Park load optimization scheduling method and device
By considering carbon emissions in an integrated energy system, using electric heating loads and distributed power forecast data to optimize scheduling, the problem of ignoring environmental costs in the existing technology is solved, and the improvement of energy utilization efficiency and carbon emission reduction is achieved.
Patent Information
- Application Number
- CN202411601654.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-04-04
AI Technical Summary
The existing comprehensive energy system optimization scheduling mainly aims to minimize system operation costs, ignoring the environmental costs during IES operation, resulting in the inability to effectively optimize the park load.
A park load optimization scheduling method is proposed. By obtaining the electric and thermal load prediction data of the comprehensive energy system and the distributed power output prediction data, substituting the joint optimization scheduling model of the comprehensive energy system that takes into account carbon emissions, solving the optimization results and formulating a flexible load optimization scheduling plan.
It has achieved effective reduction of carbon emissions and improved energy utilization efficiency, significantly improved the economy of the integrated energy system, reduced load peak-to-valley difference, optimized energy utilization efficiency, and improved the utilization rate of renewable energy.
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Figure CN119761671A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of load interaction technology, and in particular to a park load optimization scheduling method and device. Background Art
[0002] Energy is the foundation of human survival and development, and is also a key element of industrial production and residents' lives. With the continuous expansion of energy consumption and increasingly severe environmental problems, optimizing the energy structure has become an important social issue. The Integrated Energy System (IES) is of great significance in promoting energy structure transformation and energy revolution, and will also play a key role in promoting energy conservation and emission reduction and promoting energy system reform.
[0003] At present, the Energy Hub (EH) model has been proposed and has been widely used in related research on integrated energy systems. With the continuous development of energy utilization technology, energy management on the load side has gradually become a research hotspot. Some research has been carried out on the participation of flexible loads in the optimal dispatching of power systems. In the existing technology, the energy hub dispatching model considering demand response studies the transferability of thermal loads and electrical loads, and points out that thermal loads have greater flexibility than electrical loads in demand response. Further research has established an optimal operation model of a multi-energy system including a multi-energy carrier production and transmission system and an interconnected smart energy hub (Smart Energy Hub, SEH). By minimizing the cost of energy supply and maximizing user satisfaction, the energy consumption of end users in the park is optimized.
[0004] However, the existing IES optimization scheduling mainly aims at minimizing the system operation cost, but largely ignores the environmental cost during the IES operation process. Therefore, it is urgent to establish a more sophisticated scheduling model to solve the park load scheduling problem. Summary of the invention
[0005] In order to overcome the above-mentioned defects, the present invention proposes a park load optimization scheduling method and device.
[0006] In a first aspect, a park load optimization scheduling method is provided, the park load optimization scheduling method comprising:
[0007] Obtain the electric and thermal load forecast data and distributed power output forecast data of the integrated energy system;
[0008] Substituting the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solving it to obtain an optimization result;
[0009] Based on the optimization results, an optimal scheduling scheme for flexible loads in the park in the integrated energy system is obtained;
[0010] The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the integrated energy system in the park.
[0011] Preferably, the controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat absorption and release power of heat storage tank, and battery charging and discharging power.
[0012] Preferably, the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the goal of minimizing the total operating cost and its corresponding constraints.
[0013] Furthermore, the objective function is as follows:
[0014] f EH =F DG +F net +F MT +F GB +F HST +F BAT +F L +f CO2
[0015] In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, F L Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
[0016] Furthermore, the total compensation costs for the distributed power generation operating costs, power grid electricity purchase costs, gas boiler operating costs, thermal storage tank depreciation costs, battery depreciation costs, and electrical and thermal flexible load optimization are as follows:
[0017]
[0018]
[0019]
[0020]
[0021]
[0022] F L =F shift +F tran +F cut
[0023] In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the grid during period t, P GB (t) is the output of the gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, F shift 、F tran 、F cut They are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
[0024] Furthermore, the carbon trading costs are as follows:
[0025] f CO2 =C t (E out -E all )
[0026] In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
[0027] Furthermore, the total CO2 emissions are as follows:
[0028]
[0029] In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the energy use stage of the i-th energy equipment, P iContribute to the operation of the i-th energy device.
