CN112819337A - Comprehensive energy system energy sharing method based on non-cooperative game - Google Patents

Comprehensive energy system energy sharing method based on non-cooperative game Download PDF

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CN112819337A
CN112819337A CN202110150373.7A CN202110150373A CN112819337A CN 112819337 A CN112819337 A CN 112819337A CN 202110150373 A CN202110150373 A CN 202110150373A CN 112819337 A CN112819337 A CN 112819337A
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彭巧
王秀丽
王一飞
吴霁
陈雨甜
张亮
宋平
钟桦
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Shanghai Electric Power Transaction Center Co ltd
Xian Jiaotong University
State Grid Shanghai Electric Power Co Ltd
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Abstract

A comprehensive energy system energy sharing method based on a non-cooperative game comprises the following steps: establishing a comprehensive energy system sharing mechanism framework: establishing an upper layer model of the comprehensive energy and a lower layer model of a producer and a consumer according to a sharing mechanism framework of the comprehensive energy system: establishing a non-cooperative game model according to the comprehensive energy upper layer model and the lower layer model of the producer and the consumer: and solving the non-cooperative game model by using a distributed algorithm rather than a centralized optimization method to obtain the price and the load, thereby realizing energy sharing. According to the non-cooperative game-based energy sharing method for the comprehensive energy system, the electric, gas and heat sharing price signals in a comprehensive energy operator can be obtained through an iterative game method through the non-cooperative game method, the energy using behaviors of users can be effectively guided, and the comfort level of the users is guaranteed; the energy efficiency can be improved and the optimal configuration of regional resources can be promoted by a method of integrating distributed resources and energy conversion through the comprehensive energy system.

Description

Comprehensive energy system energy sharing method based on non-cooperative game
Technical Field
The invention belongs to the cross field of shared economy and power markets, and relates to a comprehensive energy system energy sharing method based on a non-cooperative game.
Background
In order to alleviate environmental pollution and energy shortage, a comprehensive energy system concept of regional electricity, gas, heat energy conversion, distribution and coordination has been proposed in recent years. It is estimated that by 2030, the power generation of natural gas will increase by 230%, and the corresponding installed capacity will increase rapidly, further deepening the coupling inside the comprehensive energy system. The development of an integrated energy system which can realize deep fusion of new technology and distributed energy and diversification of requirements (cold, heat, electricity, gas and the like) becomes a necessary choice of an energy revolution.
Meanwhile, the concept of the energy concentrator also brings convenience to modeling and analysis of the comprehensive energy system. An energy hub is considered to be a unit where a plurality of energy carriers can be converted, conditioned and stored. It represents the interface between the energy infrastructure and the load. Coordination and optimization of various energy sources may improve reliability and flexibility of energy utilization.
Furthermore, the concept of shared economy has been well known and widely practiced commercially in recent years. In which the shared business model has been extended to the power field, and the business model represented by the energy storage sharing has started to operate. More forms can be developed in the future, and the sharing form of the electricity-gas-heat multi-energy field of the integrated energy system does not appear at present.
At present, the existing comprehensive energy system research has the following problems:
(1) today, the interaction between users and power generation resources has increased, providing users with more options to participate in energy transactions, and facilitating the transition of the role of end-users from traditional energy consumers to victims, such as smart buildings, commercial homes, and communities of residents who may consume and provide energy themselves. If the demand response of the user side is not fully considered in the market design process, the enthusiasm of the user side and the mutual transaction process among the motivation users cannot be fully exerted, and local consumption of distributed resources cannot be promoted.
(2) With the evolution of the electricity market, trading is changing from traditional vertically integrated structures (top-down) to interactive competitive structures (interaction between each other). Electricity prices affect load demands, and loads also affect electricity prices. The traditional centralized optimization method can hardly describe the interaction between the two methods and can not protect the information privacy. Therefore, in the integrated energy system, new methods to deal with price and user-side interaction are also required to be introduced in a large environment facing market innovation. The non-cooperative game theory is a theory for researching how each party makes a decision, and when interest association or conflict exists, a reasonable decision is made according to own ability and owned information, so that the non-cooperative game theory is suitable for analyzing interaction among multiple parties.
