Rural energy system multi-agent collaborative planning method considering biomass transaction

Abstract

The invention discloses a rural energy system multi-subject collaborative planning method considering biomass transaction, and the method comprises the steps: building a rural energy system multi-subject collaborative planning model, analyzing the seasonal characteristics of rural region biomass, and building a biomass available amount model and a biogas production rate model of a biogas station; according to the biomass available amount model and the gas production amount model, in combination with the interest appeal and transaction mode of each subject, constructing an objective function and constraint model of a rural integrated energy system operator and a pumped storage power station operator; and according to the objective function and the constraint model, solving the multi-agent collaborative planning model to obtain an optimal planning scheme and a transaction mechanism of the rural energy system. According to the invention, initiative of each subject can be exerted, biomass resources are fully scheduled, collaborative planning operation of multiple subjects in a rural energy system is realized, and benefits of each subject are effectively guaranteed.

Description

Technical Field

[0001] The present invention belongs to the technical fields of rural energy system planning and multi-agent collaborative planning, and particularly relates to a multi-agent collaborative planning method for rural energy systems considering biomass trading. Background Art

[0002] At present, the energy supply in rural areas relies too much on rural distribution networks and traditional fossil fuels. Although it meets the energy demand in rural areas to a certain extent, there are obvious deficiencies in the diversification and sustainability of energy supply. Rural areas are rich in biomass resources, such as crop straws, livestock and poultry manure, etc., but the development and utilization of these resources have not been fully emphasized and effectively integrated. Existing energy system planning often ignores the seasonal characteristics and regional distribution differences of biomass resources and fails to incorporate them into the overall energy supply system for collaborative planning. In addition, the operation mode of rural energy systems is relatively single, lacking an energy trading and information sharing mechanism among multiple agents, making it difficult to achieve optimal allocation and efficient utilization of resources.

[0003] There are many problems in the resource utilization and operation mode of existing rural energy systems. First, the development and utilization of biomass resources lack systematic planning, resulting in serious resource waste and failing to effectively solve the environmental problems brought by agricultural waste. Second, rural energy systems fail to fully integrate the resource and interest demands of multiple agents and lack an effective collaborative mechanism, making it difficult for energy trading and information sharing among agents and difficult to achieve flexible scheduling and optimal allocation of resources. In addition, existing systems fail to make full use of the advantages of cascade hydropower resources in rural areas and cannot effectively solve the problems of new energy consumption and power balance. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes a multi-agent collaborative planning method for rural energy systems considering biomass trading to solve the problems existing in the above prior art.

[0005] To achieve the above object, in a first aspect, the present invention provides a multi-agent collaborative planning method for rural energy systems considering biomass trading, including:

[0006] Establishing a multi-agent collaborative planning model for rural energy systems, analyzing the seasonal characteristics of biomass in rural areas, and constructing a biomass availability model and a biogas production model for biogas stations;

[0007] According to the biomass availability model and the biogas production model, combining the interest demands and trading modes of each agent, constructing an objective function and a constraint model for rural integrated energy system operators and pumped storage power station operators;

[0008] Solve the multi-agent collaborative planning model according to the objective function and constraint model to obtain the optimal planning scheme and trading mechanism of the rural energy system.

[0009] Preferably, the process of establishing the multi-agent collaborative planning model of the rural energy system includes:

[0010] Integrate the rural integrated energy system and the pumped-storage power station, use electric energy and biomass trading as the link, and achieve collaborative planning and operation through the energy trading and information interconnection mechanism;

[0011] According to the differences in the agricultural industry, load conditions and investment operators in the regions where different operators are located, and participate in trading operations based on the principle of "self-use of self-produced energy and trading of surplus energy".

[0012] Preferably, the rural integrated energy system includes a biogas station, a biogas engine, a biogas boiler, photovoltaic, wind power, an electric boiler, an electric energy storage and a thermal energy storage device, and is reasonably configured according to the distribution of biomass resources in rural areas.