[0030] Furthermore, the compensation costs for the translation load, transferable load, and reducible load participating in the optimal dispatch are as follows:
[0031]
[0032]
[0033]
[0034] In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
[0035] Furthermore, the transferable load power in period t and the load transfer state coefficient in period t satisfy:
[0036]
[0037]
[0038] In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
[0039] Furthermore, the reducible electric load in period t after the scheduling and the electric load reduction state coefficient in period t satisfy:
[0040]
[0041]
[0042]
[0043]
[0044] In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
[0045] Furthermore, the constraints include: electric and thermal network constraints, upper and lower power limit constraints, battery constraints and heat storage tank constraints.
[0046] Furthermore, the electric heating network constraints are as follows:
[0047] P w (t)+P pv (t)+P net (t)+P MT (t) = L e +P BAT (t)
[0048] L e =L base +L shift +L tran +L cut
[0049] 0≤P pv (t)≤P pv,max
[0050] 0≤P w (t)≤P w,max
[0051] P net,min ≤P net (t)≤P net.max
[0052] 0≤P MT (t)≤P MT,max
[0053] P BAT,min ≤P BAT (t)≤P BAT,max
[0054] S min ≤S soc (t)≤S max
[0055] X t ·Y t =0
[0056]
[0057]
[0058] S0=S T
[0059] In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , P pv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the grid exchange power, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,max are the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
[0060] In a second aspect, a park load optimization scheduling device is provided, and the park load optimization scheduling device includes:
[0061] An acquisition module is used to obtain the electric and thermal load forecast data and the distributed power output forecast data of the integrated energy system;
[0062] The first analysis module is used to substitute the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solve it to obtain an optimization result;
[0063] A second analysis module is used to obtain an optimal scheduling scheme for flexible loads in a park in a comprehensive energy system based on the optimization result;
[0064] The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the integrated energy system in the park.
[0065] Preferably, the controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat absorption and release power of heat storage tank, and battery charging and discharging power.
[0066] Preferably, the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the goal of minimizing the total operating cost and its corresponding constraints.
[0067] Furthermore, the objective function is as follows:
[0068] f EH =F DG +F net +F MT +F GB +F HST +F BAT +F L +f CO2
[0069] In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, F L Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
[0070] Furthermore, the total compensation costs for the distributed power generation operating costs, power grid electricity purchase costs, gas boiler operating costs, thermal storage tank depreciation costs, battery depreciation costs, and electrical and thermal flexible load optimization are as follows:
[0071]
[0072]
[0073]
[0074]
[0075]
[0076] F L =F shift +F tran +F cut
[0077] In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the power grid during period t, P GB (t) is the output of gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, F shift 、F tran 、F cut They are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
[0078] Furthermore, the carbon trading costs are as follows:
[0079] f CO2 =C t (E out -E all )
[0080] In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
[0081] Furthermore, the total CO2 emissions are as follows:
[0082]
[0083] In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the energy use stage of the i-th energy equipment, Pi Contribute to the operation of the i-th energy device.
[0084] Furthermore, the compensation costs for the translation load, transferable load, and reducible load participating in the optimal dispatch are as follows:
[0085]
[0086]
[0087]
[0088] In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
[0089] Furthermore, the transferable load power in period t and the load transfer state coefficient in period t satisfy:
[0090]
[0091]
[0092] In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
[0093] Furthermore, the reducible electric load in period t after the scheduling and the electric load reduction state coefficient in period t satisfy:
[0094]
[0095]
[0096]
[0097]
[0098] In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
[0099] Furthermore, the constraints include: electric and thermal network constraints, upper and lower power limit constraints, battery constraints and heat storage tank constraints.
[0100] Furthermore, the electric heating network constraints are as follows:
[0101] P w (t)+P pv (t)+P net (t)+P MT (t) = L e +P BAT (t)
[0102] L e =L base +L shift +L tran +L cut
[0103] 0≤P pv (t)≤P pv,max
[0104] 0≤P w (t)≤P w,max
[0105] P net,min ≤P net (t)≤P net.max
[0106] 0≤P MT (t)≤P MT,max
[0107] P BAT,min ≤P BAT (t)≤P BAT,max
[0108] S min ≤S soc (t)≤Smax
[0109] X t ·Y t =0
[0110]
[0111]
[0112] S0=S T
[0113] In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , P pv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the grid exchange power, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,max are the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
[0114] In a third aspect, a computer device is provided, comprising: one or more processors;
[0115] The processor is used to store one or more programs;
[0116] When the one or more programs are executed by the one or more processors, the park load optimization scheduling method is implemented.
[0117] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the park load optimization scheduling method is implemented.