Disclosure of Invention
The invention aims to provide a comprehensive energy system energy sharing method based on a non-cooperative game aiming at the problem of comprehensive energy system energy sharing, the method considers the non-cooperative game and a double-layer model, can realize the maximization of profits of comprehensive energy operators and the effectiveness of producers and consumers, and can provide reference for the operation of a comprehensive energy system and the fusion of various energy sharing economy and power systems.
In order to achieve the purpose, the invention adopts the technical scheme that:
a comprehensive energy system energy sharing method based on a non-cooperative game comprises the following steps:
step I: establishing a comprehensive energy system sharing mechanism framework:
step II: establishing an upper layer model of the comprehensive energy and a lower layer model of a producer and a consumer according to a sharing mechanism framework of the comprehensive energy system:
step III: establishing a non-cooperative game model according to the comprehensive energy upper layer model and the lower layer model of the producer and the consumer:
step IV: and solving the non-cooperative game model by using a distributed algorithm rather than a centralized optimization method to obtain the price and the load, thereby realizing energy sharing.
The invention is further improved in that the comprehensive energy system sharing mechanism framework comprises an energy concentrator, new energy and a producer and consumer; the comprehensive energy system comprises a producer user side and a consumer user side and a comprehensive energy operator; the operator is an upper-layer main body of the game, issues price signals and is embedded with energy concentrator equipment for conversion; the producers and consumers are the lower main body of the game and carry out self sharing purchase and sale adjustment according to the price signal; the energy hub comprises an electricity, gas and heat energy storage device.
A further improvement of the invention is that the integrated energy system operator objective function is to maximize the profit itself, expressed as:
Figure BDA0002932490640000031
in the formula: fIESRepresents the profit of the comprehensive energy operator all day;
Figure BDA0002932490640000032
is the energy revenue sold to the producer or consumer during the time period t,
Figure BDA0002932490640000033
is the cost of purchase from the producer or consumer over time period t,
Figure BDA0002932490640000034
is the cost of interacting with the electricity market and the natural gas market,
Figure BDA0002932490640000035
is the operating cost of the energy hub.
A further improvement of the invention is the energy revenue sold to the producers or consumers during the time period t
Figure BDA0002932490640000036
Cost of purchase from the producer or consumer during time period t
Figure BDA0002932490640000037
Cost of interaction between electricity market and natural gas market
Figure BDA0002932490640000038
Operating costs associated with energy hubs
Figure BDA0002932490640000039
The following were used:
Figure BDA00029324906400000310
Figure BDA00029324906400000311
Figure BDA00029324906400000312
Figure BDA00029324906400000313
in the formula, λt p,b，λt f,b，λt h,bElectricity, supply price of natural gas and heat energy, lambda, respectively, of the energy shared by the producers and consumerst p,s，λt f,s，λt h,sRespectively, the electricity, gas and heat prices obtained by the producers receiving the energy share;
Figure BDA00029324906400000314
Figure BDA00029324906400000315
respectively the electricity, gas and heat provided and consumed by the producer and consumer i; lambda [ alpha ]t EH,p,s,λt EH,p,b,λt EH,f,s,λt EH,f,bThe prices of buying and selling electricity and natural gas in the market at the moment t are respectively; pt EH,s，Pt EH,b，Ft EH,s，Ft EH,bElectric energy and gas energy which are bought and sold in the market respectively;
Figure BDA00029324906400000316
and
Figure BDA00029324906400000317
energy of charging and discharging, gas and heat from the storage device respectively;
Figure BDA00029324906400000318
Figure BDA00029324906400000319
and
Figure BDA00029324906400000320
the unit cost of storing electricity, gas and heat by the storage device at time t;
the policy space of the upper body is represented as:
Figure BDA00029324906400000321
a further improvement of the invention is that the energy profit is expressed as:
Figure BDA0002932490640000041
in the formula (I), the compound is shown in the specification,
Figure BDA0002932490640000042
is the power demand of the producer or consumer,
Figure BDA0002932490640000043
is a natural gas demand of the producer or consumer,
Figure BDA0002932490640000044
is the heat energy requirement of the producer and consumer; u shapep i,t(. is an electric utility function, Uf i,t(. is a natural gas utility function, Uh i,t(. is a thermal energy utility function;
Figure BDA0002932490640000045
is the cost of acquiring the energy storage resources,
Figure BDA0002932490640000046
respectively, the electricity, gas and heat prices obtained by the producers receiving the energy sharing,
Figure BDA0002932490640000047