[0013] Preferably, the construction of the biomass available amount model includes:

[0014] According to the seasonal and periodic characteristics of agricultural activities in rural areas, analyze the seasonal output of agricultural biomass and construct a biomass available amount model;

[0015] Use the predicted crop yield and the predicted livestock inventory to estimate the total theoretical available amount of biomass, and calculate the cost model for IRES operators to collect local biomass.

[0016] Preferably, the construction of the biogas production amount model of the biogas station includes:

[0017] Calculate the biogas production amount of the biogas station per quarter according to the quality of various biomass materials input into the biogas station;

[0018] Based on the relationship between the construction capacity of the biogas equipment and the quarterly biogas production amount, determine the construction capacity of the biogas digester.

[0019] Preferably, the construction of the objective function and constraint model of the rural integrated energy system operator includes:

[0020] Take the annualized cost as the objective function, including the annualized construction cost, maintenance cost, energy trading cost, grid power purchase cost, straw purchase cost from biomass dealers, and biomass collection cost;

[0021] Set equipment construction constraints, equipment ramp-up constraints, power constraints and biomass constraints.

[0022] Preferably, the construction of the objective function and constraint model of the pumped-storage power station operator includes:

[0023] Taking the capacity rental fee and the electricity trading revenue as the objective function, including the leased capacity and the unit capacity rental cost;

[0024] Set the constraints on the power selling and power purchasing, to ensure that the energy storage leased capacity status of the pumped-storage power station operator is within a reasonable range.

[0025] Preferably, solving the collaborative planning model includes:

[0026] Equivalently transform the multi-agent Nash negotiation problem into an energy trading volume negotiation sub-problem and an energy trading price negotiation sub-problem;

[0027] Use the alternating direction method of multipliers to construct a distributed optimization framework, and iteratively solve the two sub-problems in turn to obtain the optimal planning scheme and trading mechanism of the energy system.

[0028] Preferably, the solution of the energy trading volume negotiation sub-problem includes:

[0029] By introducing Lagrange factors and penalty factors, transform the energy trading volume negotiation sub-problem into a distributed form of the augmented Lagrangian function;

[0030] Through iterative loop optimization of the energy trading strategies of each agent until the convergence condition is met, the solution of the energy trading volume negotiation sub-problem is completed.

[0031] Compared with the prior art, the present invention has the following advantages and technical effects:

[0032] The present invention provides a multi-agent collaborative planning method for rural energy systems considering biomass trading. First, establish a multi-agent collaborative planning model for rural energy systems, analyze the seasonal characteristics of biomass in rural areas, and construct a biomass availability model and a biogas production model for biogas stations; second, according to the biomass availability model and the biogas production model, combined with the interest demands and trading modes of each agent, construct the objective function and constraint model of the rural integrated energy system operator and the pumped-storage power station operator; finally, according to the objective function and constraint model, solve the multi-agent collaborative planning model to obtain the optimal planning scheme and trading mechanism of the rural energy system.

[0033] The present invention introduces multi-agent collaborative planning and biomass trading into the development planning of rural energy systems, can reasonably dispatch the agricultural waste resources with scale and easy transportation in rural areas, and through energy trading and information exchange among multiple agents, realize the flexible dispatch and collaborative planning operation of resources, and through the trading and utilization of biomass resources, achieve the diversification of energy supply in rural areas and solve the environmental problems caused by agricultural waste.

[0034] The rural energy system of the present invention establishes a collaborative mechanism based on the multi-agent game theory. Taking electricity and biomass trading as the link, it realizes the collaborative planning and operation of the rural energy system through the energy trading and information interconnection mechanisms. The proposed method can give play to the initiative of each agent, fully dispatch biomass resources, achieve the collaborative planning and operation of multiple agents within the rural energy system, and effectively guarantee the interests of each agent. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings forming a part of this application are used to provide a further understanding of this application. The schematic embodiments of this application and their descriptions are used to explain this application and do not constitute an improper limitation to this application. In the drawings:

