Flexible regional integrated energy system combined cooling, heating, gas and power dispatching method and system

Abstract

A flexible regional integrated energy system (RIES) cogeneration scheduling method includes: gradually introducing two-stage operation from electricity to gas and combined cooling technology using electric chillers and lithium bromide absorption chillers, and modeling them separately; establishing an RIES optimal scheduling model with the sum of power system network losses and energy purchase costs from the upper-level network as the objective function, and providing the power balance constraints of electricity, gas, heat, and cooling in the RIES; using linearization and second-order cone relaxation techniques to transform the original model into a mixed-integer second-order cone programming model; based on the predicted operating curves of wind turbines, photovoltaics, and various loads, using the established model and calling the Cplex algorithm package on Matlab equipped with the Yalmip optimization toolbox for optimization solution; analyzing the economic benefits of SOP, P2G technology, and lithium bromide absorption chillers in reducing system network losses and costs, and improving the system's ability to absorb wind and solar power. This invention also includes a flexible regional integrated energy system cogeneration scheduling system.

Description

Technical Field

[0001] This invention relates to an optimized scheduling method for combined cooling, heating, gas, and power supply in a flexible regional integrated energy system with intelligent soft switching. Background Technology

[0002] The traditional fossil fuel-driven energy supply model is being rapidly replaced by renewable clean energy (RCE) supply models such as wind power and solar power. However, the increasing power generation ratio of wind and solar power, due to their inherent ability to mitigate peak and off-peak conditions, strong uncertainty, and high volatility, makes them difficult to fully absorb. The persistently high rates of wind and solar curtailment are gradually becoming a bottleneck restricting the sustainable and healthy development of RCE power generation replacement. At the same time, a high RCE penetration rate easily leads to low overall energy utilization, voltage exceeding limits in the distribution network, and power fluctuations, posing a significant challenge to the balance of electricity supply and demand, and consequently significantly weakening the power system's ability to flexibly control its economic and safe operation.

[0003] On the other hand, although my country's energy system construction (such as coal, oil, natural gas, electricity supply, and heat supply) has made significant progress in recent years, the mutual coupling and utilization of these resources remains insufficient, and local imbalances in energy supply and demand occur frequently. There are also many problems, such as inefficiency in energy construction investment and the coordinated utilization of energy coupling equipment. Regional Integrated Energy Systems (RIES), after years of development and improvement, have gradually matured, providing a good approach to addressing issues such as wind and solar power curtailment, improving comprehensive energy utilization, and promoting the substitution of traditional fossil fuels. This allows for coordinated planning, collaborative management, interactive response, mutual complementarity, and optimized operation among various heterogeneous energy supply systems. While meeting the diverse energy demands of its loads, it can also effectively improve the quality and efficiency of the utilization of various heterogeneous energy sources within the system, promoting the green and sustainable development of local energy and society.

[0004] However, the primary challenge facing RIES in terms of optimal scheduling is how to promote the coordinated and optimized operation of various heterogeneous energy supplies within RIES, while maximizing the local absorption of RCEs, accelerating the progress of the "two carbons" vision, improving the comprehensive utilization rate and security and stability of energy within RIES, and further reducing the operating costs of RIES. Current research on RIES mainly focuses on system planning, modeling, and optimal scheduling, which cannot adequately address the primary challenge of optimal scheduling for RIES. Furthermore, due to the inherent characteristics of high-penetration wind and solar RCEs, as mentioned above, the energy conversion, coordination, and absorption capacity of RIES is significantly weakened, leading to an imbalance between supply and demand for RIES electricity. However, the rapidly developing Power to Gas (P2G) technology and the new power electronic device Soft Open Point (SOP) offer new solutions for promoting RCE absorption, reducing the cost of purchasing energy from the upper-level network for RIES, reducing power system losses, improving system voltage levels, and maintaining safe and stable operation.

[0005] Therefore, there is an urgent need for a flexible RIES cogeneration optimization scheduling method with SOP to further study the coordinated and optimized operation of RIES with multiple energy supplies, improve the energy utilization efficiency and safety stability of RIES, promote RCE consumption, and reduce the cost of RIES purchasing energy from the upper-level network. Summary of the Invention

[0006] To overcome the shortcomings of existing technologies, this invention focuses on an optimized scheduling method for flexible RIES (Regenerative Thermal Emission System) with SOP (Standard Operating Procedure) combined cooling, heating, gas, and power, in order to further study the coordinated and optimized operation of RIES with multiple energy supplies, improve the energy utilization efficiency and safety stability of RIES, promote RCE (Regenerative Energy Consumption), and reduce the cost of RIES purchasing energy from the upstream network.

[0007] In the model building process, this invention gradually introduces SOP (Start of Operation), two-stage operation from electricity to gas, and lithium bromide absorption refrigeration technology. During the solution process, linearization and second-order cone relaxation are used to transform the original model into a mixed-integer second-order cone programming (MISOCP) model to achieve fast and accurate calculation of power system line flow. The objective function is set as minimizing the sum of the energy purchase cost from the upstream network and the RIES power system network line loss cost. Finally, a modified IEEE 33-node case is used to test the effectiveness of the proposed model. Through a series of optimization comparison analyses based on quantitative analysis indicators, the effectiveness of the proposed scheduling method in improving the efficiency of multi-energy coupling utilization and improving the economic operation of the system is verified.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] A flexible regional integrated energy system cogeneration dispatching method includes the following steps:

[0010] S1: Construct a mathematical model under the architecture of a flexible regional integrated energy system (RIES) with intelligent soft open point (SOP), gradually introduce two-stage operation of power to gas (P2G) and the combined refrigeration technology of electric chiller and lithium bromide absorption chiller, and model them respectively;

[0011] S2: Using the sum of power system network losses and energy purchase costs from the upper-level network in RIES as the objective function, establish an optimal scheduling model for RIES and give the power balance constraints of electricity, gas, heat and cooling in RIES;

[0012] S3: Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model;

[0013] S4: Using a 24-hour scheduling cycle, based on the predicted operating curves of wind turbines, photovoltaics, and various loads, the Cplex algorithm package is called on Matlab with the Yalmip optimization toolbox to perform optimization and solution using the established model.

[0014] S5: Using the modified IEEE 33-node case study, we analyze the economic benefits of SOP, P2G technology and lithium bromide absorption chiller in reducing system network losses and costs and improving the system's ability to absorb wind and solar power.

[0015] Furthermore, in step S1, the mathematical model of the flexible RIES architecture containing SOP incorporates two-stage electro-gas conversion operation and lithium bromide absorption refrigeration technology, including the following components:

[0016] S1-1: Constructing the SOP model;

[0017] Introducing a Standby Operating Program (SOP) can control the power flow and reactive power compensation in a RIES power system, thereby reducing power system losses and improving the situation of voltage exceedance at power system nodes. The SOP has two main controllable variables: the active power output and reactive power compensation provided by each back-to-back voltage source converter (B2B VSC). Since B2B VSCs are fully controllable power electronic devices, although their efficiency is high enough, some losses are still inevitable when they perform large-scale active power transmission. As for reactive power compensation, due to the isolation effect of their large internal capacitors, the two B2B VSCs are independent of each other, so only their respective capacity limits need to be met. Therefore, the operation and control of the SOP must meet the following constraints:

[0018] 1) SOP active power transmission constraints:

[0019]

[0020]

[0021]

[0022] In the formula, These are the active and reactive power outputs of SOP at node i and node j in the B2B VS during time period t, respectively. Here, the direction of injection into the node is defined as the positive direction of active power transmission and reactive power compensation of SOP. These represent the active power loss of the SOP at node i and node j in time period t, respectively. These are the loss coefficients of the SOP in the B2B VSC at node i and node j, respectively.

[0023] 2) SOP active power transmission constraints:

[0024]

[0025]

[0026] in, These represent the upper and lower limits of reactive power that the SOP can output at node i and node j via B2B VSC to provide reactive power compensation.

[0027] 3) SOP capacity constraints:

[0028]

[0029]

[0030] in The SOP access capacity is connected to nodes i and j.

[0031] S1-2: Constructing a P2G device model;

[0032] The P2G (Polyhydrogen-to-Gas) system generates H2 and O2 through water electrolysis. The generated H2 is partially stored in a hydrogen storage tank and, during periods of demand, supplied to the methane reactor to react with CO2 via a Sabatier reaction to produce artificial natural gas, which is then injected into the RIES (Renewable Energy Systems) pipeline for use by RIES equipment or loads. The remaining portion is directly transferred from the hydrogen storage tank to the methane reactor during the current scheduling period to synthesize artificial natural gas, thus achieving the conversion and utilization of electricity into natural gas and further deepening the coupling of the electricity-gas integrated energy system. Since P2G involves two stages—hydrogen electrolysis and H2 methanation—and this paper uses a hydrogen storage tank as the energy storage device, the efficiency of both stages is approximated as a fixed value. The models of the electrolyzer, methane reactor, and hydrogen storage tank are shown below:

[0033] 1) Electrolytic cell model:

[0034] P P2Hout,t =η P2H P P2Hin,t (8)

[0035]

[0036] ΔP P2Hin,min ≤P P2Hin,t+1 -P P2Hin,t ≤ΔP P2Hin,max (10)

[0037] In the formula, P P2Hin,t P P2Hout,t These represent the input and output power of the electrolytic cell during time period t, respectively. η is the upper limit of the input power of the electrolytic cell. P2H The conversion efficiency of the electrolyzer is taken as 80% in this paper; ΔP P2Hin,max ΔP P2Hin,min These represent the upper and lower limits of the electrolytic cell ramp.

