Method for micro energy network capacity optimization planning based on economic-environment and energy dynamic pricing double game

Abstract

The application discloses an economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, which comprises the following steps: framework design of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, establishment of an upper-layer environment-economy evolution game planning model, establishment of a lower-layer energy dynamic pricing strategy master-slave game model and establishment of a multi-energy transformation coupling hub model. The method can consider the conflict relationship between economic benefits and environmental benefits in the planning problem, consider the dynamic characteristics of energy prices, balance the conflicting interests between micro energy network operators and users, improve the planning efficiency and economic benefits of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning, effectively reduce the total planning cost, balance the peak-valley difference of user load, improve the energy utilization efficiency, reduce the emission of greenhouse gas CO2 and air pollutants NO x , and realize the environmental value. The method has the advantages of scientific rationality, strong applicability and good effect.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of micro energy grid (MEG) capacity optimization planning, and is a micro energy grid capacity optimization planning method based on economic-environmental and energy dynamic pricing two-level game, which is applied to the joint planning of micro energy grid distributed generation (DG), multi-energy conversion elements, energy storage system (ESS) and thermal storage system (TSS) and the like in a multi-energy coupling micro energy grid. BACKGROUND

[0002] With the increasing global energy demand and the pressure brought by environmental protection demand, the energy pattern is developing from traditional fossil energy to new energy pattern of balanced development and complementary utilization of renewable energy. Micro energy grid, as a small comprehensive energy supply system integrating energy interconnection, conversion, coupling and storage, has become a research hotspot. Micro energy grid is the basic unit and important component of energy internet, which emphasizes the cascade utilization of energy and the coupling and conversion of multi-energy, can effectively improve the utilization rate of energy, reduce the cost of energy use, and reduce the pollution to the environment. Reasonable unified planning of distributed power, multi-energy conversion elements and energy storage and heat storage in micro energy grid can fully consider the coupling and complementary relationship of various forms of energy, improve the asset utilization efficiency and reduce the total social cost.

[0003] According to the energy demand, the energy selling wholesale price of the micro energy grid will change with time. However, in most cases, the micro energy grid operator charges a fixed price to the consumers, while the price fluctuation is borne by the energy operator. Since the consumers are not affected by the change of wholesale price, their demand shows a sharp fluctuation, with a low valley at night and a high peak during the day. These fluctuations reduce the energy supply reliability, system efficiency and the profit of the energy operator. The conflict of interests between the micro energy grid and the users is a problem that cannot be ignored in the planning of micro energy grid. In the existing research on energy dynamic pricing, only the fluctuation of electricity price is analyzed, while the fluctuation of heat, cold and other energy prices is ignored, and there is a lack of analysis of energy price fluctuation in the planning scenario. At the same time, the production of various energy forms is one of the main sources of greenhouse gases and air pollutants, and the micro energy grid operator invests a large amount of energy equipment in the hope of obtaining more economic returns, which is in conflict with environmental protection. It is very important to reasonably plan the investment and construction of micro energy grid to balance the economic and environmental conflicts in planning.

[0004] Game theory is an important tool to capture the complex strategic interactions among market participants and to analyze the strategic issues involving multiple independent participants. In the analysis of the dynamic pricing of energy involved in the optimization planning of micro energy network, there are multiple conflict relationships between the micro energy network operators and the user groups, the economic cost and the environment of the planning, etc. In order to better analyze the interaction relationship between the subjects, a double-layer game planning method is established through the idea of game theory, and a more reasonable planning scheme is obtained. SUMMARY

[0005] The purpose of the present application is to overcome the complexity of multiple conflict relationships in the existing micro energy network capacity optimization planning, and to provide a scientific and reasonable, highly applicable, effective, and economic and environmental conflict relationship considering the dynamic characteristics of energy price, balancing the conflicting interests between micro energy network operators and users, improving the planning efficiency and economic efficiency of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method.

[0006] The technical scheme adopted to achieve the purpose of the present application is a double-layer game micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing, characterized in that it comprises the following steps:

[0007] 1) Designing the framework of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method:

[0008] The micro energy network is responsible for the supply of electricity, heat and cold load in the responsible area, the construction of the micro energy network is responsible by the micro energy network operator, and the income is obtained by surplus electricity on the network and energy sales to users in the area. When the micro energy network cannot meet the regional electricity load demand, the electricity is purchased from the power grid company to supplement the supply of regional electricity load. Dynamic energy pricing and environmental-economic conflict of planning are introduced into the micro energy network planning, and a double-layer game planning micro energy network capacity optimization planning method based on game theory is proposed. For the upper planning, a multi-strategy set evolutionary game theory micro energy network environmental-economic conflict planning considering bounded rational decision is proposed. Then considering the comprehensive energy demand response characteristics, the micro energy network operation income and user energy cost and satisfaction are gamed based on the principal-agent game in the lower pricing layer, and a new energy dynamic pricing strategy is obtained.

