CN114372609A - Multi-source load complementary planning method considering new energy consumption cost optimization - Google Patents

Multi-source load complementary planning method considering new energy consumption cost optimization Download PDF

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CN114372609A
CN114372609A CN202111498622.8A CN202111498622A CN114372609A CN 114372609 A CN114372609 A CN 114372609A CN 202111498622 A CN202111498622 A CN 202111498622A CN 114372609 A CN114372609 A CN 114372609A
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power
cost
load
energy
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潘霄
张明理
赵琳
陈良
张娜
宋卓然
高靖
吕旭明
程孟增
吉星
商文颖
侯依昕
杨朔
杨博
刘禹彤
满林坤
徐熙林
杨海峰
杨方圆
刘凯
李金起
王宗元
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
State Grid Liaoning Electric Power Co Ltd
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STATE GRID LIAONING ECONOMIC TECHNIQUE INSTITUTE
State Grid Corp of China SGCC
State Grid Liaoning Electric Power Co Ltd
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Abstract

The invention discloses a multisource and load complementary planning method considering the optimal new energy consumption cost, which comprises the steps of establishing an upper-layer planning-lower-layer running multisource and load double-layer planning model considering the new energy consumption cost, establishing an energy concentrator structure model containing a multisource and load system, determining the coupling relation of equipment in the system, considering an electric power market clearing mechanism in the multisource and load system planning, and solving the problems that the economic cost and the environmental protection are difficult to balance in the current multisource and load dynamic price in the multisource and load system planning affect the economic benefit. The method can effectively reduce the total planning cost, promote the consumption of new energy with high proportion and improve the utilization efficiency of multiple energy sources of the system. Meanwhile, the emission of greenhouse gas CO2 is reduced, and the environmental value is realized. The method has the advantages of being scientific and reasonable, strong in applicability, good in effect, capable of improving the intelligent planning efficiency of the multi-source load system and reducing the environmental hazard generated by energy production and utilization.

Description

Multi-source load complementary planning method considering new energy consumption cost optimization
Technical Field
The invention belongs to the technical field of comprehensive energy system capacity optimization planning, and particularly relates to a multisource load complementary planning method considering new energy consumption cost optimization.
Background
In recent years, renewable energy sources such as wind, light and the like in China develop rapidly and occupy more and more important positions in a power grid. Because the installed capacity of renewable energy does not match with the power demand, the consumption space is not enough. The problem of wind and light abandonment caused by the influence of factors such as the limitation of an outgoing channel, the asynchronism of power supply construction and power grid construction, the imperfect market mechanism and the like is not well solved. In addition, the intermittence, the changeability and the randomness of the renewable energy power generation are not solved, and the grid-connected consumption difficulty is increased.
With the increase of energy demand forms and the pressure brought by environmental protection demands, the energy pattern is continuously developed from the traditional fossil energy to a novel pattern of balanced development of renewable energy and multi-source-load complementary utilization. The multi-source system has become a research hotspot as a small-sized comprehensive energy system integrating the functions of energy and load interconnection, conversion, coupling, storage and the like. MSLS is the basic unit and important component of IES, and it strengthens the cascade utilization of energy and the coupling conversion of multiple energy sources, and can effectively improve the utilization rate of energy, reduce the energy cost, and reduce the pollution to the environment. The coupling and complementary relations of various forms of energy sources can be fully considered by scientifically and reasonably planning renewable energy source equipment, a multi-energy conversion element, electricity storage, heat storage, gas storage and the like in the MSLS, so that the energy utilization efficiency is improved, and the social cost is reduced.
The energy price of MSLS varies over time according to energy market demand. However, in most cases, MSLS charges the consumer a fixed price, while price fluctuations are borne by the energy carrier. Since consumers are not affected by wholesale price changes, their demand appears to fluctuate dramatically, to be seasonal, and to be highly loaded during the day and less loaded at night. Price fluctuations reduce energy supply reliability and system efficiency and reduce energy operators' profits. In the existing research on dynamic pricing of energy prices, time-of-use electricity prices or real-time electricity prices are adopted for electricity prices, and analysis on fluctuation of the energy prices by applying a power market clearing mechanism in a planning scene is lacked. Meanwhile, besides renewable energy, the production of various forms of energy is one of the main emission sources of greenhouse gases. With the close attention to climate change, how to reasonably plan the equipment capacity of the MSLS is urgently needed to be researched, and the environmental protection is realized while the energy utilization efficiency is improved.
Disclosure of Invention
The invention aims to provide a multisource load complementary planning method with optimal new energy consumption cost, which effectively reduces the total planning cost, promotes the new energy consumption with high proportion and improves the multi-energy utilization efficiency of a system.
The technical scheme adopted by the invention is that the multisource load complementary planning method with optimal new energy consumption cost is taken into account, and the method is implemented according to the following steps:
step 1, constructing a double-layer planning model: the method comprises the steps that an upper-layer planning model is built according to annual net present value cost of a multi-energy system considering investment and operation cost of the multi-energy system, a lower-layer planning model is built according to operation net income containing new energy consumption cost and environment cost in the multi-energy system, planning capacity of various units obtained through planning is transmitted to the lower-layer operation model through the upper-layer planning model, the lower-layer planning model simulates the overall operation scheduling condition of the multi-energy system, a power market clearing mechanism is considered, and income is obtained and returned to the upper-layer planning model;
step 2, calculating the planning capacity of various units: constructing an annual net present value cost function and constraint conditions by taking the minimum annual net present value cost of the multi-energy system as a target according to the characteristics of an upper-layer planning participation main body and upper-layer planning source load equipment, and calculating planning capacity of various units;
step 3, calculating the lower-layer income: transferring the planning capacity of various units to a lower-layer operation model, combining a lower-layer planning participation subject and clearing profits of an electric power market, constructing an operation benefit function and constraint conditions of the lower-layer planning model with the maximum operation profits as a target, and calculating the optimal profits;
and 4, returning the optimal profit to the upper-layer planning model, after the net present value cost of the multi-source load system planning year is corrected, returning to the step 2, performing iteration, and outputting the planning capacity and the optimal profit of various units after the iteration termination condition is reached.