[0038] 2) Methane reactor model:

[0039] P H2Cout,t =η H2C P H2Cin,t (11)

[0040]

[0041] ΔP H2Cin,min ≤P H2Cin,t+1 -P H2Cin,t ≤ΔPH2Cin,max (13)

[0042] In the formula, P H2Cin,t P H2Cout,t These represent the input and output power of the methane reactor during time period t; η is the upper limit of the input power to the methane reactor. H2C The conversion efficiency of the methane reactor is taken as 80% in this paper; ΔP H2Cin,max ΔP H2Cin,min These represent the upper and lower limits of the ramp-up for the methane reactor, respectively.

[0043] 3) Hydrogen storage tank model:

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052] In the formula, These represent the input and output power of the hydrogen storage tank H2 during time period t, respectively. These represent the upper and lower limits of the input and output power of the hydrogen storage tank H2 within a certain time period; These represent the H2 power contained in the hydrogen storage tank during time period t and the upper limit of H2 storage capacity in the hydrogen storage tank, respectively; N T The RIES scheduling period is set to 24 hours per day in this paper.

[0053] S1-3: Construct a gas turbine unit model;

[0054] The gas turbine unit described in this paper comprises a gas turbine and a gas boiler. Both are powered by natural gas from the RIES natural gas pipeline and supply electricity (heat) to the electrical (thermal) loads in the power (heat) system during demand periods. They simultaneously function as a power source in the power system, a gas load in the natural gas system, and a heat source in the thermal system, thus promoting the coupling of the integrated electricity, gas, and heat energy system to a certain extent. The constructed models of the gas turbine and gas boiler are shown below:

[0055] 1) Gas turbine model:

[0056] P g2Eout,t =η g2E P gin,t (twenty two)

[0057] Q gout,t =η g2Q P gin,t (twenty three)

[0058]

[0059] ΔP gin,min ≤P gin,t+1 -P gin,t ≤ΔP gin,max (25)

[0060] In the formula, P gin,t P g2Eout,t Q gout,t These represent the input power and electrical and thermal output power of the gas turbine during time period t, respectively. η is the upper limit input value for the gas turbine. g2E η g2Q The values ​​are the electromechanical and thermal conversion efficiencies of the gas turbine, respectively, which are taken as 35% and 45% in this paper; ΔP gin,max ΔP gin,min These represent the upper and lower limits of the ramp rate for the gas turbine.

[0061] 2) Gas-fired boiler model:

[0062] Q Gout,t =η G2Q P Gin,t (26)

[0063]

[0064] ΔP Gin,min ≤P Gin,t+1 -P Gin,t ≤ΔP Gin,max (28)

[0065] In the formula, P Gin,t Q Gout,t These represent the gas input and heat output power values ​​of the gas-fired boiler during time period t, respectively. η is the upper limit input value for the gas-fired boiler. G2Q The thermal conversion efficiency of the gas turbine is taken as 85% in this paper; ΔP Gin,max ΔP Gin,min These represent the upper and lower limits of the ramp rate for gas-fired boilers.

[0066] S1-4: Construct a combined refrigeration unit model;

[0067] A combined cooling system using an electric chiller and a lithium bromide absorption chiller is employed. The electric chiller directly utilizes electricity from the RIES power system to drive the compressor for cooling. The lithium bromide absorption chiller, on the other hand, uses high-temperature flue gas and waste heat from the gas turbine and gas boiler to perform work, enabling the reuse of waste heat and promoting energy efficiency and economical operation of the RIES. The combined chiller unit models constructed in this paper are shown below:

[0068] 1) Electric refrigeration unit model:

[0069] P P2Cout,t =η P2C P P2Cin,t (29)

[0070]

[0071] ΔP P2Cin,min ≤P P2Cin,t+1 -P P2Cin,t ≤ΔP P2Cin,max (31)

[0072] In the formula, P P2Cin,t P P2Cout,t These represent the electrical input and cooling output power values ​​of the electric chiller during time period t, respectively. η is the upper limit input value for the electric chiller. P2C The refrigeration conversion efficiency of the electric chiller is taken as 90% in this paper; ΔP P2Cin,max ΔP P2Cin,min These represent the upper and lower limits of the ramp rate for the electric chiller.

[0073] 2) Lithium bromide absorption chiller model:

[0074] Q Lin,t =η R (η gloss Q gout,t +η Gloss Q Gout,t (32)

[0075] Q Lout,t =η L Q Lin,t (33)

[0076]

[0077] ΔQ Lin,min ≤Q Lin,t+1 -Q Lin,t ≤ΔQ Lin,max (35)

[0078] In the formula, Q Lin,t Q Lout,tThese represent the heat input and cold output power values ​​of the lithium bromide absorption chiller during time period t, respectively. For lithium bromide absorption chillers, the upper limit input value is η. gloss η Gloss η R η L The heat loss coefficients of recoverable heat from gas turbines, gas boilers, recovery efficiency of recovery devices, and conversion efficiency of lithium bromide absorption chillers are respectively taken as 20%, 15%, 60%, and 80% in this paper; ΔQ Lin,max ΔQ Lin,min These represent the upper and lower limits of the ramp rate for lithium bromide absorption chillers.

[0079] Furthermore, in step S2, the objective function and constraints are established as follows:

[0080] S2-1: Define the objective function;

[0081] The optimization scheduling model aims to minimize the sum of the cost of RIES purchasing energy from the upper-level network and the cost of RIES power system network line losses, i.e., including the cost C of the system purchasing energy from the upper-level network. buy And system power network loss cost C loss Two main parts:

[0082] minF=(C buy +C loss (36)

[0083] In the formula, the system purchases energy from the superior network. buy The system power network loss cost includes two parts: the cost of purchasing electricity from the upstream power grid and the cost of purchasing gas from the upstream gas grid. loss It includes two parts: the cost of power network line losses and the cost of SOP (Standard Operating Procedure) operation losses, which are described in detail below:

[0084] 1) Energy purchase cost:

[0085]

[0086] P ebuy,t =P P2Hin,t +P eload,t -P W,t -P PV,t -P g2Eout,t (38)

[0087] P gbuy,t =P gin,t +P Gin,t +P gload,t -P H2Cout,t (39)

[0088] In the formula, P ebuy,t P gbuy,t These represent the electricity and gas power purchased from the upstream network during time period t; f e f n These are the unit electricity price from the superior power grid and the unit gas price from the superior gas grid, respectively; P eload,t P gload,t P W,t P PV,t These represent the electrical load, gas load, wind turbine output power, and photovoltaic output power during time period t, respectively.

[0089] 2) System power network loss cost:

[0090]

[0091] In the formula, r ij I t,ij Δt and Δt represent the resistance of power system branch ij, the current amplitude of power system branch ij during time period t, and the duration of each time period, respectively. The duration of each time period is set to 1 hour in this paper; N N This represents the total number of nodes in the RIES power system.

[0092] S2-2: Determine the constraints;

[0093] Based on the actual system operation and considering the coupling relationship between the RIES supply side, conversion side, and load side, the RIES power balance constraints for electricity, gas, heat, and cooling are given. These constraints include six parts: power system operation constraints, electricity power balance constraints, gas power balance constraints, heat power balance constraints, cooling power balance constraints, and interaction constraints with the upper-level electricity (gas) network. The specific descriptions are as follows:

[0094] 1) Power system operation constraints:

[0095]

[0096]

[0097]

[0098]

[0099]

[0100]

[0101]

[0102]

[0103] Constraints (41) and (42) represent the active and reactive power balance of node i during time period t, respectively; constraints (43) and (44) represent the sum of active and reactive power injected into node i during time period t, respectively; constraint (45) represents Ohm's law on branch ij during time period t; the current magnitude of each branch during time period t can be determined by constraint (46); and in the formula, P t,ji Q t,ji P represents the active and reactive power on branch ij during time period t, respectively; t,i Q t,i Let x represent the total active and reactive power injected into node i during time period t, respectively; ij Let be the reactance of branch ij in the power system; These represent the active power injected into node i during time period t, specifically the power from the wind turbine, photovoltaic system, and gas turbine. These represent the active power consumed by the electrolytic cell, the active power consumed by the electric chiller, the active power consumed by the electrical load, and the reactive power consumed by the electrical load at node i during time period t, respectively; U t,i U t,j These are the voltage amplitudes at nodes i and j during time period t, respectively. U , These are the upper and lower limits of the node voltage amplitude and the upper limit of the branch current, respectively.