[0009] 2) Establishing the upper environmental-economic evolutionary game planning model:

[0010] The upper layer is the planning layer. In the optimization planning of the key equipment of the micro energy network, in addition to the economic problem of cost and benefit, the greenhouse gas and air pollutants discharged by the multi-energy conversion equipment, including the combined heat and power unit and the gas boiler, are also worth noting. The main advantage of the micro energy network as the terminal of comprehensive energy utilization is to maximize the comprehensive utilization of energy and the energy conversion equipment to achieve the maximum utilization of energy. The environmental value is added to the planning layer to ensure the regional energy supply and economy, and to achieve environmental energy supply. The upper layer takes the economy and environment of the micro energy network planning as the game subject, considers the limited rational decision of the game subject, and realizes the goal of maximizing the economy of the micro energy network and minimizing the CO2, NO x and other gas emissions in a year through the evolutionary game method of multi-strategy set.

[0011] (2.1) Participants in the evolutionary game of the planning layer: planning economic subject and planning environmental subject.

[0012] (2.2) Strategy space of the evolutionary game of the planning layer:

[0013] Let be the strategy space of the economic subject, be the strategy space of the environmental subject, and N represent the number of element installations in the micro energy network. Then

[0014]

[0015]

[0016] where N1 represents the number of the first element installation; N i represents the number of the i-th element installation.

[0017] (2.3) Payment function of the evolutionary game of the planning layer:

[0018] (a) Payment function of the economic subject

[0019] In the payment function of the cost and benefit of the micro energy network, the cost part considers the installation cost, operation and maintenance cost, and equipment replacement cost of the elements of the micro energy network construction, and the benefit part considers the benefit of selling electricity, heat, and cold energy to users after the construction of the micro energy network. The payment function of the cost and benefit of the micro energy network is specifically represented as formula (1),

[0020]

[0021] wherein is the payment of the planning economic subject of the micro energy network; C invs is the total annual installation cost of the elements to be planned of the micro energy network; C om is the annual operation and maintenance cost of the elements to be planned of the micro energy network; C re is the annual equipment replacement cost; and C purThe micro energy network sells energy and gains revenue;

[0022] (b) Environmental subject payment function:

[0023] The environmental impact of the micro energy network is mainly caused by multi-energy conversion elements, including CO2, NO x emissions from combined heat and power units and gas boilers, x The emissions of CO2, NO x are proportional to the power output of multi-energy conversion elements including combined heat and power units and gas boilers,

[0024]

[0025] In the formula, is the environmental subject payment of the micro energy network; is the CO2 emission of the micro energy network; is the NO x emission of the micro energy network; is the CO2 emission quota of the micro energy network;

[0026] (c) Replicator dynamic equation:

[0027] In the evolutionary game analysis, the replicator dynamic equation of the participants needs to be established to analyze the evolutionary state of the participants in the game. The replicator dynamic equations of the two subjects are established as formula (3) and formula (4),

[0028]

[0029]

[0030] In the formula, is the proportion of individuals adopting strategy S k of the total individuals, f1 k is the fitness function of the economic subject; is the evolutionary state of the economic subject's strategy selection; k is the kth strategy; is the proportion of individuals adopting strategy S g of the total individuals, is the fitness function of the economic subject; is the evolutionary state of the economic subject's strategy selection; g is the gth strategy;

[0031] 3) Establish the lower energy dynamic pricing strategy master-slave game model:

[0032] The lower layer is the energy dynamic pricing layer. Considering the electricity, heat and cold energy produced by the micro energy network, most of which is used to meet internal demand, the internal energy conversion and utilization is relatively independent. Therefore, the cold, heat and electricity prices of the system can be determined by the micro energy network operator according to the real-time operation and demand. When determining the optimal energy price, the micro energy network operator faces uncertainties from external energy supply price, internal renewable energy generation output and load demand. Different energy prices will also affect the operation and revenue of the micro energy network. Usually, the demand side only considers the interests of the micro energy network operator to adjust the demand response strategy, ignoring the price response of the user end. The level of load response of the end user will be directly determined by the energy price, which will also affect the economic efficiency of the system. Therefore, there is a natural contradiction between the two. In the lower layer, the micro energy network operator and the user are the main game subjects. The operator maximizes its net revenue by optimizing the energy purchasing strategy, managing the operation state of the owned equipment and formulating the dynamic energy buying / selling price. The user reasonably arranges the user energy strategy according to the energy price formulated by the operator to reduce the total cost of various energy purchased. The operator has the priority to decide as the management party. The game between the operator and the user agent can be described as a master-slave game.

