CN114118609A - Comprehensive energy system optimization operation method considering energy market trading - Google Patents

Comprehensive energy system optimization operation method considering energy market trading Download PDF

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CN114118609A
CN114118609A CN202111469785.3A CN202111469785A CN114118609A CN 114118609 A CN114118609 A CN 114118609A CN 202111469785 A CN202111469785 A CN 202111469785A CN 114118609 A CN114118609 A CN 114118609A
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郭维一
王家同
王志宏
韩明彤
郭健楠
李淼
李琳
宋美奇
高鹤鸣
陈诗琪
曲薪颖
张悦
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Yingkou Electric Power Supply Co Of State Grid Liaoning Electric Power Supply Co ltd
State Grid Corp of China SGCC
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Abstract

The invention provides a comprehensive energy system optimization operation method considering energy market trading, which establishes a double-layer optimization model to solve the problem, and sets the energy supply unit price by the upper layer aiming at maximizing the energy supply side income; the lower layer target is the maximum user energy satisfaction degree target, and the energy consumption at each moment is calculated; the three types of user loads are subjected to detailed simulation analysis, and the conditions that the user interaction is not considered by the energy supply side, the fixed energy supply price and the energy supply side are adopted, the user interaction is considered, and when the floating energy supply price is adopted, the two conditions of the satisfaction degree of the energy consumption of the user and the income of the energy supply side are compared; the method comprises the steps that under the mode of the invention, the change of the energy utilization strategy of a user and the reason of the change of the energy utilization strategy of the energy supply side are analyzed, and the influence of the energy utilization preference of the user on the income of the energy supply side is analyzed; and data conclusion showing that the user can perform better for the satisfaction of the user and the increase of the income of the energy supply side is given.

Description

Comprehensive energy system optimization operation method considering energy market trading
Technical Field
The invention relates to the field of operation scheduling optimization of a comprehensive energy system, in particular to an optimized operation scheme of an energy supplier under the condition that multiple kinds of energy participate in market-oriented trading at the same time, and particularly relates to an optimized operation method of the comprehensive energy system considering energy market trading.
Background
The rapid growth of the world population has made the energy crisis increasingly prominent and has received widespread attention from countries throughout the world. In the field of Energy supply, compared with a traditional thermoelectric Power supply System, an Integrated Energy System (IES) with a Combined Heat and Power (CHP) unit as a core is in Energy conservation and emission reductionAnd the performance in the aspects of improving the energy utilization efficiency and the like is more outstanding, so that the method becomes an important development idea for solving the energy crisis. In the development of the comprehensive energy system, the whole energy supply of the system is optimized, the whole operation economy of the system is improved, and the method is a core problem for driving the commercialization forward development of the energy supply system[1-2]。
At present, one idea for improving the economical efficiency of the operation of a comprehensive energy system is to optimize the output of various devices on the combined energy supply side so as to reduce the energy supply cost[3-5]Reference [6 ]]On the premise of considering the network constraints of electric and thermal gases, a multi-type energy combined supply optimization model of the regional energy Internet is provided; document [7 ]]When comprehensive energy optimization modeling is carried out, the load uncertainty of random participation demand response users is considered; document [8]When the comprehensive energy system is adopted for supplying energy, simulation data such as the annual operation cost, the fuel consumption, the carbon emission and the like of the energy system are provided based on the energy prediction data of China in 2020. The above documents all prove the important role of the comprehensive energy system in improving energy supply benefit and reducing environmental pollution in simulation. The references in this paragraph are as follows:
[1] gi Hongjie, Wang Dan, Xuxiong, et al, regional Integrated energy systems several problems research [ J ] electric power systems automation, 2015,39(7): 198-.
[2] Wangyi, Zhang Ning, KangChongqing, overview of optimization planning and operation research of energy hub in energy Internet and prospect [ J ] in China Motor engineering bulletin 2015,35(22):5669-5681.
[3] Augen, herran, multipotency complementation, integrated optimization of energy system key technologies and challenges [ J ] power system automation, 2018,42(4):2-10,46.
[4] Shao Cheng, Wang Xifan, Wang Xili, etc. analysis and planning of multi-energy system was first explored [ J ] China Motor engineering report 2016,36(14):3817-3829.
[5]Admasie S,Bukhari SBA,Haider R,et al.A Passive Islanding Detection Scheme Using Variational Mode Decomposition-based Mode Singular Entropy for Integrated Microgrids[J].Electric Power Systems Research,2019,177:105983.