is the total profit;
assuming that the utility function is a convex non-decreasing function, it is expressed as:
Figure BDA0002932490640000048
in the formula, xii,t，τi,tFirst order coefficients and second order coefficients of the utility function, respectively;
the strategy space of the lower model is as follows:
Figure BDA0002932490640000049
the invention is further improved in that the non-cooperative game model is as follows:
G＝{N；{δpro}；{ψload}；{Fpro}；{FIES}} (10)
the model includes three elements, normalized as:
(1) participants
IES, DP, SP are 3 participants in the game, and the set is N ═ i, IES };
(2) policy
The 24 hour electricity, gas and heat price buying and selling strategy of the IES leader is represented in a matrix form as deltapro＝(λp,pro,λf,pro,λh,pro)；
Wherein, deltaproPrice policy, λ, representing IES operatorp,pro,λf,pro,λh,proRespectively in the form of a matrix of electricity, gas and heat sharing prices; the strategy of DP and SP is that the shared energy is represented by psi in matrix formload＝(Pload,Fload,Hload) Wherein ψloadLoad strategy representing the victims of origin and consumption, Pload,Fload,HloadRespectively in the form of a matrix of electrical, gas and thermal loads;
(3) profit
The profit of each participant is defined as an objective function;
optimal response strategy
Figure BDA0002932490640000051
Simplified to
Figure BDA0002932490640000052
Definition of
Figure BDA0002932490640000053
Is the set of strategies for the best response of all the victims; omega-omega1×Ω2×···×ΩIPolicy space defined as low-income person, whereload*∈Ω；
When all followers react best to the leader's policy, the game reaches Stackelberg equilibrium and the leader accepts the response; the following conditions are satisfied:
FIES(ψload*，δpro*)≥FIES(ψload*，δpro) (11)
Figure BDA0002932490640000054
the further improvement of the invention is that the specific process of solving the non-cooperative game model by using a distributed algorithm rather than a centralized optimization method is as follows: the profit and price are initialized, then IES price is generated randomly, and for each producer and consumer, a price signal delta is receivedproThen, the best response result is obtained, all the results are returned to the operator, and the profit F is calculatedIES.j(ii) a If the newly derived profit is large, the result is updated and value F is assignedIES*＝FIES,j，δpro*＝δpro(ii) a And if the newly obtained profit is small, performing variation updating on the price until the profit is the maximum and the price and the load are balanced.
Compared with the prior art, the invention has the following beneficial effects: the invention can integrate distributed power generation, producers and consumers and energy storage resources in a certain area, and coordinately participate in the operation of the comprehensive energy system which comprises the energy hub and can be converted in an energy sharing mode, thereby avoiding the waste of surplus energy and reducing the loss caused by the power grid. According to the non-cooperative game-based energy sharing method for the comprehensive energy system, the electric, gas and heat sharing price signals in a comprehensive energy operator can be obtained through an iterative game method through the non-cooperative game method, the energy using behaviors of users can be effectively guided, and the comfort level of the users is guaranteed; the energy efficiency can be improved and the optimal configuration of regional resources can be promoted by a method of integrating distributed resources and energy conversion through a comprehensive energy system; the space-time complementary characteristic of the distributed resources can be fully exerted through the concept of multi-energy sharing, and the social welfare space is enlarged.
Drawings
Fig. 1 is an integrated energy system energy sharing framework in accordance with the present invention.
Fig. 2 is a diagram of the balance electricity prices.
Fig. 3 is the equilibrium gas price.
FIG. 4 is a comparison of the load change of the person with qi production and consumption.
Fig. 5 is an energy hub dispatch thermal result.
Fig. 6 is a load comparison before and after gas sharing.
Detailed Description
The invention is further described in detail below with reference to the figures and specific examples.
Firstly, establishing a distributed electric heat energy sharing method which takes an energy concentrator as a center and can carry out energy conversion, and constructing a more universal comprehensive energy system main body; secondly, constructing a non-cooperative game model with a comprehensive energy operator as a leader and a producer and a destroyer as followers, and considering the benefit of the leader and the energy utilization efficiency of the followers in a multi-energy sharing mode; and then, based on the protection of the privacy of the participants, a distributed algorithm is adopted to solve the balance problem, an optimal strategy is obtained through master-slave interaction, and the balance of pursuing benefit maximization by the leader and pursuing utility maximization by the follower is realized. Finally, an example analysis verification is performed.