The invention is also characterized in that:
the specific process of the step 2 is as follows:
step 2.1, determining an upper-layer planning participation main body as an upper-layer planning investment main body and an upper-layer planning operation main body;
step 2.2, determining that the upper-layer planning source load equipment comprises a capacity unit, an energy storage unit and an energy utilization unit, and constructing constraint conditions according to the consumption of the upper-layer planning source load equipment and the uncertainty of the source-load equipment;
step 2.3, constructing an annual net present value cost function by taking the minimum annual net present value cost of the multi-energy system as a target:
Figure BDA0003400664850000031
in the formula, CNPCPlanning a layer net present value cost for the multi-source load system; cinvPlanning layer equipment investment cost for the multi-source load system;
Figure BDA0003400664850000032
the investment cost of each equipment unit of the system planning layer is calculated; capiFor the construction capacity of each device; f is an equal-year value coefficient; r is a reference discount rate; y is the planning period age; copeThe annual operation income of the elements to be planned of the multi-source load system is obtained.
Step 2.2, according to the consumption of the upper-layer planning source load equipment and the uncertainty of the source load equipment, constructing constraint conditions specifically as follows:
(a) the productivity unit model is as follows:
the capacity unit comprises a fan, a photovoltaic unit, a gas turbine unit, a combined heat and power generation unit and a gas boiler, and the capacity unit model is expressed as follows:
Figure BDA0003400664850000041
in the formula: peleThe sum of the output of the fan and the photovoltaic is obtained; sgasThe variation of the gas storage device;
Figure BDA0003400664850000042
Figure BDA0003400664850000043
the total gas consumption of the gas turbine unit, the cogeneration unit and the gas boiler is respectively;
(b) the energy storage unit model is as follows:
the energy storage unit comprises a storage battery, a heat storage module and a gas storage module, the output of the energy storage unit at the moment t is related to the residual electric quantity at the moment, the residual electric quantity at the previous moment and the electric quantity attenuation quantity per hour, and the model expression of the energy storage unit is as follows:
S＝[Sele,Sheat,Sgas]T (2)
in the formula: sele、Sheat、SheatThe variation of the electricity storage device, the heat storage device and the gas storage device are respectively;
(c) the energy use unit model is as follows:
the energy utilization unit comprises an electric load, a heat load, a gas load and an electric quantity interacting with a power grid, and the model of the energy utilization unit is represented as follows:
D＝[Dele,Dheat,Dgas]T (3)
in the formula: deleIs the sum of electric load and electric network interaction electric quantity Dheat、DgasRespectively representing the variation of heat load and gas load;
the coupling relation among the energy production unit, the energy storage unit and the energy utilization unit is shown as the formula (4):
Figure BDA0003400664850000044
in the formula:
Figure BDA0003400664850000045
the electricity and heat conversion efficiency of the cogeneration unit is achieved;
Figure BDA0003400664850000046
the conversion efficiency of the gas turbine unit and the conversion efficiency of the gas boiler are respectively obtained; hgasIs natural gas with low heat value;
(d) uncertainty model of source-load device:
and (3) combining the fluctuation and uncertainty of the output of the equipment on the source load side, so that a source load uncertainty model is built and expressed as:
Figure BDA0003400664850000051
in the formula: pfi(t) power values predicted by the source side and load side devices according to historical data; delta Pu(t)、ΔPv(t) upper and lower limits of source-side or load-side output fluctuation, u, respectivelyi(t)、vi(t) binary integer variables of respective variation ranges, when ui(t) 1 or (u)i(t) ═ 0), when the source side or the load side is at the upper limit (or lower limit) of uncertainty, at which time v isi(t) 0 (or v)i(t)＝1)；
Figure BDA0003400664850000052
Respectively the maximum value of the fluctuation range upper limit;
Figure BDA0003400664850000053
is the minimum value of the lower limit of the fluctuation range;
(e) constraint conditions
The constraint conditions comprise installation equipment capacity constraint, load power balance constraint, equipment operation upper and lower limit constraint, electric storage/heat device constraint and electric selling power constraint:
1) capacity constraint of installation equipment:
Figure BDA0003400664850000054
in the formula:
Figure BDA0003400664850000055
respectively installing upper and lower limits of capacity for the equipment i;
2) load power balance constraint:
Figure BDA0003400664850000056
in the formula:
Figure BDA0003400664850000057
the power generation operating power of a fuel gas peak shaving unit and cogeneration power generation equipment at the time t; pPW(t)、PPV(t)、PEES(t) isOperating power of wind power, photovoltaic and electric energy storage at the time t, wherein the operating power of the wind power and the photovoltaic is equal to the planned capacity of the wind power and the photovoltaic multiplied by a per unit value of a typical time of the corresponding season; pload(t) electrical load power at time t; psell(t) selling power from the energy concentrator to the power grid in a period t;
meanwhile, the thermal load power balance constraint also needs to satisfy the coupling equation relation of the energy hub in equation (4);
3) upper and lower limit constraints of equipment operation
Pi min≤Pi≤Pi max
In the formula: pi max、Pi minRespectively an upper limit and a lower limit of the running power of the equipment i at the moment t; the upper limit of the operating power of the gas peak shaving unit, the cogeneration unit and the gas boiler unit is the product of the planned capacity of the equipment and the energy conversion efficiency; the upper limit of the operating power of electricity and heat energy storage is the energy storage capacity multiplied by the energy storage conversion coefficient, and generally 0.18-0.21 of the energy storage planning capacity is taken;
4) electric/thermal storage device restraint
Figure BDA0003400664850000061
In the formula:
Figure BDA0003400664850000062
respectively representing the maximum and minimum values of the residual capacities of the electric energy storage and the thermal energy storage; omegachar_i、ωdis_iRespectively represents the charging and discharging/thermal efficiency of the electric energy storage and the thermal energy storage, and the charging and discharging/thermal efficiency ranges are all [0,1]；
Figure BDA0003400664850000063
Respectively representing the upper limits of charging, discharging and thermal power allowed by electric energy storage and thermal energy storage;
5) power supply restriction
The redundant electric quantity of wind-light output is incorporated into the power networks, in order to prevent that too big power of sending backward from causing the influence to the major network, need set up the power constraint of selling electricity:
Figure BDA0003400664850000064
in the formula:
Figure BDA0003400664850000065
the maximum allowed power sold to the main network for the energy hub.
