity-quality synergy planning scheme is approximately 17.82% more economical than not constructing an energy station.

[0168] In terms of carbon emissions, the lack of energy stations will result in an additional 2096.09 tons of carbon emissions annually, an increase of approximately one-fifth. Regarding efficiency, the multi-energy coupling equipment in the energy stations improves the overall energy efficiency of the RIES (Resource Energy Systems), increasing its efficiency by 0.11 compared to not having energy stations. The quantity-quality synergy planning scheme achieves improvements in economy, low carbon emissions, and energy efficiency to a certain extent.

[0169] Typical daily operating results under the quantity-quality collaborative planning scheme are as follows: Figures 6 to 8 As shown. Regarding electricity, the HP in EH1 maintains high energy output due to its high energy conversion efficiency, with the remaining heat supply being provided by EB. Photovoltaics, on the other hand, reduce daytime energy demand, especially at midday, by consuming renewable energy locally. Regarding natural gas in EH2, due to the lower energy efficiency and higher investment costs of CHP, EH2 abandoned the CHP configuration and relied entirely on GB for heating. Therefore, the GL08 expansion model connected to EH2 is larger. In terms of heating, due to the uneconomical nature and high carbon emissions of centralized heating stations, heat energy is no longer purchased from heating stations. EH2 and EH1 share the entire heat load demand, and their energy supply ratios are essentially the same.

[0170] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein can be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. The general-purpose processor may be a microprocessor, but in alternatives, it may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors cooperating with a DSP core, or any other such configuration.

[0171] Those skilled in the art will further appreciate that, to clearly illustrate this interchangeability between hardware and software, various illustrative components, frames, modules, circuits, and steps are described above in a generalized manner in terms of their functionality. Whether such functionality is implemented in hardware or software depends on the specific application and the design constraints imposed on the overall system. Those skilled in the art may implement the described functionality in different ways for each specific application, but such implementation decisions should not be construed as departing from the scope of the invention.

Claims

1. A method for coordinated planning of regional integrated energy system stations and networks considering quantity and quality balance, characterized in that, The method includes the following steps: S1. Establish a steady-state energy flow model for the electricity, natural gas and heat subsystems in a regional integrated energy system; S2. Based on the energy flow steady-state model, establish a system carbon flow model; S3. Based on the solution results of the energy flow steady-state model, establish a system energy flow calculation model; S4. Construct a unified steady-state model of energy flow, carbon flow, and carbon dioxide flow in a regional integrated energy system, covering energy transmission, carbon emissions, and carbon dioxide flow distribution in the power, natural gas, and heat subsystems; S5. Establish a two-layer optimization framework based on multiple objectives of "energy-carbon-energy": the upper layer takes quantity and quality balance as the objective and makes decisions on the selection of energy station equipment and the expansion plan of energy network; the lower layer optimizes the distribution of typical daily energy flow-carbon flow-energy flow under the constraints of the upper layer plan to achieve optimal operating economy and feeds back to the upper layer. S6. Introduce the quantitative-quality balance planning criterion, and solve the "energy-carbon-energy" equilibrium solution based on Pareto front solution set fitting and Hessian discriminant method to determine the optimal planning scheme.