[0104] 2) Power balance constraints:

[0105]

[0106] 3) Gas power balance constraint:

[0107] P gbuy,t +P H2Cout,t -P gin,t -P Gin,t -P gload,t =0 (50)

[0108] 4) Thermal power balance constraint:

[0109] Q gout,t +Q Gout,t -Q hload,t =0 (51)

[0110] 5) Cold power balance constraint:

[0111] P P2Cout,t +Q Lout,t -Q cload,t =0 (52)

[0112] 6) Interaction constraints with the superior electrical network:

[0113]

[0114]

[0115] In the formula, These represent the upper limits of interaction between RIES and the upstream power grid and gas network, respectively.

[0116] In step S3, solving the original model includes the transformation of the original model:

[0117] Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model. Linearization is achieved through variable substitution, i.e., letting v... t,i and l t,ij They represent and The linearized constraints are expressed as follows:

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124] Since there are still quadratic terms, the current constraint (46) is still nonlinear, so it can be relaxed to the following second-order cone constraint:

[0125] ||[2P t,ij 2Q t,ij l t,ij -v t,i ] T ||2≤v t,i +l t,ij (61)

[0126] Furthermore, the operational constraints of SOP are all quadratic nonlinear constraints, therefore they can be converted into the following rotational second-order cone constraints:

[0127]

[0128]

[0129]

[0130] In step S4, the solution process for the established model is as follows:

[0131] S4-1: Solver Tools

[0132] The program was written in the MATLAB R2018b software platform with the YALMIP Optimization Toolbox, and optimization calculations were performed by calling the IBMILOG CPLEX 12.6 algorithm package. The optimization calculations were executed on a PC equipped with an Intel(R) Core(TM) i7-8700 CPU@3.20GHz processor and 8GB RAM, with the software environment being the Windows 10 operating system.

[0133] S4-2: Solution Process

[0134] Based on the standard IEEE 33-bus system, wind turbines, photovoltaics, P2G, electric refrigeration, and gas turbines coupled with the power grid were connected together as a test system to verify the effectiveness of the scheduling model proposed in this paper in improving the efficiency of multi-energy coupling utilization and improving the economic operation of the system.

[0135] To facilitate the local consumption of electricity generated by renewable clean energy sources such as wind and solar power by P2G equipment, two wind turbine generators and four solar power plants were connected to the power system. All wind turbines and solar power plants operated at unity power factor, and local reactive power support from wind turbines and solar power plants was not considered. Daily operating curves for wind turbines, solar power plants, and various loads were obtained through forecasting, using hourly intervals throughout the day. Two sets of 500kVA SOPs (Standard Operating Programs) were installed between the two pairs of nodes, with a reactive power cap of 400kVar, and the loss factor of each SOP converter was assumed to be 0.02.

[0136] In step S5, the analysis process of the optimization results of the established model is as follows:

[0137] S5-1: Set up an optimized scheduling scheme;

[0138] To clearly compare the effectiveness of the optimized scheduling model constructed in this paper in optimizing the multi-energy coupled operation of the system, the following four optimized scheduling schemes are proposed:

[0139] 1) Combined power supply of electricity, gas, heat and cooling based on electric refrigeration, gas boiler heating, gas turbine cogeneration, wind turbine and photovoltaic power generation, and power supply (gas) from the upper-level power (gas) grid;

[0140] 2) Add SOP based on Scheme 1;

[0141] 3) Add P2G equipment based on scheme 2;

[0142] 4) Based on scheme 3, a lithium bromide absorption chiller is added;

[0143] S5-2: Determine the optimization indicators;

[0144] Based on the considered quantitative analysis indicators, the power network line losses, the cost of purchasing energy from the upstream power grid and gas grid, and the total system operating cost are analyzed under different schemes. Specific optimization indicators are as follows:

[0145] 1) Power network line losses;

[0146] 2) Per-unit deviation of node voltage;

[0147] 3) Costs of purchasing energy from the superior electricity and gas grid;

[0148] 4) Total system operating cost;

[0149] 5) Comprehensive utilization rate of photovoltaic and wind turbines.

[0150] The system implementing the flexible regional integrated energy system (RIES) cogeneration scheduling method of the present invention includes, in sequence, a mathematical model construction and modeling module under the RIES architecture of the flexible regional integrated energy system with intelligent soft switching, a module for establishing the RIES optimal scheduling model and the RIES power balance constraint relationship, a mixed integer second-order cone programming model conversion module, an optimization solution module, and an economic benefit analysis module for SOP, P2G technology and lithium bromide absorption chiller.

[0151] The module for constructing and modeling mathematical models of the Regional Integrated Energy System (RIES) architecture with intelligent soft switching (Soft Open Point, SOP) is used to construct mathematical models of the RIES architecture with intelligent soft switching (Soft Open Point, SOP), and gradually introduce two-stage operation of power to gas (P2G) and the combined refrigeration technology of electric chiller and lithium bromide absorption chiller, and model them respectively.

[0152] The module for establishing the RIES optimal scheduling model and the RIES power balance constraint relationship is used to establish the RIES optimal scheduling model with the sum of power system network loss and energy purchase cost from the upper network in RIES as the objective function, and to give the RIES power balance constraint relationship.

[0153] The mixed-integer second-order cone programming model transformation module uses linearization and second-order cone relaxation techniques to transform the original model into a mixed-integer second-order cone programming model.

[0154] The optimization solution module uses a 24-hour scheduling cycle within a day. Based on the predicted operating curves of wind turbines, photovoltaics, and various loads, it uses the already built model to call the Cplex algorithm package on Matlab equipped with the Yalmip optimization toolbox for optimization solution.

[0155] The economic benefit analysis module for SOP, P2G technology and lithium bromide absorption chiller uses a modified IEEE 33-node example to analyze the economic benefits of SOP, P2G technology and lithium bromide absorption chiller in reducing system network losses and costs and improving the system's ability to absorb wind and solar power.

[0156] This invention uses SOP access to RIES, which can effectively reduce power system line losses, balance system power flow distribution, ensure global optimality, and has a moderate computational load.

[0157] The beneficial effects of this invention are:

[0158] 1. Using SOP (Standard Operating Procedure) access to RIES can effectively reduce power system line losses and balance the power flow distribution. This method is based on MISOCP, ensuring global optimality, and has a moderate computational load, making it suitable for large-scale active distribution networks with high RCE penetration to efficiently reduce line losses.

[0159] 2. The electrolyzer, methane reactor, and hydrogen storage tank in the P2G equipment constitute an electro-gas coupling. By converting the electricity that is not fully utilized during the peak periods of wind turbine and photovoltaic power generation into H2 for storage, and then converting it into natural gas through the Sabatier reaction during peak load periods, the H2 is injected into the RIES natural gas pipeline for gas turbine power generation or direct use by gas users. This effectively promotes the local utilization of wind power and photovoltaic power, two types of RCE, reduces system operating costs, and realizes long-term, large-scale spatiotemporal energy transfer.

[0160] 3. The combined use of an electric chiller and a lithium bromide absorption chiller for cooling reduces electricity demand and system operating costs, while improving the overall energy utilization rate. Attached Figure Description

[0161] Figure 1 This is a schematic diagram of the regional integrated energy system of the present invention.

[0162] Figure 2 This is a schematic diagram of the SOP installation position of the present invention.

[0163] Figure 3 This invention relates to a modified IEEE 33-node system architecture.

[0164] Figure 4 These are the daily operating curves for wind turbines, photovoltaic systems, and various loads according to the present invention.

[0165] Figures 5(a) to 5(b) Figure 5(a) shows the active power transmission and reactive power compensation curves of SOP in Scheme 4 of the present invention, and Figure 5(b) shows the reactive power compensation curve of SOP in Scheme 4.

[0166] Figure 6 These are graphs showing power system network losses under different schemes of the present invention.

[0167] Figures 7(a) to 7(b) Figure 7(a) shows the statistical diagram of the RIES power balance before optimization, and Figure 7(b) shows the statistical diagram of the RIES power balance after optimization.

[0168] Figures 8(a) to 8(b) Figure 8(a) shows the statistical diagram of the RIES gas power balance before optimization, and Figure 8(b) shows the statistical diagram of the RIES gas power balance after optimization.

[0169] Figure 9 This is a schematic diagram illustrating the comprehensive utilization rate of wind power and photovoltaic power under different schemes.

[0170] Figures 10(a) to 10(b) Figure 10(a) shows the active power transmission and reactive power compensation curves for SOP in Scheme 2, and Figure 10(b) shows the reactive power compensation curve for SOP in Scheme 2.

[0171] Figures 11(a) to 11(b) Figure 11(a) shows the active power transmission and reactive power compensation of SOP in Scheme 3, and Figure 11(b) shows the reactive power compensation of SOP in Scheme 3.

[0172] Figures 12(a) to 12(d) Figure 12(a) shows the node voltage distribution under different schemes, Figure 12(b) shows the node voltage distribution under scheme 1, Figure 12(c) shows the node voltage distribution under scheme 3, and Figure 12(d) shows the node voltage distribution under scheme 4.

[0173] Figure 13 This is a flowchart of the method of the present invention. Specific implementation methods

[0174] The invention will now be further described with reference to the accompanying drawings.