[0118] The above one or more technical solutions of the present invention have at least one or more of the following beneficial effects:
[0119] The present invention provides a method and device for optimizing and scheduling loads in a park, comprising: obtaining electric and thermal load forecast data and distributed power output forecast data of an integrated energy system; substituting the electric and thermal load forecast data and the distributed power output forecast data into a combined supply and demand day-ahead optimization scheduling model of an integrated energy system taking carbon emissions into consideration and solving the model to obtain an optimization result; based on the optimization result, obtaining an optimal scheduling scheme for flexible loads in the park in the integrated energy system; wherein the optimization result includes: controllable unit power, shiftable load, transferable load, and reducible electric load in the integrated energy system in the park. The technical solution provided by the present invention takes carbon emissions into consideration and can effectively reduce carbon emissions and improve energy efficiency. By comprehensively considering the characteristics of flexible electric and thermal loads that can be translated, transferred, and reduced, a more sophisticated scheduling model is established, which can significantly improve the economy of the integrated energy system, reduce the peak-to-valley difference in loads, and thus optimize energy efficiency. Simulation analysis shows that the multi-type flexible loads in the present invention can effectively achieve load peak shaving and peak shifting, significantly optimize the electric and thermal load curves, and not only reduce the system operating costs, but also effectively alleviate the contradiction between energy supply and demand, and improve the economy and environmental benefits of the integrated energy system. In addition, the reasonable scheduling of flexible loads also greatly improves the utilization rate of renewable energy, achieving the dual optimization goals of economic scheduling and environmental protection of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS
[0120] Figure 1 It is a schematic flow chart of the main steps of the park load optimization scheduling method according to an embodiment of the present invention;
[0121] Figure 2 This is a diagram showing the distribution of flexible electrical and thermal loads on the user side before optimization according to an embodiment of the present invention;
[0122] Figure 3 This is a curve diagram after optimization of electrical and thermal loads in scenario 1 of an embodiment of the present invention;
[0123] Figure 4 This is a curve diagram after optimization of electrical and thermal loads in scenario 2 of an embodiment of the present invention;
[0124] Figure 5 This is a curve diagram after optimization of electrical and thermal loads in scenario 3 of an embodiment of the present invention. DETAILED DESCRIPTION
[0125] The specific implementation modes of the present invention will be further described in detail below in conjunction with the accompanying drawings.
[0126] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0127] As disclosed in the background technology, energy is the foundation of human survival and development, and is also a key element of industrial production and residents' lives. With the continuous expansion of energy consumption, environmental problems are becoming increasingly severe, and optimizing the energy structure has become an important issue in society. The Integrated Energy System (IES) is of great significance in promoting the transformation of my country's energy structure and energy revolution, and will also play a key role in promoting energy conservation and emission reduction and promoting energy system reform.
[0128] The Energy Hub (EH) model has been proposed and has been widely used in related research on integrated energy systems. With the continuous development of energy utilization technology, energy management on the load side has gradually become a research hotspot. Some research has been carried out on the participation of flexible loads in the optimal dispatching of power systems. In the existing technology, the energy hub dispatching model considering demand response studies the transferability of thermal loads and electrical loads, and points out that thermal loads have greater flexibility than electrical loads in demand response. Further research has established an optimal operation model of a multi-energy system that includes a multi-energy carrier production and transmission system and an interconnected smart energy hub (SEH), which achieves the optimization of energy consumption for end users by minimizing energy supply costs and maximizing user satisfaction.
[0129] However, the existing IES optimization scheduling mainly aims at minimizing the system operation cost, but largely ignores the environmental cost during IES operation.
[0130] In order to improve the above-mentioned problems, the present invention provides a method and device for optimizing and scheduling loads in a park, including: obtaining electric and thermal load forecast data and distributed power source output forecast data of an integrated energy system; substituting the electric and thermal load forecast data and the distributed power source output forecast data into a combined supply and demand day-ahead optimization scheduling model of an integrated energy system considering carbon emissions and solving the model to obtain an optimization result; based on the optimization result, obtaining an optimal scheduling scheme for flexible loads in the park in the integrated energy system; wherein the optimization result includes: controllable unit power, shiftable load, transferable load, and reducible electric load in the integrated energy system in the park. The technical solution provided by the present invention takes carbon emissions into consideration and can effectively reduce carbon emissions and improve energy efficiency. By comprehensively considering the characteristics of flexible electric and thermal loads that can be translated, transferred, and reduced, a more sophisticated scheduling model is established, which can significantly improve the economy of the integrated energy system, reduce the peak-to-valley difference in loads, and thus optimize energy efficiency. Simulation analysis shows that the multi-type flexible loads in the present invention can effectively achieve load peak shaving and peak shifting, significantly optimize the electric and thermal load curves, and not only reduce the system operating costs, but also effectively alleviate the contradiction between energy supply and demand, and improve the economy and environmental benefits of the integrated energy system. In addition, the reasonable scheduling of flexible loads also greatly improves the utilization rate of renewable energy, achieving the dual optimization goals of economic scheduling and environmental protection of the integrated energy system.