The specific process of the step 3 is as follows:
3.1, transmitting the planning capacity of each unit to a lower-layer planning model, and constraining the upper and lower output limits of each device according to the planning capacity obtained by solving through an upper-layer planning model in the lower-layer planning model;
step 3.2, determining that the lower-layer planning participation main body comprises a lower-layer planning equipment main body, an electric power price clearing market and a plurality of users;
step 3.3, constructing an operation benefit function of an operation layer with the maximum target of the sum of the system operation and maintenance cost, the new energy consumption cost, the gas purchase cost, the wind and light abandonment punishment component, the environmental cost and the electric power market clearing income of the multi-energy system, wherein the operation benefit function is expressed as follows:
max Cope＝365×εr×(Ccle+Cpeak-Com-Cbuy-Cab-Cen) (7)
in the formula: copeOperating benefits for the operation layer; ccle、Cpeak、Com、Cbuy、Cab、CenRespectively giving out clear income, new energy consumption cost, system operation and maintenance cost, energy purchasing cost, wind and light abandoning punishment cost and environment cost for the electric power market; epsilonrIs a typical seasonal time series scene probability.
The power market clearing benefit is expressed as:
Figure BDA0003400664850000071
in the formula: celeClearing the income for the electric power market;
Figure BDA0003400664850000072
reporting power of jth electric power, heating power and power grid in the area respectively;
Figure BDA0003400664850000073
CEgridreporting power of jth electric power, heating power and power grid in the area respectively; pn(i) Reporting power for the capacity unit equipment; CEnReporting price for the capacity unit equipment;
the system operation and maintenance cost is expressed as:
Figure BDA0003400664850000074
in the formula:
Figure BDA0003400664850000075
maintaining the coefficients for the operation of each device; capPlanning capacity for each device;
the gas purchase cost is expressed as:
Figure BDA0003400664850000076
in the formula: vgas(t) annual natural gas consumption volume, fgas(t) natural gas prices;
the wind and light abandoning penalty cost is expressed as:
Figure BDA0003400664850000077
in the formula: lambda [ alpha ]w、λpvRespectively as wind abandon and light abandon penalty factors, Δ pw,t、Δppv,tAbandoning wind and light quantity at the time t;
the environmental cost is expressed as:
Figure BDA0003400664850000081
in the formula: egParticipating in peak shaving operation increment for each gas turbine set;
Figure BDA0003400664850000082
is the carbon tax value.
Step 4, the iteration termination condition is as follows: the number of iterations reaches 50.
The invention has the beneficial effects that:
the multi-source load complementary planning method considering the optimal new energy consumption cost can give consideration to the conflict relationship between economic benefit and environmental benefit in the planning problem, simultaneously considers the new energy consumption mechanism and the dynamic characteristic of the energy price of the electric power market, overcomes the defect of the capacity optimization planning technology of the multi-source load system with multi-energy coupling, and has the characteristics of strong applicability and good effect.
Drawings
FIG. 1 is a block diagram of an energy hub for a multi-source system;
FIG. 2 is a diagram of a multi-source-load double-layer optimization configuration that accounts for new energy consumption cost optimization;
FIG. 3 is a diagram of new energy consumption patterns under a market mechanism;
fig. 4 is a diagram of a plan for executing the device operation at the operation layer.
FIG. 5 is a graph of transitional season peak shaver output;
FIG. 6 is a graph of summer peak shaver output;
FIG. 7 is a graph of winter peak shaver output;
FIG. 8 is a graph of photovoltaic output curves for a typical winter, summer, and transition season of the day;
FIG. 9 is a graph of wind power output curves for typical winter, summer and transition seasons.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.
Referring to the energy hub structure and the double-layer optimization configuration principle shown in fig. 1 and 2, the multisource load complementary planning method for analyzing and calculating the optimal new energy consumption cost includes: wind Turbine (WT), Photovoltaic (PV), Combined Heat and Power (CHP), Gas Boiler (GB), Gas turbine (GF), and Electrical Energy Storage (EES), Thermal Energy Storage (TES), and Gas Energy Storage (GES) of energy storage element.
The invention relates to a multisource and load complementary planning method with optimal new energy consumption cost, which is implemented according to the following steps:
constructing a double-layer planning model:
the multi-energy system containing high proportion of clean energy is responsible for supplying electricity, heat and gas load in the region, and the capacity obtained by the multi-energy system obtains income in the form of selling energy and surfing the internet with the rest of electricity for users in the region. When the multi-energy system can not meet the requirements of regional electricity and heat loads, the regional electricity load can be supplied to the power grid company for electricity purchasing and the regional heat load can be supplemented to the gas company for gas purchasing.
In order to better utilize the operation and complementary characteristics of new energy, ensure that clean energy can be used up and can be used up, a new energy consumption mode under a market mechanism is analyzed, an electric power market clearing mechanism is introduced into the planning of a multi-energy system, a multi-source load complementary integrated double-layer planning method considering the lowest consumption cost of the new energy is provided, and on the basis, a double-layer planning model is firstly constructed: the method comprises the steps that an upper-layer planning model is built according to annual net present value cost of the multi-energy system considering investment and operation cost of the multi-energy system, a lower-layer planning model is built according to operation net income containing new energy consumption cost and environment cost in the multi-energy system, planning capacity of various units obtained through planning is transmitted to the lower-layer operation model through the upper-layer planning model, the lower-layer planning model simulates the whole operation scheduling condition of the multi-energy system, a power market clearing mechanism is considered, and income is obtained and returned to the upper-layer planning model.
