A park integrated energy system scheduling method considering multi-cchp

Abstract

The application discloses a kind of park integrated energy system scheduling method considering multiple CCHP, comprising: the basic data of park integrated energy system is initialized;Establish integrated energy system energy supply, energy storage and conversion equipment model;Based on park supply and storage equipment model, construct integrated energy system combined operation multi-objective optimization model containing electricity, heat, cold, gas and GHG emission flow;ε constraint method is used to solve the park integrated energy system model;According to actual demand, a set of optimal scheduling solution is selected using fuzzy satisfaction method;Finally, the optimal scheduling method is sent to each subsystem in the park.The application establishes multiple CCHP park integrated energy system combined operation multi-objective optimization model, fully excavates the ability of different capacity CCHP in improving energy efficiency and reducing greenhouse gas emissions, realizes the appropriate trade-off of total cost minimization and greenhouse gas emission minimization of park system operation.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of multi-energy complementary utilization of integrated energy systems, and particularly relates to a park integrated energy system scheduling method considering multiple CCHPs. BACKGROUND

[0002] At present, integrated energy systems are considered as a solution to achieve carbon neutrality. This is because integrated energy systems can combine renewable energy, combined cooling heating and power (CCHP) systems, energy storage facilities and electrified transportation, etc., to achieve efficient and low-carbon utilization of energy. As a typical application of integrated energy systems at the user side, park integrated energy systems have achieved rapid development and application.

[0003] Combined cooling heating and power, as an important part of park integrated energy systems, has the biggest feature of cascade utilization of energy of different qualities, which not only improves the energy utilization efficiency, but also reduces the emission of carbon compounds and harmful gases. It plays a bridge role of combining electricity, heat, cold and gas in the integrated energy system, which can effectively coordinate multi-energy flow to reduce the operation cost and greenhouse gas emission of the park integrated energy system.

[0004] However, in the existing research on park integrated energy systems, most of the research only considers a single CCHP, and ignores the nonlinear coupling of power efficiency and greenhouse gas emission with the cooling and heating load ratio, resulting in that the potential of multiple different capacity combined cooling heating and power to significantly reduce greenhouse gas emission and improve energy efficiency is not fully developed.

[0005] Therefore, it is necessary to carry out research on the operation strategy of park integrated energy systems, and discuss the optimal operation of multiple different capacity combined cooling heating and power systems in park integrated energy systems, so as to solve the problems of low energy efficiency and high greenhouse gas emission of park integrated energy systems. SUMMARY

[0006] The purpose of the application is to provide a park integrated energy system scheduling method considering multiple CCHPs, which can improve the energy utilization efficiency of the park integrated energy system and reduce greenhouse gas emission by coordinating multiple CCHPs of different capacities.

[0007] To achieve the above purpose, the application adopts the following technical solutions:

[0008] A park integrated energy system scheduling method considering multiple CCHPs, comprising the following steps:

[0009] Step 1: Initialize data: initialize the basic data of the park integrated energy system;

[0010] Step 2: Establishing the models of energy supply, storage and conversion devices in the integrated energy system, especially the mathematical model representing the relationship between the power efficiency of CCHP and its load rate and greenhouse gas emissions;

[0011] Step 3: Building the multi-objective optimization model of combined operation of multi-CCHP integrated energy system in the park: based on the models of energy supply, storage and conversion devices in the park, a multi-objective optimization model of combined operation of multi-CCHP integrated energy system in the park is built, which includes electricity, heat, cold, gas and GHG emission flow;

[0012] Step 4: Solving the multi-objective optimization operation model by using the epsilon constraint method;

[0013] Step 5: Selecting a set of optimal scheduling solutions from the Pareto solution set by using the fuzzy satisfaction method: according to the actual demand, a set of optimal scheduling solutions is selected from the Pareto solution set by using the fuzzy satisfaction method, and the total cost and total GHG emissions of the integrated energy system in the park are calculated;

[0014] Step 6: Sending the optimal scheduling method to each subsystem in the park.

[0015] The energy supply devices in the multi-CCHP integrated energy system in the park include photovoltaic power supply, several combined cooling, heating and power and gas boilers; the energy conversion devices include absorption chillers and electric chillers; the energy storage devices include electric vehicles and heat storage devices.