[0131] The above scheme is described in detail below.
[0132] Example 1
[0133] See attached Figure 1 , Figure 1 FIG. 1 is a flow chart showing the main steps of a method for optimizing and scheduling loads in a park according to an embodiment of the present invention. Figure 1 As shown, the park load optimization scheduling method in the embodiment of the present invention mainly includes the following steps:
[0134] Step S101: Acquire electric and thermal load forecast data and distributed power output forecast data of the integrated energy system;
[0135] Step S102: Substituting the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solving it to obtain an optimization result;
[0136] Step S103: Based on the optimization result, an optimization scheduling scheme for the park flexible load in the integrated energy system is obtained;
[0137] The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the integrated energy system in the park.
[0138] In one embodiment, the integrated energy system is an integrated energy system based on an energy hub, and the energy input sources include grid electricity, distributed power output and natural gas. The energy conversion device includes a transformer and a gas turbine to achieve the conversion of electrical energy and thermal energy; the storage component includes a battery and a heat storage tank for the storage and allocation of electrical energy and thermal energy.
[0139] Taking into account the characteristics of uncontrollable loads and controllable flexible loads, the power output is ensured to be completed by the transformer and the gas turbine. Therefore, the user load is divided into four categories: basic load, shiftable load, reducible load, and transferable load. The heat output is provided by the gas turbine heat recovery system and the gas boiler.
[0140] Shiftable load: The load power supply time can be changed according to the plan, the load needs to be shifted as a whole, and the power consumption time spans multiple scheduling periods; Transferable load: The power consumption in each time period can be flexibly adjusted, but the total load of the entire cycle after the transfer must remain unchanged from that before the transfer; Reducible load: The load can withstand certain interruptions or power reductions, and reduce the operating time, and can be partially or completely reduced according to supply and demand conditions.
[0141] In this embodiment, the controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat storage tank absorption and release power, and battery charging and discharging power.
[0142] In this embodiment, the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the goal of minimizing the total operating cost and its corresponding constraints.
[0143] In one embodiment, the objective function is as follows:
[0144] f EH =F DG +F net +F MT +F GB +F HST +F BAT +F L +f CO2
[0145] In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, FL Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
[0146] In one embodiment, the total compensation cost of the distributed power supply operation cost, the power purchase cost of the power grid, the operation cost of the gas boiler, the depreciation cost of the heat storage tank, the depreciation cost of the battery, and the optimization of the electric and thermal flexible loads is as follows:
[0147]
[0148]
[0149]
[0150]
[0151]
[0152] F L =F shift +F tran +F cut
[0153] In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the grid during period t, P GB (t) is the output of the gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, F shift 、F tran 、F cut They are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
[0154] In one embodiment, a trading mechanism is adopted that allows carbon emission rights to be bought and sold in the market, and the superior department distributes carbon emission quotas to each carbon emission source. If the emission of the carbon emission source is within the given quota, the excess quota can be sold in the carbon trading market; if the carbon emission exceeds the given quota, the excess must be purchased from the carbon trading market. Under the carbon trading system, in order to maximize economic benefits, each carbon emission enterprise will actively take effective measures to save energy and reduce emissions. The carbon trading costs are as follows:
[0155] f CO2 =C(E out -E all )
[0156] In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
[0157] In one embodiment, considering that carbon emissions in IES are generated in the production, transportation and use of various energy sources, the total CO2 emissions are as follows:
[0158]
[0159] In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the energy use stage of the i-th energy equipment, P i Contribute to the operation of the i-th energy device.
[0160] In one embodiment, the compensation fees for the translation load, transferable load, and curtailable load participating in the optimization scheduling are as follows:
[0161]
[0162]
[0163]
[0164] In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
[0165] In one embodiment, the transferable load power during period t and the load transfer state coefficient during period t satisfy:
[0166]
[0167]
[0168] In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
[0169] In one embodiment, the reducible electric load in period t after the scheduling and the electric load reduction state coefficient in period t satisfy:
[0170]
[0171]
[0172]
[0173]
[0174] In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
[0175] In one embodiment, the constraints include: electric and thermal network constraints, upper and lower power limit constraints, battery constraints and thermal storage tank constraints.