[6]Shabani MJ,Moghaddas-tafreshi SM.Fully-decentralized Coordination for Simultaneous Hydrogen,Power,and Heat Interaction in a Multi-carrier-energy System Considering Private Ownership[J].Electric Power Systems Research,2020,180:106099.
[7]
Figure BDA0003391193230000021
EAM,Mancarella P.Energy Systems Integration in Smart Districts:Robust Optimisation of Multi-energy Flows in Integrated Electricity,Heat and Gas Networks[J].IEEE Transactions on Smart Grid,2019,10(1):1122-1131.
With the development and application of the concept of terminal Energy Management and Energy Management System (EMS), load demands can be optimally managed, so that the Energy supply load faced by the Energy supply System is also changed. The research on the participation of the EMS in the system optimization operation mainly comprises the following steps: energy supply system for managing large-scale distributed power access through EMS[8-9]Realize energy conservation and emission reduction of users[10-11]And integrated management of cogeneration systems[12-14]. For the comprehensive energy supplier, in order to pursue improvement of self benefits, the load change characteristics of the user in the management mode of the comprehensive energy system EMS must be fully known, and reasonable incentive measures (such as energy pricing in an authorized range) are correspondingly formulated to guide the user to participate in interaction, so as to achieve the purpose of mutual benefits and win-win, but the existing invention is difficult to positively solve the problems. The references in this paragraph are as follows:
[8] zhoukuang, Zhengjihui, Jingchaoxia, etc. the comprehensive energy system for park microgrid has multi-objective optimized design [ J ] power grid technology, 2018,42(6): 1687-.
[9]Byrne RH,Nguyen TA,Copp DA,et al.Energy Management and Optimization Methods for Grid Energy Storage Systems[J].IEEE Access,2018,6:13231-13260.
[10]Pilloni V,Floris A,Meloni A,et al.Smart Home Energy Management Including Renewable Sources:a Qoe-driven Approach[J].IEEE Transactions on Smart Grid,2018,9(3):2006-2018.
[11] Wu Smart, Tang Wei, white Merry, etc. user-side energy Internet planning based on energy routers [ J ] Power System Automation, 2017,41(4):20-28.
[12]Liu N,Wang J,Wang L.Hybrid Energy Sharing for Multiple Microgrids in an Integrated Heat–electricity Energy System[J].Ieee Transactions on Sustainable Energy,2019,10(3):1139-1151.
[13]Jiang Z,Ai Q,Hao R.Integrated Demand Response Mechanism for Industrial Energy System Based on Multi-energy Interaction[J].Ieee Access,2019,7:66336-66346.
[14] Xu navigation, Dongfeng, He-Zhan-natural, etc. based on the comprehensive demand response of electricity/heat with multiple energy complementation [ J ] power grid technology, 2019,43(2): 480-.
Disclosure of Invention
The invention aims to carry out the optimization operation research of the comprehensive energy system considering the interaction of the users aiming at the current situation, and mainly contributes to the following points:
1) and providing an energy system optimization operation mechanism considering user interaction under an integrated energy system EMS management mode. The two factors influencing the sizes of various loads of the user are fully considered, the energy supplier can perform energy independent pricing in the authorized range, the user is encouraged to adjust the self load curve and add the self load curve into an interaction system, the economic benefit of the energy supplier is improved, and the energy consumption experience of the user is also improved.
2) And considering the marginal utility of the user energy consumption, establishing a corresponding mathematical model for the user energy consumption requirement under the management mode of the integrated energy system EMS. When the profit of the energy supplier is calculated, the price of the energy unit given by the energy supplier will change the energy purchase rate and the proportion of the energy purchased by the user, and the change obviously will also affect the overall profit of the energy supplier.
3) For interactive iteration influence brought by energy supplier changing energy supply price, the invention constructs a double-layer model to process. The upper layer aims at maximizing the income of energy suppliers, and the energy price at each moment is determined by adopting a genetic algorithm; the lower layer determines the energy purchased by the user at the current price by taking the user satisfaction maximization as a target.