[0033] (3.1) Participants of the master-slave game in the pricing layer: micro energy network operator and user

[0034] (3.2) Strategy space of the master-slave game in the pricing layer:

[0035] For the strategy space of the micro energy network operator, For the strategy space of the user, c e c is the electricity price, h c is the heat price, c c is the cold price,

[0036]

[0037]

[0038] (3.3) Payment function of the pricing layer:

[0039] (a) Payment function of the micro energy network operator,

[0040] The micro energy network operator wants to maximize the profit, minimize the load deviation and fulfill its obligation to serve the public and meet the electricity demand of the user,

[0041] Therefore, the payment function of the micro energy network operator is the profit of the micro energy network operator, which includes the revenue of selling electricity, heat and cold and the revenue of interacting with the power grid company, i.e.

[0042]

[0043] where d e , d h , d c are the electricity, heat, and cold energy demand, respectively; P grid is the power exchange between the micro energy grid and the grid company; c grid is the grid price of the micro energy grid and the grid company; P gas is the micro energy grid's natural gas purchase amount, c gas is the micro energy grid's natural gas purchase price; t is the unit time; T is the time period; f L is the load fluctuation function, which is the cost borne by the micro energy grid operator, and should be as low as possible in an ideal case so as to make the micro energy grid operator have a predictable demand pattern that they can always meet;

[0044] (b) the user payment function,

[0045] Consumers want to maximize their satisfaction while minimizing the cost of their purchased energy such as electricity, heat, and cold. Their utility function is the negative of the company cost function, expressed as

[0046]

[0047] where S k is the user satisfaction function;

[0048] 4) Establish a multi-energy conversion coupling hub model:

[0049] The energy production equipment used by the micro energy grid system includes energy coupling equipment and energy-using equipment. The micro energy grid system also contains the conversion between different energies, and a multi-energy conversion coupling hub model needs to be established to describe the functional relationship between the multi-energy input and the multi-energy output in the micro energy grid,

[0050] L + S = CP (7)

[0051] The input and output parts of energy are represented by P = [P1 P2...P m ] and L = [L1 L2...L n ], respectively, the coupling matrix C represents the energy conversion relationship from input to output, the elements of the coupling matrix are coupling factors c ij , which represent the ratio of the jth form of energy output to the ith form of energy input, and S is a correction equation used to correct the influence of energy storage and heat storage energy storage devices on the multi-energy conversion coupling hub.

[0052] The application discloses a micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing double-layer game, which comprises the following steps: framework design of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method, establishment of an upper-layer environment-economy evolution game planning model, establishment of a lower-layer energy dynamic pricing strategy master-slave game model and establishment of a multi-energy conversion coupling hub model, and the like. x The application can effectively reduce the total planning cost, balance the peak-valley difference of user load, improve energy utilization efficiency, reduce the emission of greenhouse gas CO2 and air pollutant NO BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 Fig. 1 is a framework diagram of the micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing double-layer game;

[0054] Figure 2 Fig. 2 is a structure diagram of the micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing double-layer game;

[0055] Figure 3 Fig. 3 is a structure diagram of the micro energy network system;

[0056] Figure 4 Fig. 4 is a comparison diagram of the transition season typical day electricity price;

[0057] Figure 5 Fig. 5 is a comparison diagram of the transition season typical day electricity load response;

[0058] Figure 6 Fig. 6 is a comparison diagram of the heating season typical day heat price;

[0059] Figure 7 Fig. 7 is a comparison diagram of the cooling season typical day cooling price. DETAILED DESCRIPTION

[0060] Reference Figure 3 The micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing double-layer game is analyzed by taking the micro energy network system structure as an example, and the elements to be planned include: a photovoltaic power supply element, a combined heat and power unit, a gas boiler, an electric refrigerator, a lithium bromide absorption refrigerator, an energy storage battery system and a heat storage system.