[0016] The mathematical model representing the relationship between the power efficiency of CCHP and its load rate is:

[0017]

[0018] In the formula: and are the power efficiency and power of the i-th CCHP at time t, respectively. , , and are the constant, first and second emission coefficients of the efficiency function, respectively, wherein is a negative number.

[0019] The mathematical model representing the relationship between the greenhouse gas emissions of CCHP and its load rate is:

[0020]

[0021] In the formula: is the greenhouse gas emission of the i-th CCHP at time t; , , and are the constant, first and second emission coefficients of the emission function, respectively, wherein is a positive number.

[0022] The GHG emissions are targeted to minimize the operation cost and minimize the GHG emissions:

[0023]

[0024] wherein:

[0025]

[0026]

[0027] wherein: and are the total operation cost and the greenhouse gas emissions, respectively. T is the number of dispatches, is the dispatch period; , and are the unit grid electricity price, the natural gas price and the electricity selling price at time t, respectively; and are the unit start-up and shut-down price of the ith CCHP, and n is the number of CCHPs, and represent the start-up and shut-down of the unit, respectively; and are the electricity and natural gas purchased from the grid and the gas network at time t, respectively; is the electricity sold to the grid; is the total GHG emissions.

[0028] The constraint conditions of the multi-objective optimization model include the system heat / electricity / cooling energy supply / demand balance constraint, the load adjustment capacity and conversion efficiency constraint of energy supply, energy storage and conversion equipment, the multi-CCHP park integrated energy system combined operation constraint, and the energy exchange limit constraint with the grid and the gas network.

[0029] The multi-CCHP park integrated energy system combined operation constraint is:

[0030] The constraint that the binary variables controlling the switch and start-stop of the CCHP should satisfy is:

[0031]

[0032]

[0033]

[0034] wherein: / is a binary variable, which represents the running state of the ith CCHP at t / t-1, and equals to 1 when the unit is in the running state, otherwise in the shutdown state; and respectively represent the start and stop actions of the unit.

[0035] The ramping constraint of the CCHP is:

[0036]

[0037] In the formula: and respectively represent the up-ramping and down-ramping rates of the ith CCHP;

[0038] In order to avoid frequent start / stop, the minimum start / stop time constraint is:

[0039]

[0040]

[0041] In the formula: and are the minimum start time and stop time of the ith CCHP, respectively; and are the initial start time and stop time of the ith CCHP, respectively.

[0042] The solving of the multi-objective optimization operation model by using the ε constraint method is performed according to the following method:

[0043] The cost function is taken as the main objective function, and the emission function is converted into a constraint condition, which is solved together with other constraint conditions to obtain a Pareto solution set:

[0044]

[0045] The selection of a group of suitable Pareto solutions from the Pareto solution set by using the fuzzy satisfaction method is performed according to the following method:

[0046] The membership function is:

[0047]

[0048] The selection function is:

[0049]

[0050] In the formula: and are the maximum value and the minimum value before the Pareto optimization of the pth objective function; and respectively, the value of the lth solution of the Pareto optimal front of the pth objective function and its corresponding membership function; The value of the membership function varies between 0 and 1, representing the degree of success in minimizing the objective function p.

[0051] The present application has the following beneficial effects:

[0052] The multi-CCHP park integrated energy system combined operation multi-objective optimization model is constructed, the ability of different capacity CCHP in improving energy efficiency and reducing greenhouse gas emission is fully tapped, and the appropriate trade-off between the minimization of the total cost of the park integrated energy system operation and the minimization of greenhouse gas emission is realized. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 It is a flowchart of the park integrated energy system operation optimization method.

[0054] Figure 2 It is a framework of the multi-CCHP park integrated energy system.

[0055] Figure 3 It is a graph of the relationship between the electric power of the CCHP and the load rate.

[0056] Figure 4 It is a graph of the relationship between the electric power of the CCHP and the greenhouse gas emission. DETAILED DESCRIPTION

[0057] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the technical solutions of the present application are further described in detail below with reference to the drawings and specific embodiments.