[0175] Reference Figure 1-Figure 2 2. A method for optimizing the scheduling of combined cooling, heating, gas, and power (CCHP) in a flexible regional integrated energy system with intelligent soft switching, comprising the following steps:

[0176] S1: Construct a mathematical model under the architecture of a flexible regional integrated energy system (RIES) with intelligent soft open point (SOP), gradually introduce two-stage operation of power to gas (P2G) and the combined refrigeration technology of electric chiller and lithium bromide absorption chiller, and model them respectively;

[0177] S2: Using the sum of power system network losses and energy purchase costs from the upper-level network in RIES as the objective function, establish an optimal scheduling model for RIES and give the power balance constraints of electricity, gas, heat and cooling in RIES;

[0178] S3: Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model;

[0179] S4: Using a 24-hour scheduling cycle, based on the predicted operating curves of wind turbines, photovoltaics, and various loads, the Cplex algorithm package is called on Matlab with the Yalmip optimization toolbox to perform optimization and solution using the established model.

[0180] S5: Using the modified IEEE 33-node case study, we analyze the economic benefits of SOP, P2G technology and lithium bromide absorption chiller in reducing system network losses and costs and improving the system's ability to absorb wind and solar power.

[0181] In step S1, the mathematical model of the flexible RIES architecture containing SOP incorporates two-stage electro-gas conversion operation and lithium bromide absorption refrigeration technology, including the following components:

[0182] S1-1: Construction of the SOP Model

[0183] Introducing a Standby Operating Program (SOP) can control the power flow and reactive power compensation in a RIES power system, thereby reducing power system losses and improving the situation of voltage exceedance at power system nodes. The SOP has two main controllable variables: the active power output and reactive power compensation provided by each back-to-back voltage source converter (B2B VSC). Since B2B VSCs are fully controllable power electronic devices, although their efficiency is high enough, some losses are still inevitable when they perform large-scale active power transmission. As for reactive power compensation, due to the isolation effect of their large internal capacitors, the two B2B VSCs are independent of each other, so only their respective capacity limits need to be met. Therefore, the operation and control of the SOP must meet the following constraints:

[0184] 1) SOP active power transmission constraints:

[0185]

[0186]

[0187]

[0188] In the formula, These are the active and reactive power outputs of SOP at node i and node j in the B2B VS during time period t, respectively. Here, the direction of injection into the node is defined as the positive direction of active power transmission and reactive power compensation of SOP. These represent the active power loss of the SOP at node i and node j in time period t, respectively. These are the loss coefficients of the SOP in the B2B VSC at node i and node j, respectively.

[0189] 2) SOP active power transmission constraints:

[0190]

[0191]

[0192] in, These represent the upper and lower limits of reactive power that the SOP can output at node i and node j via B2B VSC to provide reactive power compensation.

[0193] 3) SOP capacity constraints:

[0194]

[0195]

[0196] in The SOP access capacity is connected to nodes i and j.

[0197] S1-2: P2G Device Model Construction

[0198] The P2G (Polyhydrogen-to-Gas) system generates H2 and O2 through water electrolysis. The generated H2 is partially stored in a hydrogen storage tank and, during periods of demand, supplied to the methane reactor to react with CO2 via a Sabatier reaction to produce artificial natural gas, which is then injected into the RIES (Renewable Energy Systems) pipeline for use by RIES equipment or loads. The remaining portion is directly transferred from the hydrogen storage tank to the methane reactor during the current scheduling period to synthesize artificial natural gas, thus achieving the conversion and utilization of electricity into natural gas and further deepening the coupling of the electricity-gas integrated energy system. Since P2G involves two stages—hydrogen electrolysis and H2 methanation—and this paper uses a hydrogen storage tank as the energy storage device, the efficiency of both stages is approximated as a fixed value. The models of the electrolyzer, methane reactor, and hydrogen storage tank are shown below:

[0199] 1) Electrolytic cell model:

[0200] P P2Hout,t =η P2H P P2Hin,t (8)

[0201]

[0202] ΔP P2Hin,min ≤P P2Hin,t+1 -P P2Hin,t ≤ΔP P2Hin,max (10)

[0203] In the formula, P P2Hin,t P P2Hout,t These represent the input and output power of the electrolytic cell during time period t, respectively. η is the upper limit of the input power of the electrolytic cell. P2H The conversion efficiency of the electrolyzer is taken as 80% in this paper; ΔP P2Hin,max ΔP P2Hin,min These represent the upper and lower limits of the electrolytic cell ramp.

[0204] 2) Methane reactor model:

[0205] P H2Cout,t =η H2C P H2Cin,t (11)

[0206]

[0207] ΔP H2Cin,min ≤P H2Cin,t+1 -P H2Cin,t ≤ΔP H2Cin,max (13)

[0208] In the formula, P H2Cin,t P H2Cout,t These represent the input and output power of the methane reactor during time period t; The upper limit of the input power to the methane reactor; η H2C The conversion efficiency of the methane reactor is taken as 80% in this paper; ΔP H2Cin,max ΔP H2Cin,min These represent the upper and lower limits of the ramp-up for the methane reactor, respectively.

[0209] 3) Hydrogen storage tank model:

[0210]

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218] In the formula, These represent the input and output power of the hydrogen storage tank H2 during time period t, respectively. These represent the upper and lower limits of the input and output power of the hydrogen storage tank H2 within a certain time period; These represent the H2 power contained in the hydrogen storage tank during time period t and the upper limit of H2 storage capacity in the hydrogen storage tank, respectively; N T The RIES scheduling period is set to 24 hours per day in this paper.

[0219] S1-3: Construction of Gas Turbine Unit Model

[0220] The gas turbine unit described in this paper comprises a gas turbine and a gas boiler. Both are powered by natural gas from the RIES natural gas pipeline and supply electricity (heat) to the electrical (thermal) loads in the power (heat) system during demand periods. They simultaneously function as a power source in the power system, a gas load in the natural gas system, and a heat source in the thermal system, thus promoting the coupling of the integrated electricity, gas, and heat energy system to a certain extent. The constructed models of the gas turbine and gas boiler are shown below:

[0221] 1) Gas turbine model:

[0222] P g2Eout,t =η g2E P gin,t (twenty two)

[0223] Qgout,t =η g2Q P gin,t (twenty three)

[0224]

[0225] ΔP gin,min ≤P gin,t+1 -P gin,t ≤ΔP gin,max (25)

[0226] In the formula, P gin,t P g2Eout,t Q gout,t These represent the input power and electrical and thermal output power of the gas turbine during time period t, respectively. η is the upper limit input value for the gas turbine. g2E η g2Q The values ​​are the electromechanical and thermal conversion efficiencies of the gas turbine, respectively, which are taken as 35% and 45% in this paper; ΔP gin,max ΔP gin,min These represent the upper and lower limits of the ramp rate for the gas turbine.

[0227] 2) Gas-fired boiler model:

[0228] Q Gout,t =η G2Q P Gin,t (26)

[0229]

[0230] ΔP Gin,min ≤P Gin,t+1 -P Gin,t ≤ΔP Gin,max (28)

[0231] In the formula, P Gin,t Q Gout,t These represent the gas input and heat output power values ​​of the gas-fired boiler during time period t, respectively. η is the upper limit input value for the gas-fired boiler. G2Q The thermal conversion efficiency of the gas turbine is taken as 85% in this paper; ΔP Gin,max ΔP Gin,min These represent the upper and lower limits of the ramp rate for gas-fired boilers.

[0232] S1-4: Construction of the Combined Refrigeration Unit Model

[0233] A combined cooling system using an electric chiller and a lithium bromide absorption chiller is employed. The electric chiller directly utilizes electricity from the RIES power system to drive the compressor for cooling. The lithium bromide absorption chiller, on the other hand, uses high-temperature flue gas and waste heat from the gas turbine and gas boiler to perform work, enabling the reuse of waste heat and promoting energy efficiency and economical operation of the RIES. The combined chiller unit models constructed in this paper are shown below:

[0234] 1) Electric refrigeration unit model:

[0235] P P2Cout,t =η P2C P P2Cin,t (29)

[0236]

[0237] ΔP P2Cin,min ≤P P2Cin,t+1 -P P2Cin,t ≤ΔP P2Cin,max (31)

[0238] In the formula, P P2Cin,t P P2Cout,t These represent the electrical input and cooling output power values ​​of the electric chiller during time period t, respectively. η is the upper limit input value for the electric chiller. P2C The refrigeration conversion efficiency of the electric chiller is taken as 90% in this paper; ΔP P2Cin,max ΔP P2Cin,min These represent the upper and lower limits of the ramp rate for the electric chiller.

[0239] 2) Lithium bromide absorption chiller model:

[0240] Q Lin,t =η R (η gloss Q gout,t +η Gloss Q Gout,t (32)

[0241] Q Lout,t =η L Q Lin,t (33)

[0242]

[0243] ΔQ Lin,min ≤Q Lin,t+1 -Q Lin,t ≤ΔQ Lin,max (35)

[0244] In the formula, Q Lin,t Q Lout,tThese represent the heat input and cold output power values ​​of the lithium bromide absorption chiller during time period t, respectively. For lithium bromide absorption chillers, the upper limit input value is η. gloss η Gloss η R η L The heat loss coefficients of recoverable heat from gas turbines, gas boilers, recovery efficiency of recovery devices, and conversion efficiency of lithium bromide absorption chillers are respectively taken as 20%, 15%, 60%, and 80% in this paper; ΔQ Lin,max ΔQ Lin,min These represent the upper and lower limits of the ramp rate for lithium bromide absorption chillers.