The method specifically comprises the following steps:
step I: establishing a comprehensive energy system sharing mechanism framework:
the comprehensive energy system comprises an energy concentrator, renewable energy sources, producers and consumers and can carry out information interaction and energy scheduling;
step II: establishing an upper layer model of comprehensive energy and a lower layer model of a producer and a consumer:
modeling an objective function of a comprehensive energy operator, and modeling an energy supplier and an energy consumer on an energy supply side and an energy consumer on an energy demand side to obtain an objective function with maximized lower-layer utility;
step III: establishing a non-cooperative game model:
simultaneously optimizing the demand response of electricity price, gas price, heat price and producers and consumers, wherein the upper layer is a leader, and the lower layer is a follower, so as to research a dynamic game model;
step IV: solving by a distributed algorithm:
based on the principles of opaque competition and privacy protection, a distributed algorithm rather than a centralized optimization method is used for solving.
Step V: simulation calculation:
an integrated energy system comprising 6 deputients and 1 energy concentrator is established for example analysis, and the effectiveness and the practicability of the method are verified.
The specific process is as follows:
1) establishing comprehensive energy system sharing mechanism framework
The comprehensive energy system integrates an energy concentrator, new energy and producers and consumers, takes the energy concentrator as an energy conversion center, and is used for cooperatively interconnecting a comprehensive energy system network to realize economical, efficient energy supply, scientific and reasonable energy utilization, and the specific architecture is shown in figure 1. The description of fig. 1 is specifically as follows.
The system comprises two parts, namely a producer user side and a consumer user side and an integrated energy operator. Wherein, the operator is the upper main part of game, can issue the price signal and inlay the energy concentrator device and carry out the conversion. The lying-in and lying-out persons are the lower-layer main bodies of the game and carry out self sharing, purchasing and selling adjustment according to the price signals. On the user side, some producers with excess energy as producers on the supply side tend to supply energy to users with energy demands on the periphery in an energy sharing mode, so that the transmission and distribution cost and loss can be reduced, and the limitation of the admission standard of market trading can be avoided; and the energy-deficient obstetrician can also reduce unnecessary reliability payment through energy sharing, thereby reducing the electricity consumption cost. In practice, it is difficult to just balance the energy supply and the energy demand. Therefore, an energy concentrator capable of converting energy is also arranged inside the comprehensive energy system operator to perform mutual conversion of electricity, gas and heat so as to make up for the gap of energy mismatch in space and time.
Meanwhile, the energy concentrator also integrates an electricity, gas and heat energy storage device to provide a buffer space. In addition, in the energy trading process, the operators also need to bear risks caused by price fluctuation and unbalanced supply and demand. Therefore, there are information interaction and electric energy and gas energy transfer between the integrated energy operators and the main network, and when the energy concentrator conversion cannot meet the load demand, the Integrated Energy System (IES) must purchase electricity and gas from the power grid and the gas grid at a high price. The heat energy is mainly provided on the spot in consideration of the large loss of heat energy conversion and transmission.
2) Operator upper layer model and lower layer model of producer and consumer
(1) Upper layer optimization
For the comprehensive energy system operator, the comprehensive energy system operator not only can participate in market trading, but also can interact with producers and consumers, and the objective function of the comprehensive energy system operator is to maximize the profit per se, and can be expressed as follows:
Figure BDA0002932490640000081
in the formula: fIESRepresents the profit of the comprehensive energy operator all day;
Figure BDA0002932490640000082
is the energy revenue sold to the producer or consumer during the time period t,
Figure BDA0002932490640000083
is the cost of purchase from the producer or consumer over time period t,
Figure BDA0002932490640000084
is the cost of interacting with the electricity market and the natural gas market.
Figure BDA0002932490640000085
Is the operating cost of the energy hub.
The above variables may be specifically expressed as:
Figure BDA0002932490640000086
Figure BDA0002932490640000087
Figure BDA0002932490640000088
Figure BDA0002932490640000089
in the formula (I), the compound is shown in the specification,
Figure BDA00029324906400000810
electricity, supply price of natural gas and heat energy, lambda, respectively, of the energy shared by the producers and consumerst p,s，λt f,s，λt h,sRespectively, the electricity, gas and heat prices that the producer receives for energy sharing.