Calculating the planning capacity of various units:
the upper-layer planning model considers the optimization planning of key equipment in the multi-source load system, provides a planning model with the minimum annual net present value cost considering investment and operation cost, and transfers the capacity obtained through planning to the lower-layer operation model for optimization. Therefore, the calculation process for calculating the planning capacity of each type of unit is as follows: and according to the characteristics of the upper-layer planning model participating main bodies and the upper-layer planning source load equipment, constructing an annual net present value cost function and constraint conditions by taking the minimum annual net present value cost of the multi-energy system as a target, and calculating the planning capacity of various units.
Determining an upper-layer planning participation main body as an upper-layer planning investment main body and an upper-layer planning operation main body;
the energy hub model is proved to be an effective way for processing multi-source load coupling complementation in the comprehensive energy system, and provides an optimization space for multi-source load advantage complementation on the premise of energy supply and demand balance. The multi-source load system constructed by the method takes wind-solar new energy as a main body, is matched with energy storage, energy conversion equipment and multi-type loads, is divided into 3 units according to a graph 1, namely a capacity unit, an energy storage unit and an energy utilization unit, and constructs constraint conditions according to the consumption of upper-layer planning source load equipment and the uncertainty of the source-load equipment; according to the consumption of upper-layer planning source load equipment and the uncertainty of the source load equipment, the construction of constraint conditions specifically comprises the following steps:
(a) the productivity unit model is as follows:
the capacity unit comprises a fan, a photovoltaic unit, a gas turbine unit, a combined heat and power generation unit and a gas boiler, and the capacity unit model is expressed as follows:
Figure BDA0003400664850000101
in the formula: peleThe sum of the output of the fan and the photovoltaic is obtained; sgasThe variation of the gas storage device;
Figure BDA0003400664850000102
Figure BDA0003400664850000103
the total gas consumption of the gas turbine unit, the cogeneration unit and the gas boiler is respectively;
(b) the energy storage unit model is as follows:
the energy storage unit comprises a storage battery, a heat storage module and a gas storage module, the output of the energy storage unit at the moment t is related to the residual electric quantity at the moment, the residual electric quantity at the previous moment and the electric quantity attenuation quantity per hour, and the model expression of the energy storage unit is as follows:
S＝[Sele,Sheat,Sgas]T (2)
in the formula: sele、Sheat、SheatThe variation of the electricity storage device, the heat storage device and the gas storage device are respectively;
(c) the energy use unit model is as follows:
the energy utilization unit comprises an electric load, a heat load, a gas load and an electric quantity interacting with a power grid, and the model of the energy utilization unit is represented as follows:
D＝[Dele,Dheat,Dgas]T (3)
in the formula: deleIs the sum of electric load and electric network interaction electric quantity Dheat、DgasRespectively representing the variation of heat load and gas load;
the coupling relation among the energy production unit, the energy storage unit and the energy utilization unit is shown as the formula (4):
Figure BDA0003400664850000111
in the formula:
Figure BDA0003400664850000112
the electricity and heat conversion efficiency of the cogeneration unit is achieved;
Figure BDA0003400664850000113
the conversion efficiency of the gas turbine unit and the conversion efficiency of the gas boiler are respectively obtained; hgasIs natural gas with low heat value;
(d) uncertainty model of source-load device:
combining the fluctuation and uncertainty of the output of the equipment on the source load side, the influence of the source load equipment on the multi-source load system planning needs to be considered from the viewpoint of the uncertainty of the source load equipment, so that a source load uncertainty model is built and expressed as follows:
Figure BDA0003400664850000114
in the formula: pfi(t) power values predicted by the source side and load side devices according to historical data; delta Pu(t)、ΔPv(t) upper and lower limits of source-side or load-side output fluctuation, u, respectivelyi(t)、vi(t) binary integer variables of respective variation ranges, when ui(t) 1 or (u)i(t) ═ 0), when the source side or the load side is at the upper limit (or lower limit) of uncertainty, at which time v isi(t) 0 (or v)i(t)＝1)；
Figure BDA0003400664850000115
Respectively the maximum value of the fluctuation range upper limit;
Figure BDA0003400664850000121
is the minimum value of the lower limit of the fluctuation range;
(e) constraint conditions
The constraint conditions comprise installation equipment capacity constraint, load power balance constraint, equipment operation upper and lower limit constraint, electric storage/heat device constraint and electric selling power constraint:
1) capacity constraint of installation equipment:
Figure BDA0003400664850000122
in the formula:
Figure BDA0003400664850000123
respectively installing upper and lower limits of capacity for the equipment i;
2) load power balance constraint:
Figure BDA0003400664850000124
in the formula:
Figure BDA0003400664850000125
the power generation operating power of a fuel gas peak shaving unit and cogeneration power generation equipment at the time t; pPW(t)、PPV(t)、PEES(t) the operating power of wind power, photovoltaic and electric energy storage at the moment t, wherein the operating power of the wind power and the photovoltaic is equal to the planned capacity of the wind power and the photovoltaic multiplied by the per unit value of the typical daily moment of the corresponding season; pload(t) electrical load power at time t; psell(t) selling power from the energy concentrator to the power grid in a period t;
meanwhile, the thermal load power balance constraint also needs to satisfy the coupling equation relation of the energy hub in equation (4);
3) upper and lower limit constraints of equipment operation
Pi min≤Pi≤Pi max
In the formula: pi max、Pi minRespectively an upper limit and a lower limit of the running power of the equipment i at the moment t; the upper limit of the operating power of the gas peak shaving unit, the cogeneration unit and the gas boiler unit is the product of the planned capacity of the equipment and the energy conversion efficiency; the upper limit of the operating power of electricity and heat energy storage is the energy storage capacity multiplied by the energy storage conversion coefficient, and generally 0.18-0.21 of the energy storage planning capacity is taken;
4) electric/thermal storage device restraint
Figure BDA0003400664850000131
In the formula:
Figure BDA0003400664850000132
respectively representing the maximum and minimum values of the residual capacities of the electric energy storage and the thermal energy storage; omegachar_i、ωdis_iRespectively represents the charging and discharging/thermal efficiency of the electric energy storage and the thermal energy storage, and the charging and discharging/thermal efficiency ranges are all [0,1]；
Figure BDA0003400664850000133
Respectively representing the upper limits of charging, discharging and thermal power allowed by electric energy storage and thermal energy storage;
5) power supply restriction
The redundant electric quantity of wind-light output is incorporated into the power networks, in order to prevent that too big power of sending backward from causing the influence to the major network, need set up the power constraint of selling electricity:
Figure BDA0003400664850000134
in the formula:
Figure BDA0003400664850000135
the maximum allowed power sold to the main network for the energy hub.