The invention provides an energy market transaction-related comprehensive energy system optimization operation method, which considers the structure of a user-interactive comprehensive energy system:
the integrated energy system considering user interaction regards the user under the control of the EMS and the energy supply station operated by the energy supply provider as a unified whole. Energy suppliers earn energy supply benefits while meeting user load requirements through optimized output scheduling of energy supply equipment in an energy supply station. The EMS system of the user can automatically optimize and select the energy purchasing combination at each moment according to the unit price of the real-time cold, hot and electric energy sources. Therefore, changing the energy unit price will affect the energy selling of the energy supplier, thereby affecting the final profit. The energy supply station issues the energy unit price of each moment through the information center, the user EMS system receives price information, combines the user energy demand to compile an energy consumption plan, and reports the plan to the energy supply station, and the energy supply station compiles a production plan according to the load information reported by the EMS system and the self energy supply equipment state. The overall operation structure of the energy supply system is shown as the following chart:
the EMS system compiles an optimization energy utilization plan by taking the maximum user energy utilization satisfaction as a target, and a mathematical model of user energy utilization effectiveness is required to be constructed for quantifying the user energy utilization satisfaction. The energy use behaviors of different users can be described by different utility functions, and the method adopts a mature secondary utility function to model:
Figure BDA0003391193230000061
in the formula (1), the electricity utilization efficiency of the ith user in the t-th time period is represented;
Figure BDA0003391193230000062
and
Figure BDA0003391193230000063
the preset value is obtained according to the user energy consumption behavior fitting;
Figure BDA0003391193230000064
is the electricity consumption of the user. The formula (1) gives a calculation method of the electricity utilization utility of the user, and accordingly, the utility of the heat and cold energy of the user can be obtained similarly:
Figure BDA0003391193230000065
Figure BDA0003391193230000066
in the formulae (2) to (3),
Figure BDA0003391193230000067
and
Figure BDA0003391193230000068
respectively representing the effects of heat and cold energy used by users; unlike equation (1), the present invention takes into account the portion of the user's own heating, cooling, and the like, which brings utility
Figure BDA0003391193230000069
And
Figure BDA00033911932300000610
respectively representing the electricity consumption of the heating equipment and the refrigeration equipment of the user; etae2hAnd ηe2cRespectively representing the working efficiency or energy efficiency coefficient of the user heating and refrigerating equipment.
The overall usability and satisfaction of the user can be expressed by the following model:
Figure BDA00033911932300000611
Figure BDA00033911932300000612
formula (4) is the total energy use utility of the user; the formula (5) is used for calculating the user satisfaction, and the EMS system of the user uses
Figure BDA00033911932300000613
To optimize the objective, wherein
Figure BDA00033911932300000614
And the unit prices of the energy are respectively given to the information center of the energy supply station at the current moment. It should be noted that the user can use the energy ui,tThis is equivalent to what is described in the present model as the positive economic benefit that the user can generate, which has been clearly described by equations (1) - (3).
In addition, the following constraints remain for the EMS system for the user:
Figure BDA0003391193230000071
Figure BDA0003391193230000072
Figure BDA0003391193230000073
Figure BDA0003391193230000074
Figure BDA0003391193230000075
in the formula (6), the first and second groups,
Figure BDA0003391193230000076
a lower limit of the amount of electricity used necessary for the user,
Figure BDA0003391193230000077
the upper limit of the electricity consumption of the user is exceeded, and the use energy utility cannot be increased continuously; the meanings of the formulas (7) to (8) are similar to the formula (6), and the constraints of heat and cold energy used by the user are expressed; equations (9) - (10) describe the upper limit of the operation of the user-operated self-heating and cooling device, wherein
Figure BDA0003391193230000078
An upper heating capacity limit for the user;
Figure BDA0003391193230000079
the upper limit of the refrigeration capacity of the user.
Optimizing an operation model process considering the energy supply system of the user energy using behavior:
the energy supply station under the comprehensive energy system framework can provide various energy forms such as electricity, heat, cold and the like for load users. The energy supply station takes a CHP unit as a core and is supplemented with a plurality of energy conversion devices to meet different energy loads. The Power station considered by the present invention mainly includes a Transformer (Power Transformer, PT), a Gas Boiler (Gas Boiler, GB), a Heat Pump (Heat Pump, HP), and an Absorption Chiller (AC), in addition to the CHP unit. The devices were modeled separately as follows: the role of the power transformer in the energy supply station is mainly to interact with the power grid, and the purchased power from the power grid at any time of the energy supply station cannot exceed the limit of the transformer
Figure BDA00033911932300000710
In the formula (11), the reaction mixture is,
Figure BDA00033911932300000711
representing the purchased electric quantity of the energy supply station to the power grid in the t period;
Figure BDA00033911932300000712
representing the maximum power supply limit of the transformer.
The gas boiler burns natural gas and provides direct heat energy for users, and the energy conversion relationship is as follows:
Figure BDA0003391193230000081
Figure BDA0003391193230000082
in the formula (12), the reaction mixture is,
Figure BDA0003391193230000083
representing the heat energy generated by the gas boiler during the period t;
Figure BDA0003391193230000084
representing the amount of natural gas consumed by the gas boiler; etaGBThe working efficiency of the gas boiler. Equation (13) represents the maximum capacity of the gas boiler
Figure BDA0003391193230000085
And (4) limiting.