Figure BDA00029324906400000811
Figure BDA00029324906400000812
Is the energy provided and consumed by the abortive i. Lambda [ alpha ]t EH,p,s,λt EH,p,b,λt EH,f,s,λt EH,f,bIs the trading price for the electricity and gas market at time t. Pt EH,s，Pt EH,b，Ft EH,s，Ft EH,bEnergy is marketed to compensate for imbalances between supply and demand.
Figure BDA00029324906400000813
Figure BDA00029324906400000814
And
Figure BDA00029324906400000815
and is energy charged and discharged from the storage device;
Figure BDA00029324906400000816
and
Figure BDA00029324906400000817
and is the unit cost of the storage device at time t.
The policy space of the upper layer can be expressed as:
Figure BDA0002932490640000091
(2) lower layer optimization
For the producers and the consumers, the invention considers the energy cost of the producers and the consumers and establishes the utility function to consider the actual utility of the producers and the consumers through energy consumption. In fact, as energy consumption increases, although the cost of energy consumption increases, utility also increases. Conversely, the producer and consumer reduce utility by reducing energy consumption, which correspondingly reduces energy costs. Therefore, making decisions considering the cost-utility relationship between the two is of more practical significance to the producer and the consumer. It can be expressed as:
Figure BDA0002932490640000092
in the formula (I), the compound is shown in the specification,
Figure BDA0002932490640000093
is the energy demand of the patients; u shapep i,t(·)，Uf i,t(·)，Uh i,t(. is a utility function;
Figure BDA0002932490640000094
is the cost of obtaining energy storage resources. Taking electricity as an example, assuming that the utility function is a convex non-decreasing function, it can be expressed as:
Figure BDA0002932490640000095
in the formula, xii,t，τi,tAre the coefficients of the utility function.
The strategy space of the lower model is as follows:
Figure BDA0002932490640000096
3) establishing a non-cooperative game model
The static upper layer model and the static lower layer model of the two main bodies in the game are respectively established in the steps, and then the dynamic game relation between the upper layer main body and the lower layer main body, namely between an operator and a prosumer and a consumer, is further explained. The optimization of the prenatal and xiators is based on the IES quotes and their optimization results will have an impact on the IES quotes. The energy transaction process conforms to the dynamic gaming situation of a master-slave hierarchical structure. Therefore, the invention establishes a non-cooperative game model taking IES as a leading part and a yield and consumption person (DP, SP) as a follower, and further researches the dynamic game relation. It can be expressed as:
G＝{N；{δpro}；{ψload}；{Fpro}；{FIES}} (10)
the model includes three elements, normalized as:
(1) participants
The IES, DP, and SP are 3 participants in the game, and the set is N ═ i, IES. Note that the set of DP and SP are written together.
(2) Policy
The 24 hour electricity, gas and heat price buying and selling strategy of the IES leader can be represented in a matrix form as followspro＝(λp,pro,λf,pro,λh,pro). Where delta isproPrice policy, λ, representing IES operatorp,pro,λf,pro,λh,proIn the form of a matrix of electricity, gas, heat share prices, respectively. The strategy of DP and SP is to share energy, also seen as load. Can be expressed in matrix form as followsload＝(Pload,Fload,Hload). Here psiloadLoad strategy representing the victims of origin and consumption, Pload,Fload,HloadIn the form of a matrix of electrical, gas and thermal loads, respectively.
(3) Profit
The profit for each participant is defined as an objective function in step 2.