[0176] In one embodiment, the electric heating network constraints are as follows:
[0177] P w (t)+P pv (t)+P net (t)+P MT (t) = L e +P BAT (t)
[0178] L e =L base +L shift +L tran +L cut
[0179] 0≤P pv (t)≤P pv,max
[0180] 0≤P w (t)≤P w,max
[0181] P net,min ≤P net (t)≤P net.max
[0182] 0≤P MT (t)≤P MT,max
[0183] P BAT,min ≤P BAT (t)≤P BAT,max
[0184] S min ≤S soc (t)≤S max
[0185] X t ·Y t =0
[0186]
[0187]
[0188] S0=S T
[0189] In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , Ppv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the grid exchange power, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,max are the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
[0190] In a specific implementation, according to the time-of-use electricity price mechanism in a certain area, the park adopts the "peak-valley-flat-peak" electricity price strategy (see Table 1). Natural gas prices are priced in tiers. Each tier of gas volume is subject to a progressive surcharge for excess. The first tier gas price is 3 yuan per cubic meter; the second tier gas price is 1.1 times the price of the first tier, that is, 3.30 yuan per cubic meter; the third tier gas price is 1.4 times the price of the first tier, that is, 4.20 yuan per cubic meter (see Table 2). The tiered gas volume of residents is measured on an annual cycle. When the cumulative gas consumption in the year reaches the critical point of the tiered gas volume base, the tiered price increase will be implemented. It will not be accumulated or carried forward between annual cycles.
[0191] Table 1
[0192]
[0193] Table 2
[0194]
[0195]
[0196] The electric load on the user side includes basic electric load, movable electric load 1 and movable electric load 2, transferable electric load and reducible electric load; the thermal load includes basic thermal load, transferable thermal load and reducible thermal load. The distribution of each flexible electric and thermal load before optimization is as follows: Figure 2 As shown;
[0197] In order to verify the impact of flexible load on the optimal scheduling of the integrated energy system, this paper sets up three scenarios for comparative analysis:
[0198] Scenario 1: In this scenario, flexible electric loads that can be translated, transferred, and reduced, as well as flexible thermal loads that can be translated and reduced, are comprehensively considered, and the flexible loads fully participate in the system optimization and scheduling. The curves of the optimized electric and thermal loads are shown in the figure below: Figure 3 shown.
[0199] Scenario 2: In this scenario, only the flexible electric load that can be translated, transferred, and reduced is considered to participate in the system optimization scheduling, while the participation of the flexible thermal load is not considered. The curves of the optimized electric and thermal loads are as follows: Figure 4 shown.
[0200] Scenario 3: In this scenario, no flexible load is considered to participate in the system optimization scheduling. The curves of electricity and heat load after optimization are as follows: Figure 5 shown.
[0201] Except for the different settings of the flexible load, other conditions of the three scenarios remain consistent to ensure the accuracy of the comparison results.
[0202] By comparison Figure 3 and Figure 3 ,4,5, the present invention reveals the optimization scheduling strategy and effect of flexible electric load. Specifically:
[0203] The dispatching of shiftable loads: Shiftable load 1 was shifted from the original 19:00 to 22:00 period to the 05:00 to 08:00 period; shiftable load 2 was shifted from the 11:00 to 13:00 period to the 15:00 to 17:00 period. Both types of shiftable loads were shifted from the peak period of electricity prices to the period of relatively low electricity prices, thereby optimizing the electricity cost. According to the data analysis in Table 3, the load duration of the shiftable load remained unchanged during the entire dispatching process, and the power consumption was equal to the original value, which had a significant peak-shaving and valley-filling effect.
[0204] Scheduling of transferable loads: The original 4 periods of transferable loads are split into 5 periods, and their power consumption duration and power are changed. Compared with transferable loads, transferable loads have more flexible power distribution characteristics during the scheduling process, which improves the flexibility and economic benefits of electric loads.
[0205] Load reduction: During the peak electricity price periods from 10:00 to 15:00 and from 18:00 to 21:00, the load is reduced, and the load reduction is concentrated in the periods with higher electricity prices. The scheduling of flexible thermal load is similar to that of electrical load, and the thermal load can be shifted from the evening peak period to the normal period, further playing a peak reduction role and reducing the overall thermal load level.