The objective function of the upper-layer planning model is the minimization of the net current cost per year, and the decision variable is the installation capacity of equipment in the multi-source load system. And (3) with the equivalent year as a time scale, constructing an annual net present value cost function, wherein the net present value cost comprises system equipment investment cost and operation net income of a lower-layer operation model:
Figure BDA0003400664850000136
in the formula, CNPCPlanning a layer net present value cost for the multi-source load system; cinvPlanning layer equipment investment cost for the multi-source load system;
Figure BDA0003400664850000137
the investment cost of each equipment unit of the system planning layer is calculated; capiFor the construction capacity of each device; f is an equal-year value coefficient; r is a reference discount rate; y is the planning period age; copeThe annual operation income of the elements to be planned of the multi-source load system is obtained.
Calculating the lower layer income:
in the optimized operation of key equipment in a multi-source system, in order to promote the consumption of new wind energy, solar energy and new energy and obtain the maximum operation income, the low-carbon characteristic of the new energy is introduced into an electric power market clearing mechanismIn addition to the economic problem of cost and profit, greenhouse gases and air pollutants emitted by multi-energy conversion equipment such as cogeneration units, gas boilers, gas peak shaving units and the like are also of interest. The multi-source system is used as a terminal for comprehensive energy utilization, and has the advantages of comprehensively utilizing various production capacities and coupling units in the energy concentrator, and realizing the maximization of comprehensive energy utilization. On the basis, environmental value is added in a planning layer, and on the premise of ensuring regional energy supply and economy, environment-friendly energy supply is realized. The lower layer takes the system equipment capacity planned by the multi-source load system as a main body, considers the economic benefits generated under the clearing mechanism of the power market, and realizes the maximum annual economic efficiency and CO of the multi-source load system through an intelligent optimization method2The goal of minimum gas emissions.
In order to maximize the system operation benefit, the operation layer respectively reports the energy price to the power market according to the social benefit maximization principle, and the maximization of the multi-source load system operation benefit is met while the system energy utilization efficiency is improved.
Transferring the planning capacity of various units to a lower-layer operation model, combining a lower-layer planning participation subject and clearing profits of an electric power market, constructing an operation benefit function and constraint conditions of the lower-layer planning model with the maximum operation profits as a target, and calculating the optimal profits; the specific process is shown in fig. 4 as follows:
transferring the planned capacity of each unit to a lower-layer planning model, and constraining the upper and lower output limits of each device according to the planned capacity obtained by solving through an upper-layer planning model in the lower-layer planning model;
determining that the lower-layer planning participation main body comprises a lower-layer planning equipment main body, an electric power price clearing market and a plurality of users;
the method comprises the following steps of constructing an operation benefit function of an operation layer by taking the maximum sum of the system operation and maintenance cost, the new energy consumption cost, the gas purchase cost, the wind and light abandonment punishment component, the environment cost and the clear income of the power market of the multi-energy system as an objective, and expressing the maximum sum as follows:
max Cope＝365×εr×(Ccle+Cpeak-Com-Cbuy-Cab-Cen) (7)
in the formula: copeOperating benefits for the operation layer; ccle、Cpeak、Com、Cbuy、Cab、CenRespectively giving out clear income, new energy consumption cost, system operation and maintenance cost, energy purchasing cost, wind and light abandoning punishment cost and environment cost for the electric power market; epsilonrIs a typical seasonal time series scene probability.
The power market clearing benefit is expressed as:
Figure BDA0003400664850000151
in the formula: celeClearing the income for the electric power market;
Figure BDA0003400664850000152
reporting power of jth electric power, heating power and power grid in the area respectively;
Figure BDA0003400664850000153
CEgridreporting power of jth electric power, heating power and power grid in the area respectively; pn(i) Reporting power for the capacity unit equipment; CEnReporting price for the capacity unit equipment;
the system operation and maintenance cost is expressed as:
Figure BDA0003400664850000154
in the formula:
Figure BDA0003400664850000155
maintaining the coefficients for the operation of each device; capPlanning capacity for each device;
the gas purchase cost is expressed as:
Figure BDA0003400664850000156
in the formula: vgas(t) annual natural gas consumption volume, fgas(t) natural gas prices;
the wind and light abandoning penalty cost is expressed as:
Figure BDA0003400664850000157
in the formula: lambda [ alpha ]w、λpvRespectively as wind abandon and light abandon penalty factors, Δ pw,t、Δppv,tAbandoning wind and light quantity at the time t;
the environmental cost is expressed as:
Figure BDA0003400664850000158
in the formula: egParticipating in peak shaving operation increment for each gas turbine set;
Figure BDA0003400664850000161
is the carbon tax value.
And returning the optimal income to an upper-layer planning model, correcting the net present value cost of the planning year of the multi-source load system, returning to calculate the planning capacity of various units, performing iteration, and outputting the planning capacity and the optimal income of various units after an iteration termination condition is reached.
Through the proposed planning method, the overall utilization conditions of wind power and photovoltaic in the energy concentrator and the power generation proportion of clean energy in the system are compared, and the planning method can promote the consumption of the clean energy and realize the effects of green power generation and green energy utilization in the system.