[0058] The present application has the following advantages:

[0059] (1) The basic data of the park integrated energy system is initialized;

[0060] (2) The energy supply, storage and conversion equipment model of the integrated energy system is established, especially the mathematical model representing the relationship between the electric power efficiency of the CCHP and the greenhouse gas emission and the load rate thereof;

[0061] (3) Based on the park supply, use and storage equipment model, a multi-objective optimization operation model containing electric, heat, cold, gas and GHG emission flow is constructed;

[0062] (4) The epsilon constraint method is used to solve the multi-objective optimization operation model;

[0063] (5) According to the actual demand, a set of optimal scheduling solutions is selected from the Pareto solution set by using the fuzzy satisfaction method, and the total cost of the park integrated energy system operation and the total GHG emission are calculated;

[0064] (6) Finally, the optimal scheduling method is sent to each subsystem in the park, as shown in Fig. 8. Figure 1

[0065] The framework of the multi-CCHP integrated energy system of the park is shown in Fig. 1, wherein the energy supply equipment includes photovoltaic power (PV), several combined cooling heating and power (CCHP) and gas boilers (GB); the energy conversion equipment includes absorption chiller (AC) and electric chiller (EC); the energy storage equipment includes electric vehicles (EV) and thermal storage equipment (TES). Figure 2

[0066] The power generation efficiency and greenhouse gas emissions of CCHP have a nonlinear coupling relationship with its load rate and capacity. On the one hand, cogeneration has different power generation efficiency and greenhouse gas emissions under different loads; on the other hand, different capacity of cogeneration can achieve different power efficiency and greenhouse gas emissions, as shown in Figs. 3 and 4. Therefore, multiple cogeneration units with different capacities can be combined together to meet different loads while coordinating energy efficiency and greenhouse gas emissions.

[0067] The model of CCHP in the regional integrated energy system is:

[0068]

[0069]

[0070]

[0071]

[0072] In the formula: , and are the power efficiency, thermal efficiency and consumed natural gas of the i-th CCHP at time t; and are the power efficiency function and emission function of the i-th CCHP at time t, wherein , , and are the constants, first and second emission coefficients of the efficiency function, , , and are the constants, first and second emission coefficients of the emission function, is the conversion factor of 1kwh of electric energy into 1m 3 of natural gas; is the thermal efficiency of the waste heat recovery device in the CCHP.

[0073] The photovoltaic power generation model is: ​​

[0074]

[0075] In the formula: It refers to photovoltaic power generation capacity. Where S is the photovoltaic efficiency, S is the area of ​​the solar panel, and I is the solar radiation intensity. It refers to the ambient temperature.

[0076] The thermal storage system model is as follows:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula: yes Energy storage device at all times It is the energy of the heat storage device at time t. The energy loss coefficient of the heat storage device itself to the environment. and These refer to injection and extraction efficiencies, respectively. and These represent the heat injected / extracted from the heat storage device at time t. and These refer to the maximum heat injected and extracted by the thermal storage device. and These are the minimum and maximum energy ranges of the thermal storage device. and These represent the heat storage and heat release states of the thermal storage device, respectively.

[0083] The electric vehicle model is:

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090]

[0091] where: is the energy of the electric vehicle at time t+1, is the energy of the jth electric vehicle at time t, and are the charging / discharging power of the jth electric vehicle at time t, and are the charging / discharging efficiency of the electric vehicle, is the rated capacity of the jth electric vehicle, and are the maximum charging and discharging power of the electric vehicle, and are the binary variables controlling the charging and discharging actions, and are the minimum and maximum energy of the electric vehicle.

[0092] The boiler model is:

[0093]

[0094] where: and are the gas consumption and heat generation of the gas boiler at time t, is the conversion efficiency of the gas boiler, is the conversion factor from 1 kwh of electricity to 1 m 3 of natural gas.

[0095] A multi-objective optimization model for the combined operation of a multi-CCHP park integrated energy system containing electricity, heat, cold, gas, and GHG emission flows is constructed:

[0096]

[0097] where:

[0098]

[0099]

[0100] where: and are the total operating cost and greenhouse gas emissions, respectively. T is the number of dispatches, is the dispatching period; , and are the unit grid electricity price, natural gas price, and electricity selling price at time t, respectively; and are the unit start-up and shut-down prices of the ith CCHP, respectively, and n is the number of CCHPs, and are the start-up and shut-down of the unit, respectively; and are the electricity and natural gas purchased from the grid and natural gas network at time t, respectively; is the electricity sold to the grid; is the total GHG emission.