[0206] By comparison Figure 3 , Figure 4 and Figure 5The following conclusions can be drawn from the analysis: In scenario 3, since there is no flexible load involved in the scheduling, the electric load curve is not effectively optimized during the peak period; while in scenarios 1 and 2, the flexible electric load participates in the scheduling, and the electric load curve shows the phenomenon of power consumption reduction during the peak period and load period adjustment, and the peak-to-valley difference of the electric load is reduced from 229.17kW to 168.43kW. In addition, there is no flexible thermal load involved in the scheduling in scenario 2, so the thermal load curves of scenarios 1 and 3 basically overlap; in contrast, the participation of flexible thermal loads in scenario 1 effectively achieves peak shaving and valley filling, and the peak-to-valley difference of the thermal load is reduced from 65.47kW to 34.57kW. This result shows that the present invention significantly reduces the peak-to-valley difference of the load of the power and thermal systems through the scheduling of flexible electric and thermal loads, and optimizes the overall operating efficiency of the system.
[0207] Example 2
[0208] Based on the same inventive concept, the present invention also provides a park load optimization scheduling device, the park load optimization scheduling device comprising:
[0209] An acquisition module is used to obtain the electric and thermal load forecast data and the distributed power output forecast data of the integrated energy system;
[0210] The first analysis module is used to substitute the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solve it to obtain an optimization result;
[0211] A second analysis module is used to obtain an optimal scheduling scheme for flexible loads in a park in a comprehensive energy system based on the optimization result;
[0212] The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the integrated energy system in the park.
[0213] Preferably, the controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat absorption and release power of heat storage tank, and battery charging and discharging power.
[0214] Preferably, the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the goal of minimizing the total operating cost and its corresponding constraints.
[0215] Furthermore, the objective function is as follows:
[0216] f EH =F DG +F net +F MT +F GB +FHST +F BAT +F L +f CO2
[0217] In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, F L Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
[0218] Furthermore, the total compensation costs for the distributed power generation operating costs, power grid electricity purchase costs, gas boiler operating costs, thermal storage tank depreciation costs, battery depreciation costs, and electrical and thermal flexible load optimization are as follows:
[0219]
[0220]
[0221]
[0222] F L =F shift +F tran +F cut
[0223] In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the grid during period t, P GB (t) is the output of the gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, F shift 、F tran 、F cutThey are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
[0224] Furthermore, the carbon trading costs are as follows:
[0225] f CO2 =C t (E out -E all )
[0226] In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
[0227] Furthermore, the total CO2 emissions are as follows:
[0228]
[0229] In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the energy use stage of the i-th energy equipment, P i Contribute to the operation of the i-th energy device.
[0230] Furthermore, the compensation costs for the translation load, transferable load, and reducible load participating in the optimal dispatch are as follows:
[0231]
[0232]
[0233]
[0234] In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
[0235] Furthermore, the transferable load power in period t and the load transfer state coefficient in period t satisfy:
[0236]
[0237]
[0238] In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
[0239] Furthermore, the reducible electric load in period t after the scheduling and the electric load reduction state coefficient in period t satisfy:
[0240]
[0241]
[0242]
[0243]
[0244] In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
[0245] Furthermore, the constraints include: electric and thermal network constraints, upper and lower power limit constraints, battery constraints and heat storage tank constraints.
[0246] Furthermore, the electric heating network constraints are as follows:
[0247] P w (t)+P pv (t)+P net (t)+P MT (t) = L e +P BAT (t)
[0248] L e =L base +L shift +L tran +L cut
[0249] 0≤P pv (t)≤P pv,max
[0250] 0≤P w (t)≤P w,max
[0251] P net,min ≤P net (t)≤P net.max
[0252] 0≤P MT (t)≤P MT,max
[0253] P BAT,min ≤P BAT (t)≤P BAT,max
[0254] S min ≤S soc (t)≤S max
[0255] X t ·Y t =0
[0256]
[0257]
[0258] S0=S T
[0259] In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , P pv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the grid exchange power, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,maxare the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
[0260] Example 3
[0261] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function, so as to implement the steps of a park load optimization scheduling method in the above embodiment.
[0262] Example 4
[0263] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of a park load optimization scheduling method in the above embodiment.
[0264] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0265] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0266] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.