[0245] In step S2, the objective function and constraints are established as follows:

[0246] S2-1: Establish the objective function

[0247] The optimization scheduling model aims to minimize the sum of the cost of RIES purchasing energy from the upper-level network and the cost of RIES power system network line losses, i.e., including the cost C of the system purchasing energy from the upper-level network. buy And system power network loss cost C loss Two main parts:

[0248] minF=(C buy +C loss (36)

[0249] In the formula, the system purchases energy from the superior network. buy The system power network loss cost includes two parts: the cost of purchasing electricity from the upstream power grid and the cost of purchasing gas from the upstream gas grid. loss It includes two parts: the cost of power network line losses and the cost of SOP (Standard Operating Procedure) operation losses, which are described in detail below:

[0250] 1) Energy purchase cost:

[0251]

[0252] P ebuy,t =P P2Hin,t +P eload,t -P W,t -P PV,t -P g2Eout,t (38)

[0253] P gbuy,t =P gin,t +P Gin,t +P gload,t -P H2Cout,t (39)

[0254] In the formula, Pebuy,t P gbuy,t These represent the electricity and gas power purchased from the upstream network during time period t; f e f n These are the unit electricity price from the superior power grid and the unit gas price from the superior gas grid, respectively; P eload,t P gload,t P W,t P PV,t These represent the electrical load, gas load, wind turbine output power, and photovoltaic output power during time period t, respectively.

[0255] 2) System power network loss cost:

[0256]

[0257] In the formula, r ij I t,ij Δt and Δt represent the resistance of power system branch ij, the current amplitude of power system branch ij during time period t, and the duration of each time period, respectively. The duration of each time period is set to 1 hour in this paper; N N This represents the total number of nodes in the RIES power system.

[0258] S2-2: Determine the constraints

[0259] Based on the actual system operation and considering the coupling relationship between the RIES supply side, conversion side, and load side, the RIES power balance constraints for electricity, gas, heat, and cooling are given. These constraints include six parts: power system operation constraints, electricity power balance constraints, gas power balance constraints, heat power balance constraints, cooling power balance constraints, and interaction constraints with the upper-level electricity (gas) network. The specific descriptions are as follows:

[0260] 1) Power system operation constraints:

[0261]

[0262]

[0263]

[0264]

[0265]

[0266]

[0267]

[0268]

[0269] Constraints (41) and (42) represent the active and reactive power balance of node i during time period t, respectively; constraints (43) and (44) represent the sum of active and reactive power injected into node i during time period t, respectively; constraint (45) represents Ohm's law on branch ij during time period t; the current magnitude of each branch during time period t can be determined by constraint (46); and in the formula, P t,ji Q t,ji P represents the active and reactive power on branch ij during time period t, respectively; t,i Q t,i Let x represent the total active and reactive power injected into node i during time period t, respectively; ij Let be the reactance of branch ij in the power system; These represent the active power injected into node i during time period t, specifically the power from the wind turbine, photovoltaic system, and gas turbine. These represent the active power consumed by the electrolytic cell, the active power consumed by the electric chiller, the active power consumed by the electrical load, and the reactive power consumed by the electrical load at node i during time period t, respectively; U t,i U t,j These are the voltage amplitudes at nodes i and j during time period t, respectively. U , These are the upper and lower limits of the node voltage amplitude and the upper limit of the branch current, respectively.

[0270] 2) Power balance constraints:

[0271]

[0272] 3) Gas power balance constraint:

[0273] P gbuy,t +P H2Cout,t -P gin,t -P Gin,t -P gload,t =0 (50)

[0274] 4) Thermal power balance constraint:

[0275] Q gout,t +Q Gout,t -Q hload,t =0 (51)

[0276] 5) Cold power balance constraint:

[0277] P P2Cout,t +Q Lout,t -Q cload,t =0 (52)

[0278] 6) Interaction constraints with the superior electrical network:

[0279]

[0280]

[0281] In the formula, These represent the upper limits of interaction between RIES and the upstream power grid and gas network, respectively.

[0282] In step S3, solving the original model includes the transformation of the original model:

[0283] Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model. Linearization is achieved through variable substitution, i.e., letting v... t,i and l t,ij They represent and The linearized constraints are expressed as follows:

[0284]

[0285]

[0286]

[0287]

[0288]

[0289]

[0290] Since there are still quadratic terms, the current constraint (46) is still nonlinear, so it can be relaxed to the following second-order cone constraint:

[0291] ||[2P t,ij 2Q t,ij l t,ij -v t,i ] T ||2≤v t,i +l t,ij (61)

[0292] Furthermore, the operational constraints of SOP are all quadratic nonlinear constraints, therefore they can be converted into the following rotational second-order cone constraints:

[0293]

[0294]

[0295]

[0296] In step S4, the solution process for the established model is as follows:

[0297] S4-1: Solver Tools

[0298] The program was written in the MATLAB R2018b software platform with the YALMIP Optimization Toolbox, and optimization calculations were performed by calling the IBMILOG CPLEX 12.6 algorithm package. The optimization calculations were executed on a PC equipped with an Intel(R) Core(TM) i7-8700 CPU@3.20GHz processor and 8GB RAM, with the software environment being the Windows 10 operating system.

[0299] S4-2: Solution Process

[0300] Based on the standard IEEE 33-bus system, wind turbines, photovoltaics, P2G, electric refrigeration, and gas turbines coupled with the power grid were connected together as a test system to verify the effectiveness of the scheduling model proposed in this paper in improving the efficiency of multi-energy coupling utilization and improving the economic operation of the system.

[0301] To facilitate the local consumption of electricity generated by renewable clean energy sources such as wind and solar power by P2G equipment, two wind turbine generators and four solar power plants were connected to the power system. All wind turbines and solar power plants operated at unity power factor, and local reactive power support from wind turbines and solar power plants was not considered. Daily operating curves for wind turbines, solar power plants, and various loads were obtained through forecasting, using hourly intervals throughout the day. Two sets of 500kVA SOPs (Standard Operating Programs) were installed between the two pairs of nodes, with a reactive power cap of 400kVar, and the loss factor of each SOP converter was assumed to be 0.02.

[0302] In step S5, the analysis process of the optimization results of the established model is as follows:

[0303] S5-1: Optimize scheduling scheme settings

[0304] To clearly compare the effectiveness of the optimized scheduling model constructed in this paper in optimizing the multi-energy coupled operation of the system, the following four optimized scheduling schemes are proposed:

[0305] 1) Combined power supply of electricity, gas, heat and cooling based on electric refrigeration, gas boiler heating, gas turbine cogeneration, wind turbine and photovoltaic power generation, and power supply from the upper-level power (gas) grid;

[0306] 2) Add SOP based on Scheme 1;

[0307] 3) Add P2G equipment based on scheme 2;

[0308] 4) Based on scheme 3, a lithium bromide absorption chiller is added;

[0309] S5-2: Determine optimization indicators

[0310] Based on the considered quantitative analysis indicators, the power network line losses, the cost of purchasing energy from the upstream power grid and gas grid, and the total system operating cost are analyzed under different schemes. Specific optimization indicators are as follows:

[0311] 1) Power network line losses;

[0312] 2) Per-unit deviation of node voltage;

[0313] 3) Costs of purchasing energy from the superior electricity and gas grid;

[0314] 4) Total system operating cost;

[0315] 5) Comprehensive utilization rate of photovoltaic and wind turbines.

[0316] The system implementing the flexible regional integrated energy system (RIES) cogeneration scheduling method of the present invention includes, in sequence, a mathematical model construction and modeling module under the RIES architecture of the flexible regional integrated energy system with intelligent soft switching, a module for establishing the RIES optimal scheduling model and the RIES power balance constraint relationship, a mixed integer second-order cone programming model conversion module, an optimization solution module, and a module for analyzing the economic benefits generated by SOP, P2G technology and lithium bromide absorption chiller. Each module sequentially contains the technical content of steps S1 to S5 of the present invention.

[0317] To enable those skilled in the art to better understand the present invention, the numerical example analysis includes the following components:

[0318] I. Case Description and Simulation Result Analysis

[0319] The program was written in the MATLAB R2018b software platform with the YALMIP Optimization Toolbox, and optimization calculations were performed by calling the IBMILOG CPLEX 12.6 algorithm package. The optimization calculations were executed on a PC equipped with an Intel(R) Core(TM) i7-8700 CPU@3.20GHz processor and 8GB RAM, with the software environment being the Windows 10 operating system.