[0036] Figure 1 Schematic diagram of the multi-agent framework of the rural energy system according to the embodiment of the present invention;

[0037] Figure 2 Schematic diagram of the proposed construction framework of the IRES operator according to the embodiment of the present invention;

[0038] Figure 3 Seasonal diagram of biomass materials according to the embodiment of the present invention;

[0039] Figure 4 Schematic diagram of the multi-agent Nash bargaining framework according to the embodiment of the present invention;

[0040] Figure 5 Schematic diagram of the per-unit value of wind power varying with quarters according to the embodiment of the present invention;

[0041] Figure 6 Schematic diagram of the per-unit value of photovoltaic power varying with quarters according to the embodiment of the present invention;

[0042] Figure 7 Schematic diagram of the equipment construction of the rural energy system in Scheme 1 according to the embodiment of the present invention;

[0043] Figure 8 Schematic diagram of the electric power of the IRES1 operator varying with quarters in Scheme 1 according to the embodiment of the present invention;

[0044] Figure 9 Schematic diagram of the thermal power of the IRES1 operator varying with quarters in Scheme 1 according to the embodiment of the present invention;

[0045] Figure 10 Schematic diagram of the gas power of the IRES1 operator varying with quarters in Scheme 1 according to the embodiment of the present invention;

[0046] Figure 11 Schematic diagram of the electricity trading price of the operator varying with quarters in Scheme 1 according to the embodiment of the present invention;

[0047] Figure 12Schematic diagram of the operation of Scheme 1 of the PSPS operator in the embodiments of the present invention varying with quarters;

[0048] Figure 13 Schematic diagram of the proportion of electricity trading volume of the PSPS operator in different seasons in the embodiments of the present invention;

[0049] Figure 14 Schematic diagram of the biomass trading situation under Scheme 1 in the embodiments of the present invention;

[0050] Figure 15 Schematic diagram of the seasonal electricity trading volume of the IRES operator in the embodiments of the present invention. Detailed implementation manners

[0051] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0052] It should be noted that the steps shown in the flowchart of the drawings may be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than here.

[0053] Embodiment 1

[0054] In this embodiment, a multi-agent collaborative planning method for a rural energy system considering biomass trading is provided, including:

[0055] S1. Establish a multi-agent collaborative planning model for the rural energy system, analyze the seasonal characteristics of biomass in rural areas, and construct a biomass availability model and a biogas production model for biogas stations;

[0056] As an innovative implementation manner, the process of establishing a multi-agent collaborative planning model for the rural energy system includes:

[0057] Integrate the Integrated Rural Energy System (IRES) and the Pumped Storage Power Station (PSPS), use electricity and biomass trading as the link, and achieve collaborative planning and operation through an energy trading and information interconnection mechanism; as Figure 1 shown.

[0058] The rural energy system participates in trading operations according to the differences in the agricultural industries, load conditions, and investment and operation parties in the regions where different operators are located, and based on the principle of "self-production and self-use, and trading of surplus energy".

[0059] Specifically, different IRES operators have different orientations in energy planning schemes and interest demands. When participating in collaborative planning and construction, these operators should follow the principle of "self-use of generated energy and trading of surplus energy" and participate in the trading operation of the rural energy system.

[0060] As an innovative implementation method, the rural integrated energy system includes a biogas station, a biogas engine, a biogas boiler, photovoltaic, wind power, an electric boiler, electric energy storage, and heat energy storage equipment, which are rationally configured according to the distribution of biomass resources in rural areas.

[0061] Specifically, in the rural energy system, the villages where IRES operators are located are distributed with greenhouse greenhouses, farms, paddy fields, etc., which can generate a large amount of biomass resources. The present invention selects and constructs a biogas station (BS), a biogas engine (BT), a biogas boiler (BB), photovoltaic (PV), wind power (WT), an electric boiler (EB), electric energy storage (EES), heat energy storage (HES), etc. according to local conditions, as Figure 2 shown.