The heat pump can convert electric energy into heat energy and can also be used for refrigeration. The energy conversion relationship can be expressed as:
Figure BDA0003391193230000086
Figure BDA0003391193230000087
Figure BDA0003391193230000088
Figure BDA0003391193230000089
in formulae (14) to (17)
Figure BDA00033911932300000810
Respectively the output of the heat pump for heating/cooling; accordingly, the number of the first and second electrodes,
Figure BDA00033911932300000811
respectively inputting the electric quantity of the heat pump for heating/refrigerating;
Figure BDA00033911932300000812
the working efficiency of the equipment; equations (16) to (17) indicate that the heat pump needs to satisfy the maximum heating/cooling capacity
Figure BDA00033911932300000813
The limit of (2).
The absorption refrigerator can fully utilize the waste heat to convert the low-grade heat energy into cold energy to be supplied to users:
Figure BDA00033911932300000814
Figure BDA00033911932300000815
in formulae (18) to (19)
Figure BDA00033911932300000816
Indicating the refrigerating capacity of the absorption refrigerator;
Figure BDA00033911932300000817
representing the heat absorbed by the refrigerator; etaACIndicating the operating efficiency of the refrigerator; the expression (20) indicates that the absorption refrigerator needs to satisfy its maximum heat absorption capacity
Figure BDA0003391193230000091
Of (3) is performed.
The CHP unit generates electric energy by burning natural gas and simultaneously heats by using waste heat:
Figure BDA0003391193230000092
Figure BDA0003391193230000093
Figure BDA0003391193230000094
in the formulae (20) to (21),
Figure BDA0003391193230000095
and
Figure BDA0003391193230000096
representing the electric/thermal energy production and natural gas consumption of the CHP unit;
Figure BDA0003391193230000097
representing the generating efficiency of the CHP unit;
Figure BDA0003391193230000098
representing the ratio of the waste heat dissipation part to the total energy of the fuel gas; the formula (22) shows that the generating capacity of the CHP does not exceed the maximum output of the unit
Figure BDA0003391193230000099
Except for the self energy supply constraint of the equipment, the energy supply station integrally guarantees the energy balance for the energy consumption of the user, namely:
Figure BDA00033911932300000910
Figure BDA00033911932300000911
Figure BDA00033911932300000912
equations (23) - (25) are the balance of the current, hot and cold energy flow between the energy supply station and the user, respectively, and the meaning of each element in the equations is given above.
Aiming at a certain fixed load scene, the energy supply station can make various equipment output schemes with different combinations, and select the scheme with the highest economic benefit to operate. The revenue target for powering the power stations may be expressed as:
maxCprofit＝Csell-Cbuy-Cop-Cem (26)
in the formula (26), the total profit C of the energy supply stationprofitRespectively by energy sales earnings CsellEnergy purchase cost CbuyEquipment operation maintenance cost CopEnergy supply station emission cost CemThe four parts are determined together.
The concrete models of the four parts are as follows:
Figure BDA0003391193230000101
Figure BDA0003391193230000102
Figure BDA0003391193230000103
Figure BDA0003391193230000104
the variable explanations in formula (27) have been given above; in formulae (28) to (30)
Figure BDA0003391193230000105
pgasRespectively purchasing electricity and gas for the energy supply station;
Figure BDA0003391193230000106
the output of the kth equipment in the energy supply station at the time t is represented, omega represents the set of all types of equipment in the energy supply station, and the operation and maintenance cost of the whole energy supply station is related to the operation time of the equipment; the formula (30) represents the emission cost of the gas power generation of the power station, wheregasRepresenting the cost of emissions generated by burning a unit of gas.