Optimal response strategy
Figure BDA0002932490640000101
Simplified to
Figure BDA0002932490640000102
Definition of
Figure BDA0002932490640000103
Is the set of strategies for the best response of all the victims. Omega-omega1×Ω2×···×ΩIPolicy space defined as low-income person, whereload*E.g. omega. When all followers react best to the leader's policy, the game reaches Stackelberg equilibrium and the leader accepts the response. At this time, the following conditions are satisfied:
FIES(ψload*，δpro*)≥FIES(ψload*，δpro) (11)
Figure BDA0002932490640000104
4) distributed algorithm solution
Conventional centralized optimization methods require detailed information about all participants, such as device parameters, energy preferences, etc. In a competitive power market, the information is not transparent and each participant needs to be optimized individually. To ensure privacy, the present invention uses a distributed solution. The specific algorithm flow is as follows. The profit, price are first initialized and then the IES price, such as
Figure BDA0002932490640000111
And published downward. The following steps are repeated: for each of the parity producers, a price signal delta is receivedproThereafter, the optimization procedure is started and the best response results are obtained. Then all the results are returned to the operator, and the advantages of the operator are startedTo program and calculate profit FIES.j. If the new profit is greater, the result is updated and the value assigned
Figure BDA0002932490640000112
＝FIES,j，
Figure BDA0002932490640000113
And if the newly obtained profit is smaller, performing variation updating on the price, and repeating the initialized steps again. And (4) until the profit is maximally unchanged, obtaining the balanced solution of the price and the load. At this time, the operator cannot obtain more profit by changing the price, and the user cannot obtain more utility by changing the load, and the optimal value is reached at this time.
5) Simulation calculation of model
The example includes an energy hub and an integrated energy system of 6 producers and consumers. At different moments, due to different energy utilization behaviors, the method sets three typical periods of peak, flat and valley, and analyzes the operation characteristics of different schemes. The maximum and minimum intervals of the load are respectively as follows: the lower limit of the electric load is 1.0656-1.25498 MW, the upper limit is 3.1968-4.41 MW, the lower limit of the gas load is 2.475-4.17 kcf/h, the upper limit is 7.641-12.2475 kcf/h, the lower limit of the heat load is 1.005-1.235 MBtu/h, and the upper limit is 2.97-3.705 MBtu/h. And finally obtaining a balanced solution of price and load through the back-and-forth iteration of the playing and the algorithm of the two parties in the specific steps. The first analysis of the equilibrium price illustrates the correctness of the invention. The electricity price refers to the electricity price provided by the local power grid during the use period. The IES operator makes price strategies in the upper limit range and the lower limit range to provide prices superior to the power grid for the supply and demand parties. In fig. 2, the fluctuation trend of the IES hour balance price is closer to the electricity consumption time price of the power grid, and there are two peaks at 11:00-13:00 and 18:00-21: 00. While in fig. 3, the natural gas price also exhibits similar but more intense pricing characteristics. During low-load periods, the price established by the IES operator is low, whereas during peak-load periods, the price signal issued by the IES operator is high, almost reaching the upper price limit (market price), and during flat-load periods, the price fluctuates considerably.
Fig. 4 compares the results of the air load of all users before and after the optimization, respectively. During peak hours, a 30% large drop in gas load occurs, subject to the upper price limit. Further, thermal power loads peak at 5:00-6:00 and 21:00-24:00, exhibiting reversed attributes over time. There was a 2-fold decline during the morning and almost a doubling during the afternoon. In overview, under price incentive, the load curve after demand response exhibits a "peak clipping and valley filling" feature. This shows that the proposed model is effective in improving the load distribution curve.
Fig. 5 is a scheduling result of the upper layer energy hub. From the plant point of view, the heat pump and boiler are used in the first and second places because of their higher efficiency. As for the cogeneration unit, there are two modes, one is heat-set power and the other is electric-set heat. Of these, only CHP2 (in hot-fix mode) was used for 3 periods, as heat is more scarce than electricity. The CHP is less efficient than the two due to the conversion and distribution ratio, and therefore generates less heat. In the energy storage device aspect, the upper bar of the axis is discharging and the lower bar is charging. The energy storage device is stored in a low-price period and released when the shared energy is insufficient, so that the cost is reduced. In fact, there are also electric gas-transfer devices in the energy centers, which can transfer electric power to the gas. However, it works the least well. Therefore, when the power is sufficient at 14:00-16:00 and the gas is insufficient, P2G is not used for storing electricity. In a word, the energy hub can realize the complementation of multiple energy sources.
To illustrate the scientificity of the sharing mechanism, the comparison of shared versus unshared was also compared. Fig. 6 shows the load change before and after (taking heat as an example) the DP participates in energy sharing. It can be seen that the load demand of the DP is met to a greater extent, which makes the use comfortable for the user. And because the share contract price of DP is lower than market selling price, is greater than the cost while the increased load utility, under the prerequisite of guaranteeing to use can the travelling comfort, has improved the economic nature of energy consumption.