[0062] Furthermore, the construction of the biomass available amount model includes:

[0063] Analyze the seasonal output of agricultural biomass according to the seasonal and periodic characteristics of agricultural activities in rural areas, and construct a biomass available amount model;

[0064] Specifically, in the biomass available amount model, it is difficult to statistically monitor the output of waste generated by agricultural production activities in rural areas. The present invention uses the predicted crop yields and predicted livestock inventories in this area to estimate the corresponding biomass amounts.

[0065] Use the predicted yields of agricultural crops to estimate the total theoretical available amounts of biomass such as rice straw, pepper straw in greenhouse greenhouses, and rotten citrus fruits. The mathematical model is:

[0066]

[0067] In the formula, is the total theoretical available amount of the i-th type of planting waste in the r-th quarter, is the yield of the i-th type of crop in the r-th quarter; r i is the waste production ratio coefficient of the i-th type of crop; ν i is the collection coefficient of the i-th type of crop waste.

[0068] Estimate the available amount of livestock and poultry manure per quarter by using the daily predicted inventory quantity of livestock and poultry. The mathematical model is as follows:

[0069]

[0070] In the formula, is the available amount of manure and sewage of livestock and poultry in the r-th quarter; z r is the daily inventory quantity of livestock and poultry in the r-th quarter; x is the daily excretion quantity of livestock and poultry; φ represents the collection coefficient of livestock and poultry excreta.

[0071] As an innovative implementation method, estimate the total theoretical available amount of biomass by using the predicted crop yield and the predicted inventory quantity of livestock and poultry, and calculate the cost model for IRES operators to collect local biomass.

[0072] Specifically, agricultural activities in rural areas have seasonal and periodic characteristics, which have a significant impact on agricultural biomass output. Biogas production usually depends on agricultural biomass, such as livestock and poultry manure, crop straw, etc. The output of these wastes varies in different seasons, thus affecting the biogas production, as Figure 3 shown. Therefore, IRES operator BS operator needs to adjust the production plan according to the biomass material supply in each season.

[0073] Specifically, the cost model for IRES operators to collect local biomass;

[0074] The cost of IRES collecting local agricultural wastes can be divided into three links according to the links: acquisition, transportation, and raw material pretreatment.

[0075] The mathematical expression of the biomass acquisition cost is:

[0076] C b1,i =W i c de,i (3)

[0077] In the formula, C b1,i is the acquisition cost of the i-th type of biomass; W i is the acquisition quantity of the i-th type of biomass, c de,i is the acquisition price of the i-th type of biomass.

[0078] The transportation cost of biomass can be expressed as:

[0079] C b2,i =W i c tra,i d i (4)

[0080] In the formula: C b2,i is the transportation cost of the i-th type of biomass; the d i is the transportation distance of the i-th type of biomass, ctra,i is the unit transportation cost of the i-th type of biomass.

[0081] In addition to the acquisition and transportation costs, the biomass waste also involves the raw material pretreatment process. These costs are linearly related to the biomass acquisition volume, and their mathematical expression is:

[0082] C b3,i = k i W i (5)

[0083] Where: C b3,i is the raw material pretreatment cost of the i-th type of biomass; k i is the pretreatment cost coefficient of the i-th type of biomass.

[0084] The mathematical expression for the cost of the IRES operator to collect local agricultural waste is:

[0085]

[0086] Where: π col is the biomass collection cost of the IRES operator, C b1,i is the acquisition cost of the i-th type of biomass, C b2,i is the transportation cost of the i-th type of biomass, C b3,i is the raw material pretreatment cost of the i-th type of biomass.

[0087] As an innovative implementation method, the construction of the biogas production volume model of the biogas station includes:

[0088] Calculate the biogas production volume of the biogas station per quarter according to the mass of various biomass materials input into the biogas station;

[0089] Based on the relationship between the construction capacity of the biogas equipment and the quarterly biogas production volume, determine the construction capacity of the biogas digester.