[0320] Based on the standard IEEE 33-bus system, wind turbines, photovoltaics, P2G, electric refrigeration, and gas turbines coupled to the power grid were integrated into the test system to verify the effectiveness of the proposed scheduling model in improving the efficiency of multi-energy coupling and enhancing the economic operation of the system. The constructed RIES IEEE 33-bus power system network is as follows: Figure 3As shown, its rated voltage is 12.66kV. To promote the local consumption of electricity generated by wind power, photovoltaics, and other RCEs by P2G equipment, two wind turbine generators and four photovoltaic power stations are connected to the power system. All wind turbines and photovoltaics operate at unity power factor, and the local reactive power support of wind turbines and photovoltaics is not considered. The basic installation parameters are shown in Table 1. The article uses hourly intervals throughout the day as a stepping time period and obtains the daily operating curves of wind turbines, photovoltaics, and various loads through prediction, as shown in Table 1. Figure 4 As shown in the figure. Two sets of SOPs with a capacity of 500kVA each were installed between nodes 12 and 22 and between nodes 25 and 29, with a reactive power limit of 400kVar, and it is assumed that the loss factor of each SOP converter is 0.02.

[0321] To clearly compare the effectiveness of the optimized scheduling model constructed in this paper in optimizing the multi-energy coupled operation of the system, the following four optimized scheduling schemes are proposed:

[0322] Option 1: Combined power supply of electricity, gas, heat and cooling based on electric refrigeration, gas boiler heating, gas turbine cogeneration, wind turbine and photovoltaic power generation, and power supply from the upstream power (gas) grid;

[0323] Option 2: Add SOP based on Option 1;

[0324] Option 3: Add P2G devices based on Option 2;

[0325] Option 4: Based on Option 3, add a lithium bromide absorption chiller, which is the optimized scheduling method proposed in the article;

[0326] The quantitative analysis indicators in this article are: ① power grid line losses; ② per-unit voltage deviation at nodes; ③ energy purchase costs from upstream power and gas grids; ④ total system operating costs; and ⑤ comprehensive utilization rate of photovoltaic and wind turbines.

[0327] Table 1 Basic Installed Capacity of Wind Turbines and Photovoltaics

[0328]

[0329] Based on the considered quantitative analysis indicators, Table 2 shows the power network line losses, energy purchase costs from the upper-level power grid and gas grid, and total system operating costs under different schemes; while Table 3 shows the per-unit voltage range and deviation of each of the 33 nodes within a scheduling cycle. The specific optimization results are analyzed as follows:

[0330] Table 2 Network Loss and Various Costs under Different Schemes

[0331]

[0332]

[0333]

[0334] Table 3 shows the per-unit voltage range and deviation for each of the 33 nodes during a scheduling cycle.

[0335] 1) Power network line loss analysis:

[0336] Based on the case analysis, the article obtained the active power transfer and reactive power compensation curves of SOP in Scheme 4 at different time periods after adding SOP, as shown in Figures 5(a) and 5(b). As for the active power transfer and reactive power compensation curves of SOP in Schemes 2 and 3, since SOPs all have actions and these are comparative schemes, they will not be elaborated here; the specific curves can be found in Figures 10 and 11. In addition, by comparing the simulation results of the four schemes, the article also obtained the power system network loss curves under different schemes, as shown in Figures 5(a) and 5(b). Figure 6 As shown in Figure 5(a), the SOP operation strategy in Scheme 4 is consistent with the power supply and demand of RIES. Between 7:00 and 20:00, wind turbines and photovoltaics cannot meet the high power demand of the current nodes locally. The two SOPs transfer active power to nodes 12 and 29 to alleviate the power demand of the system. The SOPs regulate the active and reactive power flow of the power system by transferring active power and providing reactive power compensation, and respond promptly to voltage fluctuations caused by DG, thereby reducing power line network losses.

[0337] From Tables 2 and 3 and Figure 6 It can be seen that after Scheme 2 incorporates the Standard Operating Procedure (SOP), the power system network loss is significantly reduced, from 755.31kW to 376.95kW, a reduction of 50.09%. In Scheme 3, after incorporating P2G equipment, the active power transmission of power lines increases due to the increased absorption of excess power generated by wind turbines and photovoltaics, leading to a corresponding increase in network loss. However, compared to Scheme 1, Schemes 3 and 4 still show reductions in network loss of 25.60% and 25.32%, respectively, thus verifying the positive effect of incorporating the SOP on reducing power system network loss.

[0338] 2) Analysis of per-unit voltage deviation at nodes:

[0339] As shown in Table 3, without the addition of SOP in Scheme 1, the lowest per-unit voltage value of each of the 33 nodes within a scheduling cycle was 0.9425, indicating a relatively serious voltage limit violation. The specific voltage limit violation situation can be seen in Figure 12. However, after adding SOP in Scheme 2, the lowest per-unit voltage value of each of the 33 nodes within a scheduling cycle was 0.9696, a direct increase of 0.0271 from 0.9425 in Scheme 1, and the voltage limit violation situation disappeared. Although Schemes 3 and 4, after connecting P2G equipment, increased the active power of the power lines, leading to a decrease in node voltage, the reactive power compensation provided by SOP maintained it in a non-limit-violation state, improving the system's operational economy and safety. This verifies the advantages of SOP in voltage and reactive power control, improving feeder voltage levels. The node voltage distribution can also be seen in Figure 12.

[0340] 3) Analysis of energy purchase costs from the upstream power grid and gas grid:

[0341] As shown in Table 2, after adding SOP, P2G, and lithium bromide absorption chiller in sequence, the cost of electricity purchased from the upstream grid decreased from RMB 2060.55 to RMB 2035.22, RMB 1805.67, and RMB 1407.09, respectively, representing cost reductions of 1.23%, 12.37%, and 31.71%. Meanwhile, the cost of gas purchased from the upstream gas network decreased from RMB 12861.52 to RMB 12855.91, RMB 9184.79, and RMB 8863.41, respectively, representing cost reductions of 0.04%, 28.59%, and 31.09%. The reason why the cost reduction percentage of Scheme 2 is relatively small is that Scheme 2 only adds SOP, and SOP itself suffers losses during operation, resulting in a less significant reduction in the total network loss. However, by providing reactive power compensation, SOP optimizes the power system node voltage from the over-limit state of Scheme 1 to the non-over-limit state, thereby reducing power system network losses, balancing power flow distribution, and improving power system feeder voltage levels. This effectively verifies the important role of SOP, P2G equipment, and lithium bromide absorption chillers in improving the economic efficiency of RIES operation, while promoting wind and solar energy integration and reusing waste heat from gas turbines and gas boilers to enhance the overall energy utilization rate of RIES. The power balance of electricity and gas before and after optimization is shown in Figures 7(a), 7(b), 8(a), and 8(b), respectively.

[0342] 4) Analysis of the comprehensive utilization rate of photovoltaic and wind turbines:

[0343] By comparing the simulation results of the four schemes, the following graphs were plotted. Figure 9The graphs show the comprehensive utilization rates of wind and solar power under different schemes. Since P2G equipment is the most important factor in promoting the absorption of wind and solar power, and adding SOPs only slightly promotes the absorption of wind and solar power by reducing grid losses, the comprehensive utilization rates of wind and solar power are almost equal in Schemes 1 and 2. However, after adding P2G equipment, the excess electricity generated by wind power at night and solar power during the day is converted into natural gas that is easy to store and transmit on a large scale and input into the gas grid for storage. During peak electricity and gas consumption periods, this natural gas is reused or used directly through combined heat and power (CHP) of gas turbine units. Therefore, the comprehensive utilization rate of wind turbines and solar power in Schemes 3 and 4 is 100% in every period. As shown in Figure 9, the comprehensive utilization rate of Schemes 1 and 2 is 100% only during the peak electricity load period from 17:00 to 23:00; the comprehensive utilization rate is lower during other periods. This verifies that P2G equipment can effectively absorb large-scale wind and solar power and realize the long-term, large-scale spatiotemporal transfer of energy. It is an effective way to promote wind power absorption, reduce wind and solar curtailment, and realize "high generation and low storage" arbitrage.

[0344] 5) Total system operating cost analysis:

[0345] As shown in Table 1, after adding SOP, P2G, and lithium bromide absorption chillers in sequence, the total operating cost of the system decreased from RMB 15,345.05 to RMB 15,234.15, RMB 11,509.71, and RMB 10,787.27, respectively, representing cost reductions of 0.72%, 24.99%, and 29.70%. Furthermore, the addition of the lithium bromide absorption chiller, by utilizing waste heat and residual heat for cooling, further reduced the system cost by 4.71%. This verifies that using electric chillers in conjunction with lithium bromide absorption chillers can effectively improve comprehensive energy utilization and reduce the overall operating cost of the system.

[0346] II. Conclusion

[0347] 1) Using SOP to access RIES can effectively reduce power system line losses and balance the power flow distribution. This method is based on MISOCP, ensuring global optimality, and has a moderate computational load, making it suitable for large-scale active distribution networks with high RCE penetration to efficiently reduce line losses.

[0348] 2) The electrolyzer, methane reactor, and hydrogen storage tank in the P2G equipment constitute an electro-gas coupling. By converting the electricity that is not fully consumed during the peak periods of wind turbine and photovoltaic power generation into H2 for storage, and then converting it into natural gas through the Sabatier reaction during peak load periods, the H2 is injected into the RIES natural gas pipeline for gas turbine power generation or direct use by gas users. This effectively promotes the local consumption of wind power and photovoltaic power, which are two types of RCEs, reduces the system operating cost, and realizes long-term and large-scale spatiotemporal transfer of energy.