(a) New energy consumption rate
The new energy consumption rate is defined as the percentage of the annual actual utilization value of the wind and light to the annual power generation value of the new energy, and is shown as a formula (13).
Figure BDA0003400664850000162
In the formula: wuse(t)、WactAnd (t) the actual utilization value and the actual power generation value of the new energy respectively.
(b) Permeability of new energy power generation
The new energy power generation permeability is defined as the percentage of the annual renewable energy output in all the clean energy power generation units and the electrical energy storage, and is shown as a formula (14).
Figure BDA0003400664850000163
In the formula: pwt(t)、Ppv(t)、
Figure BDA0003400664850000164
PEESAnd (t) are respectively the power generation and electricity storage output values of wind power, photovoltaic and gas turbine units.
In order to illustrate the effectiveness of the multisource load complementary planning method considering the optimal new energy consumption cost and show the influence of the new energy consumption cost on the overall planning of a multisource load system, on the aspect of the operation mode of the CHP unit, the condition that the system operation rule is considered, the electric power is high in summer, the heat demand is low, and the electric power and the heat demand are high in winter is considered, an operation mode of fixing the heat by electricity is adopted for the CHP unit in summer, an operation mode of fixing the electricity by heat is adopted in winter, and two peak regulation schemes are arranged in the method to compare the planning results. Scheme 1: considering that the CHP unit and the storage battery are brought into the new energy consumption cost, the CHP unit participates in peak shaving, and the planning GF unit is not considered in the system at the moment; scheme 2: and the GF unit and the storage battery are considered to be included into the new energy consumption cost. And performing time sequence simulation on the two schemes, wherein the planning results of the two schemes are shown in table 1.
TABLE 1
Figure BDA0003400664850000171
A comparison of the capital costs of the equipment under the 2 listed scenarios is shown in table 2.
TABLE 2
Figure BDA0003400664850000172
As can be seen by comparing the planning results of the two schemes with the investment cost results in tables 1 and 2, the overall planned capacity of scheme 2 is less than that of scheme 1. When the electrical load is high and the sum of the wind and light combined output cannot meet the electrical load requirement, the capacities of the wind power, the CHP unit and the storage battery of the system in the scheme 1 are higher than the planned capacity of the corresponding equipment in the scheme 2, because when the peak regulation of the gas unit is not considered, more capacities of the storage battery and the CHP unit are required to be configured to meet the peak regulation requirement. Meanwhile, the CHP unit planned by the scheme 1 has larger capacity, so that less GB unit capacity can meet the requirement of heat load, and meanwhile, heat energy storage with larger capacity is needed to meet heat circulation, while the CHP unit of the scheme 2 has smaller capacity and GB unit with larger capacity is needed to meet the requirement of heat load. On the power generation permeability of new energy, the wind-light power generation ratio of the scheme 1 is 63.74%, the wind-light power generation ratio of the scheme 2 is 66.21%, and the power generation ratio of the scheme 2 is improved by 2.2% compared with the scheme 1.
The economic comparison of the 2 schemes is shown in table 3:
TABLE 3
Figure BDA0003400664850000181
According to the economic comparison of the two schemes in the table 3, the cost of the scheme 2 is far less than that of the scheme 1, the cost of the scheme 2 is reduced by 2.2% compared with the scheme 1 in terms of investment construction cost, and the penalty cost of wind abandoning and light abandoning is also far lower than that of the scheme 1 in terms of the scheme 2, but in terms of peak regulation income, the overall peak regulation income value of the scheme 1 is higher than that of the scheme 2, and the overall output value is higher, so that the peak regulation income value of the scheme 1 is higher than that of the scheme 2.
In order to better embody the advantages of the planning method, the time sequence simulation is performed on the typical days of the three seasons, and the wind and light absorption rates of the two schemes are shown in table 4.
TABLE 4
Figure BDA0003400664850000191
The wind-light absorption rates in three seasons are compared to know that the wind-light absorption rate in summer is high, the output of the peak shaving unit is required to meet the requirement of the electric load, the wind-light output can be fully absorbed in two schemes, the wind-power output in transition seasons and winter is high, the wind-power planning capacity in the scheme 1 is larger than the wind-power capacity value in the scheme 2, the wind-power output at night is high, and meanwhile, due to grid-connected constraint, the new energy absorption rate in the scheme 1 is smaller than the new energy absorption rate in the scheme 2 on the whole.
Fig. 5, fig. 6 and fig. 7 are comparative graphs of peak-shaving output curves of 2 schemes in transition seasons, summer seasons and winter seasons respectively. It can be seen from fig. 5, 6 and 7 that when the load shortage is large, the peak-shaving output value of the GF unit and the storage battery in the scheme 1 is smaller than that of the CHP unit and the storage battery as a whole, the scheme 2 can play a good role in peak shaving and valley filling through a small peak-shaving value, and can also see that the peak-shaving requirement is highest in the midday time period in summer, while the peak-shaving is mainly distributed at night in the transition seasons and winter, which is related to the time sequence characteristics of seasons, and it can be seen from comparison of peak shaving in different seasons in the 2 schemes.
FIG. 8 is a typical graph of the variation of photovoltaic output curves in winter, summer and transition seasons, and it can be seen from FIG. 8 that the illumination intensity in summer is long, the radiation duration is longer, the photovoltaic output power is higher in summer than in transition seasons and winter, and because the electrical load is higher in summer than in other seasons, the 2 schemes can achieve the full wind and light consumption;
fig. 9 is a typical change diagram of wind power output curves in winter, summer and transition seasons, and it can be seen from fig. 9 that the wind power output power in the transition season is much higher than that in winter and summer, and compared with 2 schemes, it can be seen that the wind-solar energy consumption rate in the transition season is lower than that in summer and winter, and meanwhile, the wind abandon penalty cost is increased, but compared with scheme 1, scheme 2 can improve the wind power utilization rate and reduce the wind abandon penalty cost to a certain extent.