[0101] The energy balance constraints are:

[0102]

[0103]

[0104]

[0105]

[0106]

[0107] where: and are the electricity purchased from or sold to the grid at time t, respectively, is the PV power, is the electrical efficiency of the ith CCHP at time t, and are the charging / discharging power of the jth EV at time t, respectively, is the power of the electric chiller at time t, and n and m are the number of CCHPs and EVs, respectively; is the thermal efficiency of the ith CCHP at time t, is the heat power of the gas boiler at time t, and are the charging / discharging power of the jth EV at time t, respectively, is the heat power of the absorption chiller at time t, is the coefficient of performance of the electric chiller, is the coefficient of performance of the absorption chiller; , and are the electrical, thermal, and cooling loads, respectively; is the total GHG emission, and are the emission factors of the grid electricity and natural gas, is the GHG emission of the ith CCHP at time t, is the gas consumption of the gas boiler at time t.

[0108] The unit commitment constraints of the cogeneration system are:

[0109] The constraints that the binary variables controlling the switch and start-stop of the CCHP should satisfy are:

[0110]

[0111]

[0112]

[0113] wherein: / is a binary variable representing the running state of the ith CCHP at t / t-1, and equals 1 when the unit is in the on state, otherwise in the off state; and represent the start and stop actions of the unit, respectively.

[0114] The ramping constraints of the CCHP are:

[0115]

[0116] wherein: and are the electrical power of the ith CCHP at t and t-1, respectively, and represent the up-ramping and down-ramping rates of the ith CCHP, respectively.

[0117] The minimum start-stop time constraints to avoid frequent start-stop are:

[0118]

[0119]

[0120] wherein: and are the minimum start time and stop time of the ith CCHP, respectively; and are the initial start time and stop time of the ith CCHP, respectively.

[0121] The operation constraints of the conversion equipment are:

[0122]

[0123]

[0124]

[0125]

[0126]

[0127] wherein: is the power efficiency of the ith CCHP at time t, and are the maximum and minimum heat power of the CCHP, respectively; is the heat power of the gas boiler at time t, and are the maximum and minimum heat power of the gas boiler, respectively, and are the up and down ramp rates of the gas boiler, respectively; is the heat power of the absorption chiller at time t, and are the maximum and minimum heat power of the absorption chiller, respectively; is the power of the electric chiller at time t, and are the maximum and minimum heat power of the absorption chiller, respectively.

[0128] The multi-energy exchange constraints are:

[0129]

[0130]

[0131]

[0132] wherein: and are the power purchased or sold to the grid at time t, and are the maximum values of power purchased or sold to the grid. is the natural gas purchased from the natural gas network at time t, is the maximum value of natural gas purchased from the natural gas network.

[0133] According to the historical data of the thermal / electric / cold load demand, the scheduling period = 15, the scheduling times T = 96, the multi-objective optimization operation model is solved by using the ε constraint method:

[0134] The cost function is taken as the main objective function, the emission function is converted into a constraint condition, and the other constraint conditions are solved together to obtain the Pareto solution set.

[0135]

[0136] A set of suitable Pareto solutions is selected from the Pareto solution set by using the fuzzy satisfaction method. The membership function is defined as:

[0137]

[0138] The selection function is:

[0139]

[0140] In the formula: and are the maximum value and the minimum value of the Pareto optimal front of the pth objective function. and are the values of the lth solution of the Pareto optimal front of the pth objective function and the membership function corresponding to the pth objective function, respectively. The value of is between 0 and 1, indicating the success degree of minimizing the objective function p.

[0141] In summary, the present application proposes a park integrated energy system scheduling method considering multiple CCHPs, by establishing a mathematical model representing the relationship between the power efficiency of CCHP and greenhouse gas emission and its load rate and a multi-CCHP park integrated energy system combined operation multi-objective optimization model, the ability of different capacity CCHPs in improving energy efficiency and reducing greenhouse gas emission is fully tapped, and appropriate trade-off between minimizing the total cost of park system operation and minimizing greenhouse gas emission is realized.

[0142] It should be noted that the above is only a preferred embodiment of the present application, which is intended to illustrate the technical concept and characteristics of the present application, and cannot limit the protection scope of the present application. Any equivalent changes or modifications made in accordance with the spirit and essence of the present application should be covered within the protection scope of the present application.