[0349] 3) The combined use of electric chillers and lithium bromide absorption chillers for cooling reduces electricity demand and system operating costs, and improves the overall utilization rate of energy.

[0350] In this specification, the illustrative descriptions of the invention are not necessarily directed at the same embodiments or examples. Those skilled in the art can combine and integrate the different embodiments or examples described in this specification. Furthermore, the content described in the embodiments of this specification is merely an enumeration of implementation forms of the inventive concept, and the scope of protection of the invention should not be regarded as limited to the specific forms stated in the embodiments. The scope of protection of the invention also includes equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A flexible regional integrated energy system cogeneration dispatching method, characterized in that, Includes the following steps: S1: Construct a mathematical model of the flexible regional integrated energy system RIES architecture with intelligent soft switching SOP, gradually introduce the two-stage operation of electric to gas P2G and the combined refrigeration technology of electric chiller and lithium bromide absorption chiller, and model them respectively. S2: Taking the sum of power system network loss and energy purchase cost from the upper network in the Flexible Regional Integrated Energy System (RIES) as the objective function, establish an optimal scheduling model for the RIES and give the power balance constraints of electricity, gas, heat and cooling in the RIES. S3: Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model; S4: Using a 24-hour scheduling cycle, based on the predicted operating curves of wind turbines, photovoltaics, and various loads, the Cplex algorithm package is called on Matlab with the Yalmip optimization toolbox to perform optimization and solution using the established model. S5: Using the modified IEEE 33-node case study, we analyze the economic benefits of smart soft switching (SOP), electric-to-gas (P2G) technology, and lithium bromide absorption chiller in reducing system network losses and costs, and improving the system's ability to absorb wind and solar power.

2. The flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 1, characterized in that, Step S1, which introduces two-stage electro-gas conversion and lithium bromide absorption refrigeration technology into the mathematical model of the flexible RIES architecture with intelligent soft-switching SOP, specifically includes: S1-1: Constructing the SOP model for intelligent soft switches; Introducing a Standard Operating Procedure (SOP) can control the power flow and reactive power compensation in a RIES power system, thereby reducing power system losses and improving the situation of voltage exceedance at power system nodes. The SOP has two controllable variables: the active power output and reactive power compensation provided by each back-to-back voltage source converter (B2B VSC). Since the B2B VSC is a fully controllable power electronic device, although its efficiency is high enough, some losses will still inevitably occur when it performs large-scale active power transmission. Regarding reactive power compensation, due to the isolation effect of its large internal capacitors, the two B2B VSCs are independent of each other, so only their respective capacity limits need to be met. Therefore, the operation and control of the SOP must meet the following constraints: T1) Active power transmission constraints of intelligent soft switch SOP: In the formula, These are the active and reactive power outputs of SOP at node i and node j in the B2BVS during time period t, respectively. Here, the direction of injection into the node is defined as the positive direction of active power transmission and reactive power compensation of SOP. The active power loss of the SOP at node i and node j during time period t is respectively. These are the loss coefficients of the SOP at node i and node j, respectively, for the B2B VSC. T2) Reactive power transmission constraints of intelligent soft switching SOP: in, These represent the upper and lower limits of reactive power that the intelligent soft switch SOP can output at the B2B VSC between node i and node j to provide reactive power compensation. T3) Intelligent Soft Switch SOP Capacity Constraints: in The access capacity of the smart soft switch SOP connected to nodes i and j; S1-2: Constructing a model for an electro-gas P2G equipment; The power-to-gas (P2G) equipment generates H2 and O2 through water electrolysis. The generated H2 is partially stored in a hydrogen storage tank and, during periods of demand, supplied to a methane reactor to react with CO2 via a Sabatier reaction to produce artificial natural gas, which is then injected into the RIES natural gas pipeline for use by RIES gas-consuming equipment or loads. The remaining portion is directly transferred from the hydrogen storage tank to the methane reactor for further synthesis of artificial natural gas during the current scheduling period. This achieves the conversion and utilization of more electricity into natural gas and further deepens the coupling of the electricity-gas integrated energy system. Since the P2G system includes two stages—hydrogen production by electricity and H2 methanation—and uses a hydrogen storage tank as the energy storage device, the efficiencies of hydrogen production by electricity and H2 methanation are approximated as fixed values. The models of the electrolyzer, methane reactor, and hydrogen storage tank are shown below: M1) Electrolyzer Model: P.S P2Hout,t Hη P2H P.S P2Hin,t (8) △P P2Hin,min ≤P P2Hin,t+1 -P P2Hin,t ≤△P P2Hin,max (10) In the formula, P P2Hin,t P P2Hout,t These represent the input and output power of the electrolytic cell during time period t, respectively. η is the upper limit of the input power of the electrolytic cell. P2H The conversion efficiency of the electrolyzer is taken as 80%; ΔP P2Hin,max , △P P2Hin,min These represent the upper and lower limits of the electrolytic cell ramp rate, respectively. M2) Methane reactor model: P.S H2Cout,t Hη H2C P.S H2Cin,t (11) △P H2Cin,min ≤P H2Cin,t+1 -P H2Cin,t ≤△P H2Cin,max (13) In the formula, P H2Cin,t P H2Cout,t These represent the input and output power of the methane reactor during time period t; The upper limit of the input power to the methane reactor; η H2C The conversion efficiency of the methane reactor is taken as 80%; ΔP H2Cin,max , △P H2Cin,min These represent the upper and lower limits of the ramp-up for a methane reactor; M3) Hydrogen storage tank model: In the formula, These represent the input and output power of the hydrogen storage tank H2 during time period t, respectively. These represent the upper and lower limits of the input and output power of the hydrogen storage tank H2 within a certain time period; These represent the H2 power contained in the hydrogen storage tank during time period t and the upper limit of H2 storage capacity in the hydrogen storage tank, respectively; N T The RIES scheduling period is set to 24 hours within a day. S1-3: Construct a gas turbine unit model; The gas turbine unit consists of a gas turbine and a gas boiler. Its energy source is natural gas from the RIES natural gas pipeline of the Flexible Regional Integrated Energy System. It supplies power to the electrical load in the power system and heat to the thermal load in the thermal system when needed. It also serves as a power source in the power system, a gas load in the natural gas system, and a heat source in the thermal system, thus promoting the coupling of the integrated energy system of electricity, gas and heat. The constructed gas turbine and gas boiler models are shown below: N1) Gas turbine model: P.S g2Eout,t Hη g2E P.S gin,t (22) Q gout,t =η g2Q P gin,t (23) △P gin,min ≤P gin,t+1 -P gin,t ≤△P gin,max In equation (25), P gin,t P g2Eout,t Q gout,t These represent the input power and electrical and thermal output power of the gas turbine during time period t, respectively. η is the upper limit input value for the gas turbine. g2E η g2Q The values ​​are the electrical and thermal conversion efficiencies of the gas turbine, respectively, taken as 35% and 45%; ΔP gin,max , △P gin,min These represent the upper and lower limits of the ramp speed for the gas turbine; N2) Gas-fired boiler model: Q Gout,t =η G2Q P Gin,t (26) △P Gin,min ≤P Gin,t+1 -P Gin,t ≤△P Gin,max (28) In the formula, P Gin,t Q Gout,t These represent the gas input and heat output power values ​​of the gas-fired boiler during time period t, respectively. η is the upper limit input value for the gas-fired boiler. G2Q The thermal conversion efficiency of the gas turbine is taken as 85%; ΔP Gin,max , △P Gin,min These are the upper and lower limits of the ramp-up speed for gas-fired boilers; S1-4: Construct a combined refrigeration unit model; A combined cooling system using an electric chiller and a lithium bromide absorption chiller is employed. The electric chiller directly utilizes electricity from the Flexible Regional Integrated Energy System (RIES) power system to drive the compressor for cooling. The lithium bromide absorption chiller, on the other hand, leverages the high-temperature flue gas and waste heat from the gas turbine and gas boiler to perform work, achieving the reuse of waste heat and promoting energy efficiency, thus facilitating the economical operation of the RIES. The combined chiller unit models are shown below: P1) Electric Refrigeration Unit Model: P.S P2Cout,t Hη P2C P.S P2Cin,t (29) △P P2Cin,min ≤P P2Cin,t+1 -P P2Cin,t ≤△P P2Cin,max (31) In the formula, P P2Cin,t P P2Cout,t These represent the electrical input and cooling output power values ​​of the electric chiller during time period t, respectively. η is the upper limit input value for the electric chiller. P2C The refrigeration conversion efficiency of the electric chiller is taken as 90%; △P P2Cin,max , △P P2Cin,min These represent the upper and lower limits of the ramp rate for electric chillers; P2) Lithium bromide absorption chiller model: Q Lin,t =the R (or gloss Q gout,t +n Gloss Q Gout,t ) (32) Q Lout,t =η L Q Lin,t (33) △Q Lin,min ≤Q Lin,t+1 -Q Lin,t ≤△Q Lin,max In equation (35), Q Lin,t Q Lout,t These represent the heat input and cold output power values ​​of the lithium bromide absorption chiller during time period t, respectively. For lithium bromide absorption chillers, the upper limit input value is η. gloss η Gloss η R η L The heat loss coefficients of recoverable heat from gas turbines, gas boilers, recovery efficiency of recovery devices, and conversion efficiency of lithium bromide absorption chillers are respectively taken as 20%, 15%, 60%, and 80%; △Q Lin,max , △Q Lin,min These represent the upper and lower limits of the ramp rate for lithium bromide absorption chillers.