By the mode, the multi-source load complementary planning method with the optimal new energy consumption cost is taken into consideration, the conflict relationship between economic benefit and environmental benefit in the planning problem can be considered, the new energy consumption mechanism and the dynamic property of the energy price of the electric power market are considered, the problem of the deficiency of the multi-source load system capacity optimization planning technology of multi-energy coupling is solved, and the multi-source load complementary planning method has the characteristics of strong applicability and good effect.

Claims (6)

1. The multi-source load complementary planning method considering the optimal new energy consumption cost is characterized by being implemented according to the following steps:
step 1, constructing a double-layer planning model: the method comprises the steps that an upper-layer planning model is built according to annual net present value cost of a multi-energy system considering investment and operation cost of the multi-energy system, a lower-layer planning model is built according to operation net income containing new energy consumption cost and environment cost in the multi-energy system, planning capacity of various units obtained through planning is transmitted to the lower-layer operation model through the upper-layer planning model, the lower-layer planning model simulates the overall operation scheduling condition of the multi-energy system, a power market clearing mechanism is considered, and income is obtained and returned to the upper-layer planning model;
step 2, calculating the planning capacity of various units: constructing an annual net present value cost function and constraint conditions by taking the minimum annual net present value cost of the multi-energy system as a target according to the characteristics of an upper-layer planning participation main body and upper-layer planning source load equipment, and calculating planning capacity of various units;
step 3, calculating the lower-layer income: transferring the planning capacity of various units to a lower-layer operation model, combining a lower-layer planning participation subject and clearing profits of an electric power market, constructing an operation benefit function and constraint conditions of the lower-layer planning model with the maximum operation profits as a target, and calculating the optimal profits;
and 4, returning the optimal profit to the upper-layer planning model, after the net present value cost of the multi-source load system planning year is corrected, returning to the step 2, performing iteration, and outputting the planning capacity and the optimal profit of various units after the iteration termination condition is reached.
2. The multi-source load complementary planning method considering the optimal new energy consumption cost according to claim 1, wherein the specific process of the step 2 is as follows:
step 2.1, determining an upper-layer planning participation main body as an upper-layer planning investment main body and an upper-layer planning operation main body;
step 2.2, determining that the upper-layer planning source load equipment comprises a capacity unit, an energy storage unit and an energy utilization unit, and constructing constraint conditions according to the consumption of the upper-layer planning source load equipment and the uncertainty of the source-load equipment;
step 2.3, constructing an annual net present value cost function by taking the minimum annual net present value cost of the multi-energy system as a target:
Figure FDA0003400664840000021
in the formula, CNPCPlanning a layer net present value cost for the multi-source load system; cinvPlanning layer equipment investment cost for the multi-source load system;
Figure FDA0003400664840000022
the investment cost of each equipment unit of the system planning layer is calculated; capiFor the construction capacity of each device; f is an equal-year value coefficient; r is a reference discount rate; y is the planning period age; copeThe annual operation income of the elements to be planned of the multi-source load system is obtained.
3. The multi-source load complementary planning method considering the optimal new energy consumption cost according to claim 2, wherein the step 2.2 of constructing the constraint conditions according to the consumption of the upper-layer planning source load equipment and the uncertainty of the source load equipment specifically comprises the following steps:
(a) the productivity unit model is as follows:
the capacity unit comprises a fan, a photovoltaic unit, a gas turbine unit, a combined heat and power generation unit and a gas boiler, and the capacity unit model is expressed as follows:
Figure FDA0003400664840000023
in the formula: peleThe sum of the output of the fan and the photovoltaic is obtained; sgasThe variation of the gas storage device;
Figure FDA0003400664840000024
Figure FDA0003400664840000025
the total gas consumption of the gas turbine unit, the cogeneration unit and the gas boiler is respectively;
(b) the energy storage unit model is as follows:
the energy storage unit comprises a storage battery, a heat storage module and a gas storage module, the output of the energy storage unit at the moment t is related to the residual electric quantity at the moment, the residual electric quantity at the previous moment and the electric quantity attenuation quantity per hour, and the model expression of the energy storage unit is as follows:
S＝[Sele,Sheat,Sgas]T (2)
in the formula: sele、Sheat、SheatThe variation of the electricity storage device, the heat storage device and the gas storage device are respectively;
(c) the energy use unit model is as follows:
the energy utilization unit comprises an electric load, a heat load, a gas load and an electric quantity interacting with a power grid, and the model of the energy utilization unit is represented as follows:
D＝[Dele,Dheat,Dgas]T (3)
in the formula: deleIs the sum of electric load and electric network interaction electric quantity Dheat、DgasRespectively representing the variation of heat load and gas load;
the coupling relation among the energy production unit, the energy storage unit and the energy utilization unit is shown as the formula (4):
Figure FDA0003400664840000031
in the formula:
Figure FDA0003400664840000032
the electricity and heat conversion efficiency of the cogeneration unit is achieved;
Figure FDA0003400664840000033
the conversion efficiency of the gas turbine unit and the conversion efficiency of the gas boiler are respectively obtained; hgasIs natural gas with low heat value;
(d) uncertainty model of source-load device:
and (3) combining the fluctuation and uncertainty of the output of the equipment on the source load side, so that a source load uncertainty model is built and expressed as:
Figure FDA0003400664840000034
in the formula: pfi(t) power values predicted by the source side and load side devices according to historical data; delta Pu(t)、ΔPv(t) upper and lower limits of source-side or load-side output fluctuation, u, respectivelyi(t)、vi(t) binary integer variables of respective variation ranges, when ui(t) 1 or (u)i(t) ═ 0), when the source side or the load side is at the upper limit (or lower limit) of uncertainty, at which time v isi(t) 0 (or v)i(t)＝1)；