The comprehensive energy system optimization operation method considering the energy market transaction comprises the following steps:
when the energy supply station takes equation (26) as an optimization target, a determined load scenario must be assumed; for the energy price information given by the energy supply station at each moment, the load EMS is optimized with equation (5), and the calculated energy consumption will in turn change the energy supply load of the energy supply station. Therefore, an interactive iterative relationship exists between the two. In order to fully show the relation and solve the optimization problem caused by the relation, a double-layer optimization model is constructed, the upper layer aims to maximize the total income of the energy supply station in the formula (26), the optimal unit price of various energy sources which enables the total income of the energy supply station to be the maximum at each moment is calculated, the calculation of the unit price of the energy sources adopts a genetic algorithm, and an initial value is randomly generated in a certain range and is transmitted to the lower layer; the user energy satisfaction degree of the lower layer is the maximum target in the formula (5), various energy purchasing quantities which enable the user satisfaction degree to be the maximum at all times are calculated, the lower layer optimization is a quadratic programming model, the solution can be carried out by using a Yalmips tool box in Matlab in combination with a Gurobi solver, the solved energy purchasing quantities are transmitted back to the upper layer to form a new energy supply scene, and iteration is carried out by using a genetic algorithm. The calculation flow is specifically shown in fig. 2:
(1) firstly, inputting initial information of upper and lower layer optimization: the upper-layer optimization initial information comprises energy supply station equipment information and parameters, gas price, power grid price information and the like; the lower layer initial information comprises a user utility function, a necessary energy use lower limit and an energy use upper limit of a user and the like;
(2) the upper layer generates an energy unit price initial population in an optimized mode and transmits the energy unit price initial population to the lower layer to optimize the EMS energy of the user;
(3) the lower layer is optimized to obtain an energy utilization plan under the current energy unit price and transmits the energy utilization plan back to the upper layer;
(4) the upper layer combines the energy utilization plan and the energy unit price of the lower layer, and the output of each energy supply device of the energy supply station in each time period is obtained through optimization by taking the formula (25) as an optimization target;
(5) and (4) repeating the steps (2) to (4) until the genetic algorithm is converged, and outputting the maximum benefit of the energy supply station, the energy supply unit price under the situation, the energy consumption satisfaction degree of the user, the energy consumption and other information.
The invention has the advantages that:
the comprehensive energy system optimization operation method considering the energy market transaction optimizes the load proportion structure of the load for the energy supply station, so that the operation proportion of the gas turbine in the energy supply station is improved, and the change also has positive influence on environmental protection. The method can effectively solve the problem of how to configure and operate the energy supply equipment of the energy supplier after the energy supplier is marketized in the future, and has higher practicability for the condition that the user owns an EMS system to adjust the energy per se.
Drawings
The invention is described in further detail below with reference to the following figures and embodiments:
FIG. 1 is a schematic diagram of the overall operation of an energy supply system;
fig. 2 is a schematic diagram of an optimization process.
Detailed Description
Example 1
The invention provides a comprehensive energy system optimization operation method considering energy market trading,
the integrated energy system architecture considering user interaction:
the integrated energy system considering user interaction regards the user under the control of the EMS and the energy supply station operated by the energy supply provider as a unified whole. Energy suppliers earn energy supply benefits while meeting user load requirements through optimized output scheduling of energy supply equipment in an energy supply station. The EMS system of the user can automatically optimize and select the energy purchasing combination at each moment according to the unit price of the real-time cold, hot and electric energy sources. Therefore, changing the energy unit price will affect the energy selling of the energy supplier, thereby affecting the final profit. The energy supply station issues the energy unit price of each moment through the information center, the user EMS system receives price information, combines the user energy demand to compile an energy consumption plan, and reports the plan to the energy supply station, and the energy supply station compiles a production plan according to the load information reported by the EMS system and the self energy supply equipment state. The overall operation structure of the energy supply system is shown as the following chart:
the EMS system compiles an optimization energy utilization plan by taking the maximum user energy utilization satisfaction as a target, and a mathematical model of user energy utilization effectiveness is required to be constructed for quantifying the user energy utilization satisfaction. The energy use behaviors of different users can be described by different utility functions, and the method adopts a mature secondary utility function to model:
Figure BDA0003391193230000131
in the formula (1), the electricity utilization efficiency of the ith user in the t-th time period is represented;
Figure BDA0003391193230000132
and
Figure BDA0003391193230000133
the preset value is obtained according to the user energy consumption behavior fitting;
Figure BDA0003391193230000134
is the electricity consumption of the user. The formula (1) gives a calculation method of the electricity utilization utility of the user, and accordingly, the utility of the heat and cold energy of the user can be obtained similarly:
Figure BDA0003391193230000135
Figure BDA0003391193230000136
in the formulae (2) to (3),
Figure BDA0003391193230000137
and
Figure BDA0003391193230000138
respectively representing the effects of heat and cold energy used by users; unlike equation (1), the present invention takes into account the portion of the user's own heating, cooling, and the like, which brings utility
Figure BDA0003391193230000139
And
Figure BDA00033911932300001310
respectively representing the electricity consumption of the heating equipment and the refrigeration equipment of the user; etae2hAnd ηe2cRespectively representing the working efficiency or energy efficiency coefficient of the user heating and refrigerating equipment.