[0061] Reference Figure 1 and Figure 2The application discloses a micro energy network capacity optimization planning method based on economic-environment and energy dynamic pricing double-layer game, which comprises the following steps:

[0062] 1) Framework design of the economic-environment and energy dynamic pricing double-layer game micro energy network capacity optimization planning method:

[0063] The micro energy network is responsible for the supply of electric, heat and cold loads in a region, the construction of the micro energy network is responsible by a micro energy network operator, and the micro energy network operator obtains income by surplus electricity grid connection and energy sales to users in the region; when the micro energy network cannot meet the regional electric load demand, the micro energy network operator purchases electricity from a large power grid company to supplement the regional electric load; dynamic energy pricing and the environmental-economic conflict in planning are introduced into the micro energy network planning, a double-layer game planning micro energy network capacity optimization planning method based on game theory is proposed, a multi-strategy set evolutionary game theory micro energy network environmental-economic conflict planning considering limited rational decision-making is proposed for the upper layer planning, then the comprehensive energy demand response characteristics are considered, the master-slave game is used to game the micro energy network operation income and user energy consumption cost and satisfaction in the lower layer pricing layer, and a new energy dynamic pricing strategy is obtained.

[0064] 2) Upper layer environmental-economic evolutionary game planning model:

[0065] The upper layer is a planning layer, in the micro energy network key equipment optimization planning, in addition to the economic problems of cost and benefit, the greenhouse gas and air pollutants discharged by the multi-energy conversion equipment including heat and power cogeneration units and gas boilers are also worth noting; the main advantage of the micro energy network as a terminal of comprehensive energy utilization is to comprehensively utilize various energy and multi-energy conversion equipment to maximize energy comprehensive utilization; the environmental value is added in the planning layer to realize environmental protection energy supply under the premise of regional energy supply and economy, the economic and environmental planning of the micro energy network is taken as a game subject, the limited rational decision-making of the game subject is considered, and the multi-strategy set evolutionary game method is used to realize the target of maximizing the economy of the micro energy network and minimizing the CO2, NOx and other gas emissions in a year, x

[0066] (2.1) Participants in the evolutionary game of the planning layer: planning economic subject and planning environmental subject;

[0067] (2.2) Evolutionary game strategy space of the planning layer:

[0068] Let be the economic subject strategy space, be the environmental subject strategy space, N represents the number of element installations in the micro energy network, and then

[0069]

[0070] ​

[0071] Where N1 represents the number of the first component installed; N i This indicates the number of times the i-th component is installed;

[0072] (2.3) Evolutionary game payoff function at the planning level:

[0073] (a) Pay functions of economic agents

[0074] In the cost-benefit payment function of a microgrid, the time scale is equivalent years. The cost part considers the installation cost, operation and maintenance cost, and equipment replacement cost of each component in the construction of the microgrid, while the revenue part considers the revenue from the sale of electricity, heat, and cooling energy to users after the construction of the microgrid. The specific expression of the cost-benefit payment function of the microgrid is as shown in equation (1).

[0075]

[0076] In the formula, Payment for the economic entities planning micro-energy grids; C invs The total annual installation cost of planned components for microgrids; C om Annual operation and maintenance costs of components to be planned for microgrids; C re Annual equipment replacement cost; C pur Revenue from selling energy through micro-energy grids;

[0077] (b) Environmental agent payoff function:

[0078] The environmental impact of microgrids mainly stems from multi-energy conversion elements, including CO2 and NO produced by combined heat and power units and gas-fired boilers. x CO2, NO x Emissions are proportional to the power output of multiple energy conversion elements, including combined heat and power units and gas-fired boilers.

[0079]

[0080] In the formula, Payments are made to the main environmental entities of the micro-energy grid; CO2 emissions from microgrid operation; For microgrid operation NO x Emissions; CO2 emission quotas for microgrids;

[0081] (c) Replicator dynamic equations:

[0082] In evolutionary game analysis, it is necessary to establish the replicator dynamic equations of the participants in order to analyze their evolutionary state in the game. The replicator dynamic equations of the two subjects are established separately, as shown in equations (3) and (4).