In conclusion, it can be seen that the electricity, gas and heat sharing price signals inside the comprehensive energy operator can effectively guide the energy using behaviors of the producers and the consumers and improve the load curve; the energy efficiency can be improved and the optimal configuration of resources is promoted by integrating distributed resources and energy conversion through the comprehensive energy system; the space-time complementary characteristic of the distributed resources can be fully exerted through multi-energy sharing, and the social welfare space is enlarged. The effectiveness and the practicability of the invention can be seen from the calculation examples.

Claims (7)

1. A comprehensive energy system energy sharing method based on a non-cooperative game is characterized by comprising the following steps:
step I: establishing a comprehensive energy system sharing mechanism framework:
step II: establishing an upper layer model of the comprehensive energy and a lower layer model of a producer and a consumer according to a sharing mechanism framework of the comprehensive energy system:
step III: establishing a non-cooperative game model according to the comprehensive energy upper layer model and the lower layer model of the producer and the consumer:
step IV: and solving the non-cooperative game model by using a distributed algorithm rather than a centralized optimization method to obtain the price and the load, thereby realizing energy sharing.
2. The integrated energy system energy sharing method based on the non-cooperative game as claimed in claim 1, wherein the integrated energy system sharing mechanism framework comprises an energy hub, new energy and a prosumer and consummated persons; the comprehensive energy system comprises a producer user side and a consumer user side and a comprehensive energy operator; the operator is an upper-layer main body of the game, issues price signals and is embedded with energy concentrator equipment for conversion; the producers and consumers are the lower main body of the game and carry out self sharing purchase and sale adjustment according to the price signal; the energy hub comprises an electricity, gas and heat energy storage device.
3. The method as claimed in claim 2, wherein the objective function of the integrated energy system operator is to maximize the profit, expressed as:
Figure FDA0002932490630000011
in the formula: fIESRepresents the profit of the comprehensive energy operator all day;
Figure FDA0002932490630000012
is the energy revenue sold to the producer or consumer during the time period t,
Figure FDA0002932490630000013
is the cost of purchase from the producer or consumer over time period t,
Figure FDA0002932490630000014
is the cost of interacting with the electricity market and the natural gas market,
Figure FDA0002932490630000015
is the operating cost of the energy hub.
4. The method of claim 3, wherein the energy revenue sold to the producers and consumers in the time period t is
Figure FDA0002932490630000016
Cost of purchase from the producer or consumer during time period t
Figure FDA0002932490630000017
Cost of interaction between electricity market and natural gas market
Figure FDA0002932490630000018
Operating costs associated with energy hubs
Figure FDA0002932490630000019
The following were used:
Figure FDA00029324906300000110
Figure FDA00029324906300000111
Figure FDA0002932490630000021
Figure FDA0002932490630000022
in the formula, λt p,b，λt f,b，λt h,bElectricity, supply price of natural gas and heat energy, lambda, respectively, of the energy shared by the producers and consumerst p,s，λt f,s，λt h,sRespectively, the electricity, gas and heat prices obtained by the producers receiving the energy share;
Figure FDA0002932490630000023
Figure FDA0002932490630000024
respectively the electricity, gas and heat provided and consumed by the producer and consumer i; lambda [ alpha ]t EH,p,s,λt EH,p,b,λt EH,f,s,λt EH,f,bThe prices of buying and selling electricity and natural gas in the market at the moment t are respectively; pt EH,s，Pt EH,b，Ft EH,s，Ft EH,bElectric energy and gas energy which are bought and sold in the market respectively;
Figure FDA0002932490630000025
and
Figure FDA00029324906300000217
energy from charging and discharging, gas and heat, respectively, from the storage device;
Figure FDA0002932490630000026
Figure FDA0002932490630000027
and
Figure FDA0002932490630000028
the unit cost of storing electricity, gas and heat by the storage device at time t;
the policy space of the upper body is represented as:
Figure FDA0002932490630000029
5. the integrated energy system energy sharing method based on the non-cooperative game as claimed in claim 2, wherein the profit of the energy is as follows:
Figure FDA00029324906300000210
in the formula (I), the compound is shown in the specification,