The overall usability and satisfaction of the user can be expressed by the following model:
Figure BDA00033911932300001311
Figure BDA00033911932300001312
formula (4) is the total energy use utility of the user; the formula (5) is used for calculating the user satisfaction, and the EMS system of the user uses
Figure BDA00033911932300001313
To optimize the objective, wherein
Figure BDA00033911932300001314
And the unit prices of the energy are respectively given to the information center of the energy supply station at the current moment. It should be noted that the user can use the energy ui,tEquivalent to what is described as user-specific in this modelThe positive economic gain that can be generated is clearly described by equations (1) - (3).
In addition, the following constraints remain for the EMS system for the user:
Figure BDA0003391193230000141
Figure BDA0003391193230000142
Figure BDA0003391193230000143
Figure BDA0003391193230000144
Figure BDA0003391193230000145
in the formula (6), the first and second groups,
Figure BDA0003391193230000146
a lower limit of the amount of electricity used necessary for the user,
Figure BDA0003391193230000147
the upper limit of the electricity consumption of the user is exceeded, and the use energy utility cannot be increased continuously; the meanings of the formulas (7) to (8) are similar to the formula (6), and the constraints of heat and cold energy used by the user are expressed; equations (9) - (10) describe the upper limit of the operation of the user-operated self-heating and cooling device, wherein
Figure BDA0003391193230000148
An upper heating capacity limit for the user;
Figure BDA0003391193230000149
for cooling usersAnd (4) the upper limit of the capacity.
Optimizing an operation model process considering the energy supply system of the user energy using behavior:
the energy supply station under the comprehensive energy system framework can provide various energy forms such as electricity, heat, cold and the like for load users. The energy supply station takes a CHP unit as a core and is supplemented with a plurality of energy conversion devices to meet different energy loads. The Power station considered by the present invention mainly includes a Transformer (Power Transformer, PT), a Gas Boiler (Gas Boiler, GB), a Heat Pump (Heat Pump, HP), and an Absorption Chiller (AC), in addition to the CHP unit. The devices were modeled separately as follows: the role of the power transformer in the energy supply station is mainly to interact with the power grid, and the purchased power from the power grid at any time of the energy supply station cannot exceed the limit of the transformer
Figure BDA0003391193230000151
In the formula (11), the reaction mixture is,
Figure BDA0003391193230000152
representing the purchased electric quantity of the energy supply station to the power grid in the t period;
Figure BDA0003391193230000153
representing the maximum power supply limit of the transformer.
The gas boiler burns natural gas and provides direct heat energy for users, and the energy conversion relationship is as follows:
Figure BDA0003391193230000154
Figure BDA0003391193230000155
in the formula (12), the reaction mixture is,
Figure BDA0003391193230000156
indicating gas boiler production during time tHeat energy;
Figure BDA0003391193230000157
representing the amount of natural gas consumed by the gas boiler; etaGBThe working efficiency of the gas boiler. Equation (13) represents the maximum capacity of the gas boiler
Figure BDA0003391193230000158
And (4) limiting.
The heat pump can convert electric energy into heat energy and can also be used for refrigeration. The energy conversion relationship can be expressed as:
Figure BDA0003391193230000159
Figure BDA00033911932300001510
Figure BDA00033911932300001511
Figure BDA00033911932300001512
in formulae (14) to (17)
Figure BDA00033911932300001513
Respectively the output of the heat pump for heating/cooling; accordingly, the number of the first and second electrodes,
Figure BDA00033911932300001514
respectively inputting the electric quantity of the heat pump for heating/refrigerating;
Figure BDA00033911932300001515
the working efficiency of the equipment; equations (16) to (17) indicate that the heat pump needs to satisfy the maximum heating/cooling capacity
Figure BDA00033911932300001516
The limit of (2).
The absorption refrigerator can fully utilize the waste heat to convert the low-grade heat energy into cold energy to be supplied to users:
Figure BDA00033911932300001517
Figure BDA0003391193230000161
in formulae (18) to (19)
Figure BDA0003391193230000162
Indicating the refrigerating capacity of the absorption refrigerator;
Figure BDA0003391193230000163
representing the heat absorbed by the refrigerator; etaACIndicating the operating efficiency of the refrigerator; the expression (20) indicates that the absorption refrigerator needs to satisfy its maximum heat absorption capacity
Figure BDA0003391193230000164
Of (3) is performed.