[0083]

[0084]

[0085] wherein, is the proportion of individuals adopting strategy S k is the proportion of individuals adopting strategy S k is the fitness function of the economic agent; is the evolutionary state of the economic agent choosing strategy; k is the kth strategy; is the proportion of individuals adopting strategy S g is the proportion of individuals adopting strategy S is the fitness function of the economic agent; is the evolutionary state of the economic agent choosing strategy; g is the gth strategy;

[0086] 3) Establishing the lower energy dynamic pricing strategy master-slave game model:

[0087] The lower layer is the energy dynamic pricing layer. Considering the electricity, heat, and cold energy produced by the micro energy network, most of which is used to meet internal demand, the internal energy conversion and utilization is relatively independent. Therefore, the cold, heat, and electricity prices of the system can be determined by the micro energy network operator according to the real-time operation and demand. When determining the optimal energy price, the micro energy network operator faces uncertainties from external energy supply prices, system internal renewable energy generation output, and load demand. Different energy prices will also affect the micro energy network operation and revenue. Usually, the demand side uncertainty only considers the interests of the micro energy network operator to adjust the demand response strategy, ignoring the price response of the user end. The level of the terminal user's load response will be directly determined by the energy price, which will also affect the economic efficiency of the system operation. Therefore, there is a natural contradiction between the two. In the lower layer, the micro energy network operator and the user are the game subjects. The operator maximizes its net income by optimizing the energy purchasing strategy, managing the operation state of the owned equipment, and formulating the dynamic energy buying / selling price. The user reasonably arranges the user energy strategy according to the energy price formulated by the operator to reduce the total cost of various energy purchased. The operator has the priority to decide as the management party. The game between the operator and the user agent can be described as a master-slave game.

[0088] (3.1) Pricing layer master-slave game participants: micro energy network operator, user;

[0089] (3.2) Pricing layer master-slave game strategy space:

[0090] is the micro energy network operator strategy space, is the user strategy space, c e is the electricity price, c hThermal price, c c Cold price,

[0091]

[0092]

[0093] (3.3) Pricing tier payment function:

[0094] (a) Micro energy grid operator payment function,

[0095] The micro energy grid operator wants to maximize profit, minimize load deviation, and fulfill its obligation to serve the public and meet the needs of power users,

[0096] Therefore, the payment function of the micro energy grid operator is its profit, which includes the revenue from selling electricity, heat, and cold, as well as the revenue from interacting with the grid company, i.e.

[0097]

[0098] In the formula, d e , d h , d c are the electricity demand, heat demand, and cold energy demand, respectively; P grid is the power interaction between the micro energy grid and the grid company; c grid is the on-grid electricity price of the micro energy grid and the grid company; P gas is the natural gas purchase quantity of the micro energy grid, c gas is the unit price of natural gas purchased by the micro energy grid; t is the unit time; T is the time period; f L is the load fluctuation function, which is the cost borne by the micro energy grid operator, and ideally should be as low as possible so that the micro energy grid operator has a predictable demand pattern that they can always meet;

[0099] (b) User payment function,

[0100] Consumers want to maximize their satisfaction while minimizing the cost of purchasing energy such as electricity, heat, and cold. Their utility function is the negative of the company's cost function, expressed as

[0101]

[0102] In the formula, S k is the user satisfaction function;

[0103] 4) Establish a multi-energy conversion coupling hub model:

[0104] The energy production device used by the micro energy network system includes: energy coupling device, energy using device, the micro energy network system also contains the transformation between different energies, and a multi-energy transformation coupling hub model needs to be established to describe the function relationship of multi-energy input and multi-energy output in the micro energy network,

[0105] L+S=CP (7)

[0106] The input and output parts of the energy are respectively represented by P=[P1 P2...P m ] and L=[L1 L2...L n ], the coupling matrix C represents the energy conversion relationship from input to output, the elements of the coupling matrix are coupling factors c ij , which represent the ratio of the jth form of energy output to the ith form of energy input, and S is a correction equation used to correct the influence of energy storage and heat storage energy storage devices on the multi-energy transformation coupling hub.