[0090] Specifically, the quarterly biogas production volume and construction capacity model of the biogas equipment;

[0091] The total biogas production volume of BS per quarter in the IRES operator depends on the total solid content, also known as the dry matter content, in the biomass raw materials used for fermentation in that quarter. Based on the mass of various biomass materials input into the biogas station, the mathematical expression for calculating the biogas production volume of BS per quarter is:

[0092]

[0093] Where, G r is the total biogas production volume of the biogas station in the r-th quarter; is the mass of the i-th type of biomass raw material fermented in the r-th quarter of the biogas station; S i is the total solid content of the i-th type of biomass raw material; Y iis the gas production rate of the total solids of the fermentation material; y i is the correction factor for the biogas production of the biomass fermentation material.

[0094] The relationship between the construction capacity of the biogas equipment and the quarterly biogas production is:

[0095]

[0096] In the formula, V BIO is the construction capacity of the biogas digester, G r is the quarterly biogas production of the biogas digester in the r-th quarter; D r is the number of days in the r-th quarter; T e is the set temperature of the fermentation environment; r p(20) is the volumetric gas production rate; θ is the temperature influence coefficient.

[0097] S2. According to the biomass availability model and the gas production model, combined with the interest demands and transaction models of each entity, construct the objective function and constraint model of the rural integrated energy system operator and the pumped-storage power station operator;

[0098] Furthermore, the construction of the objective function and constraint model of the rural integrated energy system operator includes:

[0099] Taking the annualized cost as the objective function, including the annualized construction cost, maintenance cost, energy trading cost, grid power purchase cost, straw purchase cost of the biomass dealer, and biomass collection cost;

[0100] Set equipment construction constraints, equipment ramp-up constraints, power constraints, and biomass constraints.

[0101] Specifically, the objective function and constraint conditions of the IRES type operator:

[0102] The objective function of the j-th operator in the IRES type operator is the annualized cost F RIES,j , including the annualized construction cost C inv,j , maintenance cost C op,j , energy trading cost C trade,j , grid power purchase cost C grid,j , straw purchase cost C of the biomass dealer de,j , and biomass collection cost π col,j , and its objective function is expressed as:

[0103]

[0104]

[0105]

[0106]

[0107]

[0108]

[0109] Wherein, Cap n,j , T n,j , ψ n,j are respectively the planned capacity, life cycle and unit capacity construction cost of the nth type of equipment of the jth IRES operator; s is the discount rate of the equipment; Se is the total number of quarters; D r is the total number of days in the rth quarter; are respectively the output and unit output maintenance cost of the nth type of equipment in the rth quarter; T takes the value of 24; is the electricity trading volume between the kth IRES operator and the jth IRES operator in the rth quarter, and a positive value indicates that the kth IRES operator sells electricity to the jth IRES operator; represents the electricity trading price between the kth IRES operator and the jth IRES operator; are respectively the electricity trading volume and trading price between the PSPS operator and the jth IRES operator; represents the electricity purchase volume of the jth IRES operator from the power grid; is the time-of-use electricity price of the power grid; are respectively the trading volume and trading price of biomass between the kth IRES operator and the jth IRES operator; is the straw purchase volume and purchase price of the jth IRES operator from the biomass dealer.

[0110] IRES type operator constraint model:

[0111] 1) Equipment construction constraint:

[0112]

[0113] Wherein: Cap n,j represents the capacity of the nth type of energy equipment of the jth IRES operator; is a Boolean variable representing the construction situation of the equipment, 1 indicates construction, and 0 indicates non-construction; are respectively the minimum and maximum values of the equipment construction capacity.

[0114] 2) Equipment ramp-up constraint:

[0115] When the IRES operator's equipment is operating normally, it needs to operate within a reasonable power range and meet the ramp-up constraint:

[0116]

[0117] In the formula: are respectively the minimum and maximum operating powers of the nth type of energy equipment in the jth IRES operator; Δt is the unit time, and in the present invention, the value is 1 hour; B n,j , D n,j are respectively the upper and lower limits of the ramping power of the energy equipment.