The CHP unit generates electric energy by burning natural gas and simultaneously heats by using waste heat:
Figure BDA0003391193230000165
Figure BDA0003391193230000166
Figure BDA0003391193230000167
in the formulae (20) to (21),
Figure BDA0003391193230000168
and
Figure BDA0003391193230000169
representing the electric/thermal energy production and natural gas consumption of the CHP unit;
Figure BDA00033911932300001610
representing the generating efficiency of the CHP unit;
Figure BDA00033911932300001611
representing the ratio of the waste heat dissipation part to the total energy of the fuel gas; the formula (22) shows that the generating capacity of the CHP does not exceed the maximum output of the unit
Figure BDA00033911932300001612
Except for the self energy supply constraint of the equipment, the energy supply station integrally guarantees the energy balance for the energy consumption of the user, namely:
Figure BDA00033911932300001613
Figure BDA00033911932300001614
Figure BDA00033911932300001615
equations (23) - (25) are the balance of the current, hot and cold energy flow between the energy supply station and the user, respectively, and the meaning of each element in the equations is given above.
Aiming at a certain fixed load scene, the energy supply station can make various equipment output schemes with different combinations, and select the scheme with the highest economic benefit to operate. The revenue target for powering the power stations may be expressed as:
maxCprofit＝Csell-Cbuy-Cop-Cem (26)
formula (II)(26) In, total profit C of energy supply stationprofitRespectively by energy sales earnings CsellEnergy purchase cost CbuyEquipment operation maintenance cost CopEnergy supply station emission cost CemThe four parts are determined together.
The concrete models of the four parts are as follows:
Figure BDA0003391193230000171
Figure BDA0003391193230000172
Figure BDA0003391193230000173
Figure BDA0003391193230000174
the variable explanations in formula (27) have been given above; in formulae (28) to (30)
Figure BDA0003391193230000175
pgasRespectively purchasing electricity and gas for the energy supply station;
Figure BDA0003391193230000176
the output of the kth equipment in the energy supply station at the time t is represented, omega represents the set of all types of equipment in the energy supply station, and the operation and maintenance cost of the whole energy supply station is related to the operation time of the equipment; the formula (30) represents the emission cost of the gas power generation of the power station, wheregasRepresenting the cost of emissions generated by burning a unit of gas.
The comprehensive energy system optimization operation method considering the energy market transaction comprises the following steps:
when the energy supply station takes equation (26) as an optimization target, a determined load scenario must be assumed; for the energy price information given by the energy supply station at each moment, the load EMS is optimized with equation (5), and the calculated energy consumption will in turn change the energy supply load of the energy supply station. Therefore, an interactive iterative relationship exists between the two. In order to fully show the relation and solve the optimization problem caused by the relation, a double-layer optimization model is constructed, the upper layer aims to maximize the total income of the energy supply station in the formula (26), the optimal unit price of various energy sources which enables the total income of the energy supply station to be the maximum at each moment is calculated, the calculation of the unit price of the energy sources adopts a genetic algorithm, and an initial value is randomly generated in a certain range and is transmitted to the lower layer; the user energy satisfaction degree of the lower layer is the maximum target in the formula (5), various energy purchasing quantities which enable the user satisfaction degree to be the maximum at all times are calculated, the lower layer optimization is a quadratic programming model, the solution can be carried out by using a Yalmips tool box in Matlab in combination with a Gurobi solver, the solved energy purchasing quantities are transmitted back to the upper layer to form a new energy supply scene, and iteration is carried out by using a genetic algorithm. The calculation flow is specifically shown in fig. 2:
(1) firstly, inputting initial information of upper and lower layer optimization: the upper-layer optimization initial information comprises energy supply station equipment information and parameters, gas price, power grid price information and the like; the lower layer initial information comprises a user utility function, a necessary energy use lower limit and an energy use upper limit of a user and the like;
(2) the upper layer generates an energy unit price initial population in an optimized mode and transmits the energy unit price initial population to the lower layer to optimize the EMS energy of the user;
(3) the lower layer is optimized to obtain an energy utilization plan under the current energy unit price and transmits the energy utilization plan back to the upper layer;
(4) the upper layer combines the energy utilization plan and the energy unit price of the lower layer, and the output of each energy supply device of the energy supply station in each time period is obtained through optimization by taking the formula (25) as an optimization target;
(5) and (4) repeating the steps (2) to (4) until the genetic algorithm is converged, and outputting the maximum benefit of the energy supply station, the energy supply unit price under the situation, the energy consumption satisfaction degree of the user, the energy consumption and other information.
The method provided by the invention can effectively solve the problem of how to configure and operate the energy supply equipment of the energy supplier after the future energy marketization, and has higher practicability for the condition that the user owns an EMS system to adjust the energy per se.