Figure FDA00029324906300000211
is the power demand of the producer or consumer,
Figure FDA00029324906300000212
is a natural gas demand of the producer or consumer,
Figure FDA00029324906300000213
is the heat energy requirement of the producer and consumer; u shapep i,t(. is an electric utility function, Uf i,t(. is a natural gas utility function, Uh i,tIs a heat energy utility functionCounting;
Figure FDA00029324906300000214
is the cost of acquiring energy storage resources, λt p,s，λt f,s，λt h,sRespectively, the electricity, gas and heat prices obtained by the producers receiving the energy sharing,
Figure FDA00029324906300000215
is the total profit;
assuming that the utility function is a convex non-decreasing function, it is expressed as:
Figure FDA00029324906300000216
in the formula, xii,t，τi,tFirst order coefficients and second order coefficients of the utility function, respectively;
the strategy space of the lower model is as follows:
Figure FDA0002932490630000031
6. the integrated energy system energy sharing method based on the non-cooperative game as claimed in claim 1, wherein the non-cooperative game model is:
G＝{N；{δpro}；{ψload}；{Fpro}；{FIES}} (10)
the model includes three elements, normalized as:
(1) participants
IES, DP, SP are 3 participants in the game, and the set is N ═ i, IES };
(2) policy
The 24 hour electricity, gas and heat price buying and selling strategy of the IES leader is represented in a matrix form as deltapro＝(λp,pro,λf,pro,λh,pro)；
Wherein, deltaproPrice policy, λ, representing IES operatorp,pro,λf,pro,λh,proRespectively in the form of a matrix of electricity, gas and heat sharing prices; the strategy of DP and SP is that the shared energy is represented by psi in matrix formload＝(Pload,Fload,Hload) Wherein ψloadLoad strategy representing the victims of origin and consumption, Pload,Fload,HloadRespectively in the form of a matrix of electrical, gas and thermal loads;
(3) profit
The profit of each participant is defined as an objective function;
optimal response strategy
Figure FDA0002932490630000032
Simplified to
Figure FDA0002932490630000033
Definition of
Figure FDA0002932490630000034
Is the set of strategies for the best response of all the victims; omega-omega1×Ω2×···×ΩIPolicy space defined as low-income person, whereload*∈Ω；
When all followers react best to the leader's policy, the game reaches Stackelberg equilibrium and the leader accepts the response, satisfying the following condition:
FIES(ψload*，δpro*)≥FIES(ψload*，δpro) (11)
Figure FDA0002932490630000041
7. the method as claimed in claim 1, wherein the method comprises the step of sharing the energy of the integrated energy system based on the non-cooperative gameThe specific process of solving the non-cooperative game model by using a distributed algorithm rather than a centralized optimization method is as follows: the profit and price are initialized, then IES price is generated randomly, and for each producer and consumer, a price signal delta is receivedproThen, the best response result is obtained, all the results are returned to the operator, and the profit F is calculatedIES.j(ii) a If the newly derived profit is large, the result is updated and value F is assignedIES*＝FIES,j，δpro*＝δpro(ii) a And if the newly obtained profit is small, performing variation updating on the price until the profit is the maximum and the price and the load are balanced.
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Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114091759A (en) * 2021-11-23 2022-02-25 山东大学 Multi-layer game optimization operation method and system for comprehensive energy system
CN114266391A (en) * 2021-12-21 2022-04-01 华北电力大学 Comprehensive energy system multi-main-body regulation and control method and device considering network cost
CN115204562A (en) * 2022-05-10 2022-10-18 浙江工业大学 Interconnected micro energy network distributed collaborative optimization scheduling method and system considering multi-energy sharing
CN116070754A (en) * 2023-01-16 2023-05-05 山东大学 Multi-main-body comprehensive energy system optimization operation method and system considering energy sharing

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114091759A (en) * 2021-11-23 2022-02-25 山东大学 Multi-layer game optimization operation method and system for comprehensive energy system
CN114266391A (en) * 2021-12-21 2022-04-01 华北电力大学 Comprehensive energy system multi-main-body regulation and control method and device considering network cost
CN115204562A (en) * 2022-05-10 2022-10-18 浙江工业大学 Interconnected micro energy network distributed collaborative optimization scheduling method and system considering multi-energy sharing
CN116070754A (en) * 2023-01-16 2023-05-05 山东大学 Multi-main-body comprehensive energy system optimization operation method and system considering energy sharing

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