Figure FDA0003400664840000035
Respectively the maximum value of the fluctuation range upper limit;
Figure FDA0003400664840000041
is the minimum value of the lower limit of the fluctuation range;
(e) constraint conditions
The constraint conditions comprise installation equipment capacity constraint, load power balance constraint, equipment operation upper and lower limit constraint, electric storage/heat device constraint and electric selling power constraint:
1) capacity constraint of installation equipment:
Figure FDA0003400664840000042
in the formula:
Figure FDA0003400664840000043
respectively installing upper and lower limits of capacity for the equipment i;
2) load power balance constraint:
Figure FDA0003400664840000044
in the formula:
Figure FDA0003400664840000045
the power generation operating power of a fuel gas peak shaving unit and cogeneration power generation equipment at the time t; pPW(t)、PPV(t)、PEES(t) the operating power of wind power, photovoltaic and electric energy storage at the moment t, wherein the operating power of the wind power and the photovoltaic is equal to the planned capacity of the wind power and the photovoltaic multiplied by the per unit value of the typical daily moment of the corresponding season; pload(t) electrical load power at time t; psell(t) selling power from the energy concentrator to the power grid in a period t;
meanwhile, the thermal load power balance constraint also needs to satisfy the coupling equation relation of the energy hub in equation (4);
3) upper and lower limit constraints of equipment operation
Figure FDA0003400664840000046
In the formula:
Figure FDA0003400664840000047
respectively an upper limit and a lower limit of the running power of the equipment i at the moment t; wherein the gas isThe upper limit of the operating power of the peak shaving unit, the cogeneration unit and the gas boiler unit is the product of the planned capacity of the equipment and the energy conversion efficiency; the upper limit of the operating power of electricity and heat energy storage is the energy storage capacity multiplied by the energy storage conversion coefficient, and generally 0.18-0.21 of the energy storage planning capacity is taken;
4) electric/thermal storage device restraint
Figure FDA0003400664840000051
In the formula:
Figure FDA0003400664840000052
respectively representing the maximum and minimum values of the residual capacities of the electric energy storage and the thermal energy storage; omegachar_i、ωdis_iRespectively represents the charging and discharging/thermal efficiency of the electric energy storage and the thermal energy storage, and the charging and discharging/thermal efficiency ranges are all [0,1]；
Figure FDA0003400664840000053
Respectively representing the upper limits of charging, discharging and thermal power allowed by electric energy storage and thermal energy storage;
5) power supply restriction
The redundant electric quantity of wind-light output is incorporated into the power networks, in order to prevent that too big power of sending backward from causing the influence to the major network, need set up the power constraint of selling electricity:
Figure FDA0003400664840000054
in the formula:
Figure FDA0003400664840000055
the maximum allowed power sold to the main network for the energy hub.
4. The multi-source load complementary planning method considering the optimal new energy consumption cost according to claim 1, wherein the specific process of the step 3 is as follows:
3.1, transmitting the planning capacity of each unit to a lower-layer planning model, and constraining the upper and lower output limits of each device according to the planning capacity obtained by solving through an upper-layer planning model in the lower-layer planning model;
step 3.2, determining that the lower-layer planning participation main body comprises a lower-layer planning equipment main body, an electric power price clearing market and a plurality of users;
step 3.3, constructing an operation benefit function of an operation layer with the maximum target of the sum of the system operation and maintenance cost, the new energy consumption cost, the gas purchase cost, the wind and light abandonment punishment component, the environmental cost and the electric power market clearing income of the multi-energy system, wherein the operation benefit function is expressed as follows:
maxCope＝365×εr×(Ccle+Cpeak-Com-Cbuy-Cab-Cen) (7)
in the formula: copeOperating benefits for the operation layer; ccle、Cpeak、Com、Cbuy、Cab、CenRespectively giving out clear income, new energy consumption cost, system operation and maintenance cost, energy purchasing cost, wind and light abandoning punishment cost and environment cost for the electric power market; epsilonrIs a typical seasonal time series scene probability.
5. The multi-source load complementary planning method considering new energy consumption cost optimization according to claim 4, wherein the electric power market clearing benefit is expressed as:
Figure FDA0003400664840000061
in the formula: celeClearing the income for the electric power market;
Figure FDA0003400664840000062
reporting power of jth electric power, heating power and power grid in the area respectively;
Figure FDA0003400664840000063
CEgridreporting power of jth electric power, heating power and power grid in the area respectively; pn(i) Reporting power for the capacity unit equipment; CEnReporting price for the capacity unit equipment;
the system operation and maintenance cost is expressed as:
Figure FDA0003400664840000064
in the formula:
Figure FDA0003400664840000065
maintaining the coefficients for the operation of each device; capPlanning capacity for each device;
the gas purchase cost is expressed as:
Figure FDA0003400664840000066
in the formula: vgas(t) annual natural gas consumption volume, fgas(t) natural gas prices;
the wind and light abandoning penalty cost is expressed as:
Figure FDA0003400664840000067
in the formula: lambda [ alpha ]w、λpvRespectively as wind abandon and light abandon penalty factors, Δ pw,t、Δppv,tAbandoning wind and light quantity at the time t;
the environmental cost is expressed as:
Figure FDA0003400664840000068
in the formula: egParticipating in peak shaving operation increment for each gas turbine set;
Figure FDA0003400664840000069
is the carbon tax value.
6. The multi-source load complementary planning method considering the optimal new energy consumption cost according to claim 1, wherein the iteration termination condition in step 4 is: the number of iterations reaches 50.
CN202111498622.8A 2021-12-09 2021-12-09 Multi-source load complementary planning method considering new energy consumption cost optimization Pending CN114372609A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117117924A (en) * 2023-10-24 2023-11-24 国网湖北省电力有限公司经济技术研究院 Energy storage capacity configuration method, device and equipment considering clear market income

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117117924A (en) * 2023-10-24 2023-11-24 国网湖北省电力有限公司经济技术研究院 Energy storage capacity configuration method, device and equipment considering clear market income
CN117117924B (en) * 2023-10-24 2023-12-22 国网湖北省电力有限公司经济技术研究院 Energy storage capacity configuration method, device and equipment considering clear market income

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