Claims

1. A method for scheduling a comprehensive energy system in a park considering multiple CCHPs, characterized in that, Includes the following steps: Step 1: Initialize Data: Initialize the basic data of the park's integrated energy system; Step 2: Establish models of energy supply, storage, and conversion equipment in the integrated energy system, and develop a mathematical model to characterize the relationship between the power efficiency of CCHP and greenhouse gas emissions and its load factor. Step 3: Construct a multi-objective optimization model for the combined operation of multiple CCHP park integrated energy systems: Based on the park's power supply, consumption, transfer and storage equipment model, construct a multi-objective optimization model for the combined operation of multiple CCHP park integrated energy systems, including electricity, heat, cooling, gas and GHG emission streams; Step 4: Solve the multi-objective optimization model using the ε-constraint method; Step 5: Select an optimal scheduling solution from the Pareto solution set using the fuzzy satisfaction method: Select an optimal scheduling solution from the Pareto solution set using the fuzzy satisfaction method based on actual needs, and calculate the total operating cost and total GHG emissions of the park's integrated energy system; Step 6: Send the optimal scheduling method to every subsystem in the park; The mathematical model characterizing the relationship between the power efficiency and load factor of CCHP is as follows: ， In the formula: and These are the power efficiency and power of the i-th CCHP at time t, respectively. , ,and These are the constants of the efficiency function, and the primary and secondary emission coefficients, respectively. It is a negative number; The mathematical model characterizing the relationship between greenhouse gas emissions and load factor of CCHP is as follows: ， In the formula: It is the greenhouse gas emissions of the i-th CCHP at time t; , ,and These are the constants of the emission function, and the primary and secondary emission coefficients, respectively. It is a positive number; The GHG emissions are aimed at minimizing operating costs and minimizing GHG emissions. ， in: ， ， In the formula: and These are total operating costs and greenhouse gas emissions, respectively. T represents the number of scheduling attempts. The scheduling period; , and These are the unit grid electricity price, natural gas price, and electricity sales price at time t, respectively. and These are the unit start-up and stop prices for the i-th CCHP, respectively, where n is the number of CCHPs. and These respectively indicate the start-up and shutdown of the generator unit; and These represent the amount of electricity and natural gas the system purchases from the power grid and natural gas network at time t, respectively. It is the amount of electricity sold to the power grid; This is the total GHG emissions; The constraints of the multi-objective optimization model include system heat / electricity / cooling energy supply and demand balance constraints, load regulation capacity and conversion efficiency constraints of energy supply, energy storage and conversion equipment, combined operation constraints of multi-CCHP park integrated energy system, and energy exchange limitation constraints with the power grid and gas grid. The combined operational constraints of the multi-CCHP park integrated energy system are: The binary variables controlling the on / off state and start / stop of CCHP should satisfy the following constraints: ， ， ， In the formula: / It is a binary variable representing the operating state of the i-th CCHP at time t / t-1. When it is equal to 1, it means that the unit is in the power-on state; otherwise, it is in the power-off state. and These represent the start-up and shutdown actions of the generator unit, respectively. CCHP's ramping constraint is: ， In the formula: and Let represent the uphill and downhill climbing rates of the i-th CCHP, respectively; To avoid frequent starts / stops, the minimum start / stop time constraint is: ， ， In the formula: and These are the minimum start-up time and stop time of the i-th CCHP, respectively; and These are the initial start-up time and the stop time of the i-th CCHP, respectively.

2. The method for scheduling a comprehensive energy system in a park considering multiple CCHPs as described in claim 1, characterized in that, The energy supply equipment in the multi-CCHP park integrated energy system includes photovoltaic power sources, several combined cooling, heating and power (CCHP) units, and gas-fired boilers; energy conversion equipment includes absorption chillers and electric chillers; and energy storage equipment includes electric vehicles and thermal storage devices.

3. The method for scheduling a comprehensive energy system in a park considering multiple CCHPs as described in claim 1, characterized in that, The solution of the multi-objective optimization model using the ε-constraint method is performed as follows: Using the cost function as the primary objective function, the emission function is transformed into constraints, and together with other constraints, the Pareto solution set is solved. 。 4. The method for scheduling a comprehensive energy system in a park considering multiple CCHPs as described in claim 1, characterized in that, The selection of an optimal scheduling solution from the Pareto solution set using the fuzzy satisfaction method is performed as follows: The membership function is: ， The selection function is: ， In the formula: and These are the maximum and minimum values ​​of the p-th objective function before Pareto optimization; and The Pareto optimal frontiers of the p-th objective function and its corresponding membership function are respectively... The value of each solution; The value of varies between 0 and 1, representing the degree of success in minimizing the objective function p.

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