[0267] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0268] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for optimizing park load scheduling, characterized in that: The method comprises: Obtain the electric and thermal load forecast data and distributed power output forecast data of the integrated energy system; Substituting the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solving it to obtain an optimization result; Based on the optimization results, an optimal scheduling scheme for flexible loads in the park in the integrated energy system is obtained; The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the park.
2. The method according to claim 1, characterized in that The controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat storage tank heat absorption and release power, and battery charging and discharging power.
3. The method according to claim 1, characterized in that The integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the minimum total operating cost as the goal and its corresponding constraints.
4. The method according to claim 3, characterized in that The objective function is as follows: minF EH =F DG +F net +F MT +F GB +F HST +F BAT +F L +f CO2 In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, F L Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
5. The method according to claim 4, characterized in that The total compensation costs for the distributed power generation operating costs, power grid purchase costs, gas boiler operating costs, thermal storage tank depreciation costs, battery depreciation costs, and electrical and thermal flexible load optimization are as follows: F L =F shift +F tran +F cut In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the grid during period t, P GB (t) is the output of the gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, F shift 、F tran 、F cut They are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
6. The method according to claim 4, characterized in that The carbon trading costs are as follows: f CO2 =C t (E out -E all ) In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
7. The method according to claim 6, characterized in that The total CO2 emissions are as follows: In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the i-th energy equipment corresponding to the energy use stage, P i Contribute to the operation of the i-th energy device.
8. The method according to claim 5, characterized in that The compensation costs for the translation load, transferable load, and curtailable load participating in the optimal dispatch are as follows: In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
9. The method according to claim 8, characterized in that The transferable load power during period t and the load transfer state coefficient during period t satisfy: In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
10. The method according to claim 8, characterized in that The reducible electric load in period t after the dispatch and the electric load reduction state coefficient in period t satisfy: In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
11. The method according to claim 3 or 5, characterized in that: The constraints include: electric and thermal network constraints, power upper and lower limit constraints, battery constraints and heat storage tank constraints.
12. The method according to claim 11, characterized in that The electric heating network constraints are as follows: P w (t)+P pv (t)+P net (t)+P MT (L)=L e +P BAT (L) L e =L base +L shift +L tran +L cut 0≤P pv (t)≤P pv,max 0≤P w (t)≤P w,max P net,min ≤P net (t)≤P net.max 0≤P MT (t)≤P MT,max P BAT,min ≤P BAT (t)≤P BAT,max S min ≤S soc (t)≤S max X t ·AND t =0 S0=S T In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , P pv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the power exchanged by the power grid, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,max are the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
13. A park load optimization scheduling device, characterized in that: The device comprises: An acquisition module is used to obtain the electric and thermal load forecast data and the distributed power output forecast data of the integrated energy system; The first analysis module is used to substitute the electric and thermal load forecast data and the distributed power output forecast data into the integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions and solve it to obtain an optimization result; A second analysis module is used to obtain an optimal scheduling scheme for flexible loads in a park in a comprehensive energy system based on the optimization result; The optimization results include: controllable unit power, shiftable load, transferable load, and reducible electrical load in the integrated energy system in the park.
14. The device according to claim 13, characterized in that The controllable unit power includes at least one of the following: gas turbine output, gas boiler output, wind turbine output, photovoltaic power station output, heat storage tank heat absorption and release power, and battery charging and discharging power.
15. The device according to claim 13, characterized in that The integrated energy system supply and demand joint day-ahead optimization scheduling model considering carbon emissions includes: an objective function with the minimum total operating cost as the goal and its corresponding constraints.
16. The device according to claim 15, characterized in that The objective function is as follows: minF EH =F DG +F net +F MT +F GB +F HST +F BAT +F L +f CO2 In the above formula, F EH is the objective function value, F DG is the operating cost of distributed generation, F net is the cost of purchasing electricity from the power grid, F MT is the operating cost of the gas turbine, F GB is the operating cost of the gas boiler, F HST is the depreciation cost of the heat storage tank, F BAT is the depreciation cost of the battery, F L Total compensation cost optimized for electrical and thermal flexible loads, f CO2 The cost of carbon trading.