[0107] Referring to Figure 2 the micro energy network system, a multi-energy transformation coupling hub model is established. The input energy includes electric energy and natural gas, and the electric energy, thermal energy and cold energy are output from the micro energy network through energy conversion elements such as combined heat and power units, gas boilers, absorption chillers and electric chillers. The relationship between the input matrix P and the output matrix L is as follows:

[0108]

[0109]

[0110]

[0111] In the formula, L e , L h , L c are electric, thermal and cold loads; P grid is the electric power of the power grid tie line; P PV is the total output of the photovoltaic; is the electric output of the combined heat and power unit; E EC is the electric power absorbed by the electric chiller; P ES , P TS are the total output of the electric storage and heat storage devices (positive for charging and negative for discharging); P EC is the output of the electric chiller; P AC is the output of the absorption chiller; P gas is the natural gas power used by the multi-energy system; P GB is the output of the gas boiler; is the thermal output of the combined heat and power unit; is the heat power absorbed by the absorption chiller; λ is the natural gas distribution coefficient; w is the thermal energy distribution coefficient; The electricity generation efficiency of a combined heat and power unit, the heat generation efficiency of a gas boiler, and the heat generation efficiency of a combined heat and power unit, respectively. The energy efficiency ratio of an electric refrigerator to an absorption refrigerant.

[0112] The matrix form is:

[0113]

[0114] In order to illustrate the effectiveness of the economic-environmental and energy dynamic pricing based double-layer game micro energy network capacity optimization planning method, three different planning scenarios are set. Scenario 1: cost-benefit single-layer planning under fixed market cold, heat and electricity price scenario; Scenario 2: micro energy network construction economic and environmental single-layer game under fixed market cold, heat and electricity price scenario; Scenario 3: double-layer game planning considering upper-layer energy price uncertainty game and lower-layer cost-benefit and environmental benefit game.

[0115] Table 1 shows the planning results under different planning scenarios.

[0116]

[0117] In Table 1, the planning results under three planning scenarios are listed.

[0118] Table 2 shows the cost economy comparison of planning results under different planning scenarios.

[0119]

[0120] In Table 2, the cost economy comparison of planning results under three planning scenarios is listed. In Table 2, it can be seen that in the scenario 3 of the economic-environmental and energy dynamic pricing based double-layer game micro energy network capacity optimization planning method, the energy equipment installation cost of the micro energy network side is 5.21*10 6 , the cost of purchasing energy from the power grid company and the natural gas company is 1.053*10 5 , and the total cost is 6.69*10 6 . Compared with the environmental-economic planning scenario 2 under fixed energy price, the total planning cost is reduced by 1.2*10 5 , and compared with scenario 3, the total planning cost is reduced by 4*10 4 . It can be seen that the economic-environmental and energy dynamic pricing based double-layer game micro energy network capacity optimization planning method can reduce the energy purchase cost and improve the energy sales benefit.

[0121] Table 3 shows the annual gas emission of the micro energy network under different planning scenarios.

[0122]

[0123] Table 3 lists the environmental emissions of harmful gases under three planning scenarios. As can be seen from Table 3, Scenario 3 shows lower nitrogen oxide and carbon emissions compared to Scenario 1. x Emissions were reduced by 4.87 kg, and the difference between CO2 emissions and the CO2 emission quota decreased from 2.35 × 10⁻⁶ in Scenario 1. 4 kg decreased to 1.6 × 10 3 kg. It is evident that the microgrid capacity optimization planning method based on a two-layer game theory approach involving economic, environmental, and energy dynamic pricing can effectively reduce greenhouse gas CO2 and environmental pollutant NO. x The emissions are beneficial to environmental protection.

[0124] Figure 4 A comparison of dynamic electricity prices on a typical day during the transition season. Figure 4 As can be seen, due to the flexibility of load, users adjust their load according to changes in electricity prices when consuming energy: During periods with higher electricity prices (segments 7 to 17), when the maximum load reaches 1105 kWh, which is considered peak load, users prioritize minimizing energy costs and adjust their non-essential load by approximately 2%. During periods with lower electricity prices (segments 1 to 6 and 23 to 24), users prioritize energy consumption experience, leading to increased user satisfaction and a slight shift of peak load to these periods (up to 4%). During periods 9 to 12, when electricity prices are relatively high, reaching a maximum of 1.18 yuan / kWh, users make minor load adjustments (1%) while balancing energy consumption experience and payment costs.

[0125] Figure 5 This chart compares user electricity load under different electricity prices on a typical day during the transition season. Figure 5 As can be seen, the initial electricity pricing strategy of the microgrid is formulated based on users' historical electricity consumption data. The graph shows that the Nash equilibrium electricity price is related to user load response. During periods of low load (periods 1-6 and 23-24), the microgrid appropriately lowers the electricity price to encourage user electricity consumption. During periods of high user load (periods 7-17), to prevent excessive user consumption, the electricity price is appropriately increased within the range of user satisfaction to promote peak shaving and valley filling. During periods 9-12, the microgrid increases the price at the expense of a small amount of load to increase revenue. Simultaneously, under dynamic pricing, user peak load is reduced by 9%, achieving a certain degree of peak shaving and valley filling, resulting in satisfactory results.