3. The flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 2, characterized in that, Step S2 specifically includes: S2-1: Define the objective function; The optimization scheduling model aims to minimize the sum of the cost of RIES purchasing energy from the upper-level network and the cost of RIES power system network line losses, i.e., including the cost C of the system purchasing energy from the upper-level network. buy And system power network loss cost C loss Two main parts: minF=(C buy +C loss ) (36) In the formula, the system purchases energy from the superior network at cost C. buy The system power network loss cost includes two parts: the cost of purchasing electricity from the upstream power grid and the cost of purchasing gas from the upstream gas grid. loss The cost includes two parts: the system power network line loss cost and the operating loss cost of the smart soft switch SOP itself, which are described in detail below: Q1) Energy purchase cost: P ebuy,t =P P2Hin,t +P eload,t -P W,t -P PV,t -P g2Eout,t (38) P gbuy,t =P gin,t +P Gin,t +P gload,t -P H2Cout,t (39) In the formula, P ebuy,t P gbuy,t These represent the electricity and gas power purchased from the upstream network during time period t; f e f n These are the unit electricity price from the superior power grid and the unit gas price from the superior gas grid, respectively; P eload,t P gload,t P W,t P PV,t These represent the electrical load, gas load, wind turbine output power, and photovoltaic output power during time period t, respectively. Q2) System power network loss cost: In the formula, r ij I t,ij Δt and Δt represent the resistance of power system branch ij, the current amplitude of power system branch ij during time period t, and the duration of each time period, respectively, with the duration of each time period set to 1 hour; N N This represents the total number of nodes in the RIES power system. S2-2: Determine the constraints; Based on the actual system operation and considering the coupling relationship between the RIES supply side, conversion side, and load side, the RIES power balance constraints for electricity, gas, heat, and cooling are given. These constraints include six parts: power system operation constraints, electricity power balance constraints, gas power balance constraints, heat power balance constraints, cooling power balance constraints, and interaction constraints with the upper-level electricity and gas networks. The specific descriptions are as follows: E1) Power system operation constraints: Constraints (41) and (42) represent the active and reactive power balance of node i during time period t, respectively; constraints (43) and (44) represent the sum of active and reactive power injected into node i during time period t, respectively; constraint (45) represents Ohm's law on branch ij during time period t; the current magnitude of each branch during time period t can be determined by constraint (46); and in the formula, P t,ji Q t,ji P represents the active and reactive power on branch ij during time period t, respectively; t,i Q t,i Let x represent the total active and reactive power injected into node i during time period t, respectively; ij Let be the reactance of branch ij in the power system; These represent the active power injected into node i during time period t, specifically the power from the wind turbine, photovoltaic system, and gas turbine. These represent the active power consumed by the electrolytic cell, the active power consumed by the electric chiller, the active power consumed by the electrical load, and the reactive power consumed by the electrical load at node i during time period t, respectively; U t,i U t,j These are the voltage amplitudes at nodes i and j during time period t, respectively. U、 These are the upper and lower limits of the node voltage amplitude and the upper limit of the branch current, respectively. E2) Power balance constraint: E3) Gas power balance constraint: P gbuy,t +P H2Cout,t -P gin,t -P Gin,t -P gload,t =0 (50) E4) Thermal power balance constraint: Q gout,t +Q Gout,t -Q hload,t =0 (51) E5) Cooling power balance constraint: P P2Cout,t +Q Lout,t -Q cload,t =0 (52) E6) Interaction constraints with the superior electrical network and superior gas network: In the formula, These represent the upper limits of interaction between RIES and the upstream power grid and gas network, respectively.

4. The flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 3, characterized in that, Step S3 specifically includes: Using linearization and second-order cone relaxation techniques, the original model is transformed into a mixed-integer second-order cone programming model. Linearization is achieved through variable substitution, i.e., letting v... t,i and l t,ij They represent and The linearized constraints are expressed as follows: Since there are still quadratic terms, the current constraint (46) is still nonlinear, so it is relaxed to the following second-order cone constraint: ||[2P t,ij 2Q t,ij l t,ij -v t,i ] T ||2≤v t,i +l t,ij (61) In addition, the operating constraints of the intelligent soft switch SOP are all quadratic nonlinear constraints, so they are converted into the following rotational second-order cone constraints:

5. The flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 4, characterized in that, The solution process for the model established in step S4 is as follows: S4-1: Solver tools; The program was written in the MATLAB R2018b software platform with the YALMIP optimization toolbox, and the optimization calculation was solved by calling the IBMILOGCPLEX 12.6 algorithm package. The optimization calculation was performed on a PC with an i7-8700 CPU@3.20GHz processor and 8GB RAM, and the software environment was Windows 10 operating system. S4-2: Solution process; Based on the standard IEEE 33-bus system, wind turbines, photovoltaics, P2G, electric refrigeration, and gas turbine equipment coupled with the power grid are connected together as a test system to verify the effectiveness of the proposed scheduling model in improving the efficiency of multi-energy coupling utilization and improving the economic operation of the system. To facilitate the local consumption of electricity generated by renewable clean energy by P2G equipment, two wind turbine generators and four photovoltaic power stations were connected to the power system. All wind turbines and photovoltaic units operated at unity power factor, and the local reactive power support of wind turbines and photovoltaic units was not considered. The daily operating curves of wind turbines, photovoltaic units, and various loads were obtained by prediction using hourly intervals throughout the day. Two sets of 500kVA smart soft-switching SOPs were installed between the two pairs of nodes, with a reactive power limit of 400kVar. It was assumed that the loss factor of each smart soft-switching SOP converter was 0.

02.

6. The flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 5, characterized in that, In step S5, the analysis process of the optimization results of the established model is as follows: S5-1: Set up an optimized scheduling scheme; To clearly compare the effectiveness of the constructed optimized scheduling model in optimizing the multi-energy coupled operation of the system, the following four optimized scheduling schemes are set up: F1) Combined power supply based on electric refrigeration, gas boiler heating, gas turbine cogeneration, wind turbine and photovoltaic power generation, power supply from the upper-level power grid, and gas supply from the upper-level gas grid. F2) Based on Scheme 1, add a smart soft switch SOP; F3) Based on Scheme 2, add an electro-gas P2G device; F4) Based on scheme 3, a lithium bromide absorption chiller is added; S5-2: Determine the optimization indicators; Based on the considered quantitative analysis indicators, the power network line losses, the cost of purchasing energy from the upstream power grid and gas grid, and the total system operating cost are analyzed under different schemes; the specific optimization indicators are as follows: D1) Power network line losses; D2) Per-unit deviation of node voltage; D3) Cost of purchasing energy from the superior electricity and gas grid; D4) Total system operating cost; D5) Comprehensive utilization rate of photovoltaic and wind turbines.

7. A system for implementing the flexible regional integrated energy system combined cooling, heating, gas, and power dispatching method as described in claim 1, characterized in that, The system includes, in sequence, modules for constructing and modeling mathematical models under the RIES architecture of a flexible regional integrated energy system with intelligent soft switching; modules for establishing RIES optimal scheduling models and RIES power balance constraints (electricity, gas, heat, and cooling); modules for transforming mixed-integer second-order cone programming models; optimization solution modules; intelligent soft switching SOPs; and modules for analyzing the economic benefits of electricity-to-gas (P2G) technology and lithium bromide absorption chillers. The mathematical model construction and modeling module of the flexible regional integrated energy system RIES architecture with intelligent soft switching is used to construct the mathematical model of the flexible regional integrated energy system RIES architecture with intelligent soft switching SOP, and gradually introduce the two-stage operation of electric to gas P2G and the combined refrigeration technology of electric chiller and lithium bromide absorption chiller, and model them respectively. The module for establishing the RIES optimal scheduling model and the RIES power balance constraint relationship is as follows: The RIES optimal scheduling model is established with the sum of the power system network loss and the cost of purchasing energy from the upper network in the RIES as the objective function, and the RIES power balance constraint relationship is given. The mixed-integer second-order cone programming model transformation module uses linearization and second-order cone relaxation techniques to transform the original model into a mixed-integer second-order cone programming model. The optimization solution module uses a 24-hour scheduling cycle within a day. Based on the predicted operating curves of wind turbines, photovoltaics, and various loads, it uses the already built model to call the Cplex algorithm package on Matlab equipped with the Yalmip optimization toolbox for optimization solution. The module analyzing the economic benefits of intelligent soft-switching SOP, P2G technology, and lithium bromide absorption chiller uses a modified IEEE 33-node example to analyze the economic benefits of these technologies in reducing system network losses and costs, and improving the system's ability to absorb wind and solar power.

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