17. The device according to claim 16, characterized in that The total compensation costs for the distributed power generation operating costs, power grid electricity purchase costs, gas boiler operating costs, thermal storage tank depreciation costs, battery depreciation costs, and electricity and thermal flexible load optimization are as follows: F L =F shift +F tran +F cut In the above formula, T is the total operation cycle, K w , K pv , K GB are the operating cost coefficients of wind turbines, photovoltaics, and gas boilers, respectively. b K is the time-of-use electricity price purchased from the power grid, HST , K BAT are the depreciation coefficients of the heat storage tank and battery, respectively, w (t), P pv (t) are wind turbine and photovoltaic output in period t, P net (t) is the electric power exchanged with the grid during period t, P GB (t) is the output of the gas boiler, P HST (t) is the heat absorption and release power of the heat storage tank during period t, P BAT (t) is the battery charging and discharging power during period t, f shift 、F tran 、F cut They are the compensation costs for movable loads, transferable loads and reducible loads participating in optimal scheduling.
18. The device according to claim 16, characterized in that The carbon trading costs are as follows: f CO2 =C t (E out -E all ) In the above formula, C t is the carbon trading market price on that day, E out is the total CO2 emissions, E all For carbon emission quotas.
19. The device according to claim 18, characterized in that The total CO2 emissions are as follows: In the above formula, Ω is the energy device set, c i pre is the carbon emission coefficient of the energy production and transportation stage corresponding to the i-th energy equipment, c i run is the carbon emission coefficient of the i-th energy equipment corresponding to the energy use stage, P i Contribute to the operation of the i-th energy device.
20. The device according to claim 17, characterized in that The compensation costs for the translation load, transferable load, and curtailable load participating in the optimal dispatch are as follows: In the above formula, is the compensation price for unit power load shift, is the sum of the power consumption of the shifted load power distribution vector after dispatching, t sh+ is the end period of load translation, t D is the duration, t sh- is the load shift start period, α t is the load translation state coefficient for period t, is the compensation price per unit power load transfer, t tr+ is the end period of load transfer, t tr- is the load transfer start period, β t is the load transfer state coefficient during period t, is the load power that can be transferred during period t, is the compensation price per unit power load transfer, γ t is the load reduction state coefficient during period t, is the electric load that can be reduced in period t after participating in the dispatch, The electric load that can be reduced during period t when not participating in the scheduling.
21. The device according to claim 20, characterized in that The transferable load power during period t and the load transfer state coefficient during period t satisfy: In the above formula, are the minimum and maximum values of the transferable load power, is the minimum continuous running time, β t-1 is the load transfer state coefficient in period t-1.
22. The device according to claim 20, characterized in that The reducible electric load in period t after the dispatch and the electric load reduction state coefficient in period t satisfy: In the above formula, θ t is the load reduction factor in period t, is the minimum continuous reduction time, γ t-1 is the load reduction state coefficient in period t-1, The longest continuous reduction time, N max is the maximum number of reductions.
23. The device according to claim 15 or 17, characterized in that The constraints include: electric and thermal network constraints, power upper and lower limit constraints, battery constraints and heat storage tank constraints.
24. The device according to claim 23, characterized in that The electric heating network constraints are as follows: P w (t)+P pv (t)+P net (t)+P MT (t)=L e +P BAT (t) L e =L base +L shift +L tran +L cut 0≤P pv (t)≤P pv,max 0≤P w (t)≤P w,max P net,min ≤P net (t)≤P net.max 0≤P MT (t)≤P MT,max P BAT,min ≤P BAT (t)≤P BAT,max S min ≤S soc (t)≤S max X t ·AND t =0 S0=S T In the above formula, P MT (t) is the electric power output of the gas turbine during period t, L e is the total electrical load; L base is the base load in the integrated energy system, L shift , L tran , L cut They are respectively the load that can be translated, the load that can be transferred, and the load that can be reduced, P w,max , P pv,max are the predicted wind turbine and photovoltaic output, P net,min , P net.max are the minimum and maximum values of the power exchanged by the power grid, P MT,max is the rated electrical power of the gas turbine, P BAT,min , P BAT,max are the maximum power of battery discharge and charge, S soc (t) is the battery charge state during period t, S min , S max They are the upper and lower limits of the battery charge state, X t is the charging state during period t, Y t is the discharge state during period t, X t+1 , Y t+1 is the charge and discharge state of the battery during the t+1 period, N1 and N2 are the number of charge and discharge limit values of the battery, S0 and S T It is the initial and final charge state of the battery.
25. A computer device, characterized in that: include: one or more processors; The processor is configured to execute one or more programs; When the one or more programs are executed by the one or more processors, the park load optimization scheduling method as described in any one of claims 1 to 12 is implemented.
26. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the park load optimization scheduling method as described in any one of claims 1 to 12 is implemented.