[0126] Figure 6 A chart comparing heat prices on a typical day during the heating season via the micro-energy network. Figure 6It can be seen that the original heat price is a fixed price, and the dynamic heat price obtained by the double game micro energy network capacity optimization planning method based on economic-environmental evolutionary game and master-slave game of energy dynamic pricing strategy realizes the maximization of the micro energy network operator's revenue on the basis of meeting the user satisfaction.

[0127] Figure 7 The micro energy network cold energy price comparison chart for a typical day representing the refrigeration season. Figure 7 It can be seen that the original cold energy price is a fixed price, and the dynamic cold energy price obtained by the double game micro energy network capacity optimization planning method based on economic-environmental evolutionary game and master-slave game of energy dynamic pricing strategy realizes the maximization of the micro energy network operator's revenue on the basis of meeting the user satisfaction.

[0128] Obviously, the above embodiments are only examples for clearly illustrating but not limiting the embodiments, and on the basis of the above description, other different forms of changes or variations can be made by those skilled in the art, and here, all the embodiments do not need to be exhausted, and the obvious changes or variations derived therefrom are still within the protection scope of the present application.

Claims

1. An economic-environmental and energy dynamic pricing double-layer game micro-grid capacity optimization planning method, characterized in that, It includes the following steps: 1) The framework design of the micro energy network capacity optimization planning method based on the economic-environmental and energy dynamic pricing two-level game: The micro energy network is responsible for the supply of electricity, heat and cold load in the region, and the construction of the micro energy network is responsible by the micro energy network operator, and the income is obtained by surplus electricity and energy sales to regional users, when the micro energy network cannot meet the regional electricity load demand, the electricity is purchased from the power grid company to supplement the regional electricity load; The dynamic energy pricing and the environmental-economic conflict in the planning are introduced into the micro energy network planning, and a two-level game planning method for micro energy network capacity optimization planning based on game theory is proposed, a multi-strategy evolutionary game theory for micro energy network environmental-economic conflict planning is proposed for the upper planning, considering the comprehensive energy demand response characteristics, the game between the micro energy network operation income and the user energy cost and satisfaction is based on the principal-agent game in the lower pricing layer, and a new energy dynamic pricing strategy is obtained; 2) Establish the upper environmental-economic evolutionary game planning model: The upper layer is the planning layer. In the optimization planning of the key equipment of the micro energy network, in addition to the economic problem of cost and benefit, the greenhouse gas and air pollutants discharged by the multi-energy conversion equipment, including the combined heat and power unit and the gas boiler, are also worth noting. The main advantage of the micro energy network as the terminal of the comprehensive energy utilization is to comprehensively utilize each energy and the multi-energy conversion equipment to maximize the comprehensive utilization of energy. The environmental value is added to the planning layer to ensure the regional energy supply and economy and to realize the environmental protection energy supply. The upper layer takes the economy and the environment of the micro energy network planning as the game subject, considers the limited rational decision of the game subject, and realizes the goal of maximizing the economy of the micro energy network and minimizing the CO2 and NOx gas emissions of the micro energy network throughout the year through the multi-strategy set evolution game method. x ​ (2.1) The participants of the planning layer evolutionary game: planning economic subjects and planning environmental subjects; (2.2) The strategy space of the planning layer evolutionary game: Let be the strategy space of economic agents, be the strategy space of environmental agents, N denotes the number of elements installed in the micro energy grid, then wherein N1represents the number of the first element mounting; N i represents the number of the i-th element mounting; (2.3) The payment function of the planning layer evolutionary game: (a) Economic subject payment function The payment function of the micro energy network cost and benefit is based on the equivalent year as the time scale, the cost part considers the installation cost, operation and maintenance cost and equipment replacement cost of the micro energy network construction, the benefit part considers the income of the micro energy network construction to the user sales of electricity, heat and cold energy, the specific expression of the micro energy network cost and benefit payment function is as formula (1), In the formula, is the payment of the economic subject of micro energy network planning; C invs is the total annual installation cost of the micro energy network element to be planned; C om is the annual operation and maintenance cost of the micro energy network element to be planned; C re is the annual equipment replacement cost; C pur is the energy sale income of the micro energy network; (b) Environmental subject payment function: The environmental impact of micro energy networks mainly comes from the multi-energy conversion elements, including