Claims (4)

1. An energy market transaction-related optimized operation method for a comprehensive energy system is characterized by comprising the following steps of: the integrated energy system architecture considering user interaction: the comprehensive energy system considering user interaction treats users regulated and controlled by the EMS and energy supply stations operated by energy suppliers as a unified whole; energy suppliers earn energy supply benefits while meeting the load requirements of users through optimized output scheduling of energy supply equipment in the energy supply station; the EMS system of the user automatically optimizes and selects the energy purchasing combination at each moment according to the unit price of the real-time cold, hot and electric energy sources; therefore, changing the energy unit price will affect the energy selling of the energy supplier, thereby affecting the final profit, in the energy supply system constructed by the invention, the energy supplier can adjust various energy unit prices within a certain range, so as to achieve the purposes of stimulating the energy consumption of the user and increasing the total profit; the energy supply station issues the energy unit price at each moment through the information center, the user EMS system receives the price information, compiles an energy use plan by combining the user energy demand and reports the plan to the energy supply station, and the energy supply station compiles a production plan by combining the self energy supply equipment state according to the load information reported by the EMS system;
the EMS system compiles an optimization energy utilization plan by taking the maximum user energy utilization satisfaction as a target, and a mathematical model of user energy utilization effectiveness is required to be constructed for quantifying the user energy utilization satisfaction.
2. The method for optimized operation of an integrated energy system with consideration of energy market trading according to claim 1, wherein: optimizing an operation model process considering the energy supply system of the user energy using behavior:
the energy supply station under the comprehensive energy system framework can provide various energy forms such as electricity, heat, cold and the like for load users, the energy supply station takes a CHP unit as a core and is assisted by various energy conversion devices to meet different energy loads, and the energy supply station comprises a transformer, a gas boiler, a heat pump and an absorption refrigerator besides the CHP unit; the devices were modeled separately as follows: the role of the power transformer in the energy supply station is mainly to interact with the power grid, and the purchased power from the power grid at any time of the energy supply station cannot exceed the limit of the transformer.
3. The method for optimized operation of an integrated energy system with consideration of energy market trading according to claim 1, wherein: the comprehensive energy system optimization operation method considering the energy market transaction comprises the following steps:
when the energy supply station is used as an optimization target, a determined load scene is used as a premise; for the energy price information given by the energy supply station at each moment, the energy consumption calculated by the load EMS changes the energy supply load of the energy supply station;
constructing a double-layer optimization model, maximizing the total income of an upper-layer energy supply station as a target, calculating the optimal unit price of various energy sources at each moment to enable the total income of the energy supply station to reach the maximum, wherein the energy unit price is calculated by adopting a genetic algorithm, and an initial value is randomly generated within a certain range and is transmitted to a lower layer; the lower layer is used for calculating various energy purchasing quantities which enable the user satisfaction to be maximum at each moment by taking the user energy consumption satisfaction as the maximum target, the lower layer optimization is a quadratic programming model, the solution is carried out by utilizing a Yalmips tool box in Matlab in combination with a Gurobi solver, the solved energy purchasing quantities are transmitted back to the upper layer to form a new energy supply scene, and iteration is carried out by utilizing a genetic algorithm.
4. The method for optimized operation of an integrated energy system considering energy market trading according to claim 3, wherein: the method comprises the following specific steps:
(1) firstly, inputting initial information of upper and lower layer optimization: the upper-layer optimization initial information comprises energy supply station equipment information and parameters, gas price, power grid price information and the like; the lower layer initial information comprises a user utility function, a necessary energy use lower limit and an energy use upper limit of a user and the like;
(2) the upper layer generates an energy unit price initial population in an optimized mode and transmits the energy unit price initial population to the lower layer to optimize the EMS energy of the user;
(3) the lower layer is optimized to obtain an energy utilization plan under the current energy unit price and transmits the energy utilization plan back to the upper layer;
(4) the upper layer combines the energy utilization plan and the energy unit price of the lower layer, and the output of each energy supply device of the energy supply station in each time period is obtained through optimization by taking the formula (25) as an optimization target;
(5) and (4) repeating the steps (2) to (4) until the genetic algorithm is converged, and outputting the maximum benefit of the energy supply station, the energy supply unit price under the situation, the energy consumption satisfaction degree of the user, the energy consumption and other information.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115222219A (en) * 2022-06-27 2022-10-21 国网江苏省电力有限公司常州供电分公司 Optimal scheduling method and device for electric boiler system participating in power grid frequency modulation service

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115222219A (en) * 2022-06-27 2022-10-21 国网江苏省电力有限公司常州供电分公司 Optimal scheduling method and device for electric boiler system participating in power grid frequency modulation service

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