2. The method according to claim 1, characterized in that, The establishment of the steady-state energy flow model in S1 specifically includes: S11. Establish a steady-state model of the power subsystem's energy flow, including the active power balance equations and reactive power balance equations at the nodes: In the formula: and These are nodes Injected active and reactive power; , It is a node , The voltage amplitude; It is a node , The susceptance between them; It is a node , The electrical conductance between them; It is a node , The voltage phase angle difference between them; S12. Establish a steady-state energy flow model for the natural gas subsystem, including a pipeline gas flow model, a node pressure and flow model, and a natural gas balance model; The gas flow model states that the natural gas flow rate through the pipeline has a non-linear relationship with the squared difference in natural gas pressure at both ends of the pipeline, as shown in the following equation: In the formula: For pipelines steady-state flow; It is a function symbol; and They are nodes and nodes Square of pressure; These are parameters related to the direction of natural gas flow. For pipelines The drag coefficient; The nodal pressure-flow model—constructed using the flow continuity equation of the natural gas network—is as follows: In the formula: To remove the node-branch correlation matrix of a natural gas network containing compressor pipelines; This represents the flow vector of the natural gas pipeline. This represents the flow vector of natural gas outflow from each natural gas node; The natural gas balance model states that, to ensure the reliability of long-distance energy transmission in the natural gas network, a corresponding compressor needs to be configured in the gas system to compensate for pressure losses caused by friction; the compressor outlet is designated as a node. The entrance is a node. Then, the natural gas balance model for a pipeline containing a compressor can be expressed as: In the formula: This represents the natural gas flow rate at the compressor inlet pipe. This refers to the flow rate of natural gas passing through the compressor. The amount of natural gas consumed by the compressor; This represents the natural gas flow rate of the outlet pipeline. This refers to the compression ratio of the compressor. and These are the pipe constants for the inlet and outlet pipes, respectively. The calorific value of natural gas; The temperature of the natural gas; It is a highly variable index; S13. Establish a steady-state energy flow model for the thermal subsystem, including a hydraulic model and a thermal model; The hydraulic model consists of a flow continuity equation and a loop head equation, and the specific model is as follows: In the formula: This is a connection matrix for the branch nodes of the heating network. This represents the working fluid flow vector in the thermal pipeline. Inject a flow scalar vector into the thermal nodes; The circuit-branch correlation matrix of the heating network; This is the head loss vector for the thermal pipeline; The pipeline resistance matrix; The thermal model consists of a heat equation, a temperature drop equation, and a mixing temperature equation, as shown below: In the formula: For the node thermal power vector, This is the specific heat capacity of water; and These are the node heating temperature vector and the node output temperature vector, respectively. and These are the end-point temperature vector and the beginning-point temperature vector of the pipeline, respectively. The ambient temperature; The heat transfer coefficient of the heat pipe; This refers to the length of the pipe. The flow of traffic into the node; This refers to the flow of traffic leaving the node; and These are the node inflow temperature and the node outflow temperature, respectively. S14. Based on the energy hub model, establish a multi-energy conversion model for energy stations; The multi-energy conversion model of the energy station is as follows: In the formula: This is the output vector; This is the coupling matrix; The input vector.

3. The method according to claim 1, characterized in that: The carbon flow model in S2 includes the definition and calculation of carbon flow rate, carbon flow density and node carbon potential. The carbon flow rate represents the carbon emission flow rate through a node or branch per unit time, the carbon flow density represents the carbon emission caused by a unit of energy transmitted by a branch, and the node carbon potential represents the source-side carbon emission caused by a unit of energy consumption at the node.

4. The method according to claim 1, characterized in that, The flow calculation model in S3 includes: S31. Exergy flow calculation of the power subsystem: Regarding the active power flow as exergy flow and the active power loss as exergy loss; the exergy flow and exergy loss of the power subsystem can be expressed as: In the formula: This indicates taking the real part of a complex number; The line voltage phasor at the beginning of the power line; The conjugate of the line current phasor flowing through the line; and These are the line voltage phasors at both ends of the line; S32. Calculation of flow in natural gas subsystem: Calculate flow based on the product of nodal potential and gas flow rate. Nodal potential is defined as the product of natural gas energy quality coefficient and calorific value. S33. Thermal subsystem flow calculation: Flow is calculated based on the product of temperature-dependent nodal potential and water mass flow rate. Nodal potential is defined as the potential difference between temperature and ambient temperature.

5. The method according to claim 1, characterized in that, The construction of the unified steady-state model of energy flow, carbon flow, and turbulence in S4 is specifically as follows: the equations of the electric, gas, and heat networks are treated as a unified whole, and the state variables are solved synchronously by using a unified Jacobian matrix iteratively. During the iteration process, carbon flow and turbulence are calculated. When the power flow iteration converges, the solutions for carbon flow and turbulence are completed synchronously.

6. The method according to claim 1, characterized in that, The upper-level optimization objective function of the two-layer optimization framework in S5 includes economic cost, carbon emissions, and system efficiency, while the lower-level optimization objective function is the equivalent daily operating economy, including energy operation and maintenance cost, carbon emission cost, and efficiency equivalent economic value.

7. The method according to claim 6, characterized in that, The upper-level optimization constraints include equipment capacity constraints, pipeline type constraints, and energy balance constraints, while the lower-level optimization constraints include energy flow carrying capacity constraints, carbon emission upper limit constraints, and efficiency threshold constraints.

8. The method according to claim 1, characterized in that, The specific criterion for quantity-quality balance planning in S6 is as follows: based on the Pareto front solution set generated by the upper-level multi-objective optimization, a polynomial fitting solution set curve is used, the partial derivative of the fitting curve is obtained, and the equilibrium point is solved based on the Hessian discriminant method. The planning scheme corresponding to the equilibrium point is the optimal planning scheme for the quantity-quality balance of "energy-carbon-energy".

9. A computer-readable storage medium, characterized in that: The medium contains a computer program, which, when executed, performs the method as described in any one of claims 1 to 8.

10. A computer system, characterized in that: It includes a processor and a storage medium, on which a computer program is stored, and the processor reads from the storage medium and runs the computer program to perform the method as described in any one of claims 1 to 8.

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