combined heat and power units and gas boilers, which emit CO2 and NO x The amount is proportional to the power output of the micro energy network In the formula, CO2 emissions of the micro energy network environment subject; CO2 emissions of the micro energy network operation; NOx emissions of the micro energy network operation x CO2 emissions of the micro energy network operation; CO2 emissions quota of the micro energy network; (c) Replicator dynamic equation: In the evolutionary game analysis, the replicator dynamic equation of the participants needs to be established to analyze the evolutionary state of the participants in the game, the replicator dynamic equations of the two subjects are established respectively, as formula (3), formula (4), wherein, the proportion of individuals adopting strategy Sg to the total individuals, f2 k the proportion of individuals adopting strategy Sg to the total individuals, f2 k fitness function of the economic agent; evolutionary state of the economic agent selecting strategy; k is the kth strategy; the proportion of individuals adopting strategy Sg to the total individuals, f2 g fitness function of the economic agent; evolutionary state of the economic agent selecting strategy; g is the gth strategy; 3) Establish the lower energy dynamic pricing strategy principal-agent game model: The lower layer is the energy dynamic pricing layer. Considering the production of electricity, heat and cold energy in the micro energy grid, most of which is used to meet internal demand, the internal energy conversion and utilization is relatively independent. Therefore, the cold, heat and electricity prices of the system can be determined by the micro energy grid operator according to the real-time operation and demand. When determining the optimal energy price, the micro energy grid operator faces uncertainties from external energy supply prices, internal renewable energy generation and load demand. Different energy prices will also affect the operation and revenue of the micro energy grid. Usually, the demand side uncertainty only considers the interests of the micro energy grid operator to adjust the demand response strategy, while ignoring the price response of the user end. The level of energy price will directly determine the load response degree of the terminal user, and on the other hand, it will also affect the economic efficiency of the system operation. Therefore, there is a natural contradiction between the two. In the lower layer, the micro energy grid operator and the user are the main game subjects. The operator maximizes its net income by optimizing the energy purchasing strategy, managing the operation state of the owned equipment and formulating the dynamic energy buying / selling price. The user reasonably arranges the user energy strategy according to the energy price formulated by the operator to reduce the total cost of various energy purchased. The operator as the management party has the priority decision right. The game between the operator and the user agent can be described as a master-slave game. (3.1) Participants of the master-slave game in the pricing layer: micro energy grid operator and user; (3.2) Strategy space of the master-slave game in the pricing layer: for micro energy grid operators policy space, for users policy space, c e for electricity price, c h for heat price, c c for cold price, (3.3) Payment function of the pricing layer: (a) Payment function of the micro energy grid operator, The micro energy grid operator wants to maximize the profit, minimize the load deviation, and fulfill its obligations to serve the public and meet the needs of electricity users, Thus, the payment function of the micro energy grid operator is its profit, which includes the revenues from selling electricity, heat, and cold, as well as the revenues from interacting with the grid company, i.e. where d e , d h , d c are the power demand, heat demand, and cold energy demand, respectively; P grid is the power exchange between the micro energy grid and the grid company; c grid is the grid feed-in tariff for the micro energy grid; P gas is the natural gas purchase quantity for the micro energy grid, c gas is the natural gas purchase unit price for the micro energy grid; t is the unit time; T is the time period; f L is the load fluctuation function, which is the cost borne by the micro energy grid operator and should ideally be as low as possible so as to enable the micro energy grid operator to have a predictable demand pattern that they can always meet; (b) Payment function of the user, Consumers wish to maximize their satisfaction while minimizing the cost they pay for their electricity, heat, and cold energy purchases; their utility function is the negative of the company cost function, expressed as In the formula, S k is a user satisfaction function; 4) Establish a multi-energy conversion coupling hub model: The energy production equipment used by the micro energy grid system includes energy coupling equipment and energy using equipment. The micro energy grid system also includes the conversion between different energies. A multi-energy conversion coupling hub model is needed to describe the functional relationship between the input and output of multiple energies in the micro energy grid, L+S=CP (7) The input and output parts of the energy are represented by P = [P1 P2...P m ] and L = [L1 L2...L n ], respectively. The coupling matrix C represents the energy conversion relationship from the input to the output, and the elements of the coupling matrix are coupling factors c ij , which represent the ratio of the jth form of energy output to the ith form of energy input. S is a correction equation used to correct the influence of energy storage and heat storage energy storage devices on the multi-energy conversion coupling hub.

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