[0118] 3) Power constraint:

[0119]

[0120] In the formula, are respectively the electrical load, thermal load and gas load of the jth IRES operator at the tth moment in the rth quarter; are respectively the power generation power and heat generation power of the biogas engine at the tth moment in the rth quarter; represents the power generation power of the photovoltaic unit in the rth quarter; is the electrical energy trading volume between the PSPS operator and the jth IRES operator; is the electrical energy trading volume between the kth IRES operator and the jth IRES operator; is the gas production volume of the biogas digester of the jth IRES operator at the tth moment in the rth quarter; represents the gas consumption power and heat production power of the biogas boiler at the tth moment in the rth quarter; represents the output power of the electrical energy storage device and the thermal energy storage device; represents the heat production power of the electric boiler in the rth quarter; is the total biogas production volume of the biogas digester of the jth IRES operator in the rth quarter.

[0121] 4) Biomass constraint:

[0122]

[0123] In the formula, is the mass of the ith type of biomass fermented by the BS device of the jth IRES operator in the rth quarter; is the available amount of the ith type of biomass in the village where the jth IRES operator is located in the rth quarter; is the amount of rice straw purchased by the jth IRES operator from the biomass dealer; is the trading volume of the ith type of biomass between the kth IRES operator and the jth IRES operator, a positive value indicates selling, and a negative value indicates buying, is the available amount of the ith type of biomass in the village where the kth IRES operator is located in the rth quarter.

[0124] Furthermore, the construction of the objective function and constraint model of the pumped-storage power station operator includes:

[0125] Taking the capacity rental fee and the electricity trading revenue as the objective functions, including the leased capacity and the unit capacity rental cost;

[0126] Set the constraints on the power selling and power purchasing, ensuring that the energy storage leased capacity status of the pumped-storage power station operator is within a reasonable range.

[0127] Specifically, the main objective function and constraints of the PSPS operator:

[0128] The operation mode of the pumped-storage operator is the regional agency mode. The objective function includes two parts: the capacity rental fee and the electricity trading revenue. The mathematical expression is:

[0129]

[0130] In the formula, F PSPS is the equivalent annual value economic benefit of the PSPS, Cap PSPS , ψ PSPS represent the leased capacity and the unit capacity rental cost of the PSPS respectively; D r is the total number of days in the r-th quarter; Se is the total number of quarters; are the electricity trading volume and trading price between the j-th IRES operator and the PSPS operator.

[0131]

[0132]

[0133] E min ≤E t,r ≤Cap PSPS (26)

[0134] E T =E0 (27)

[0135] In the formula, are the power selling and power purchasing of the PSPS operator at the t-th moment in the r-th quarter respectively; are the minimum and maximum values of the power selling of the PSPS operator; are the minimum and maximum values of the power purchasing of the PSPS operator; is the power selling and power purchasing state variable at the t-th moment in the r-th quarter; E t,r is the energy storage leased capacity status of the PSPS operator at the t-th moment in the r-th quarter, E max , E min represent the upper and lower limits of the leased capacity status, Cap PSPS is the leased capacity of the PSPS operator.

[0136] S3. Solve the multi-agent collaborative planning model according to the objective function and the constraint model to obtain the optimal planning scheme and trading mechanism of the rural energy system.

[0137] Further, solving the collaborative planning model includes:

[0138] Equivalently transform the multi-agent Nash bargaining problem into an energy trading volume bargaining sub-problem and an energy trading price bargaining sub-problem;

[0139] Use the alternating direction method of multipliers to construct a distributed optimization framework, and solve the two sub-problems by cyclic iteration in turn to obtain the optimal planning scheme and trading mechanism of the energy system.

[0140] Specifically, in the multi-agent game architecture of the rural energy system constructed by the present invention, the game players are the IRES1 operator, the IRES2 operator, and the PSPS operator. The three are equal in status during the game process and participate in energy trading as independent and rational individuals respectively. They reach a consensus through Nash bargaining transactions and jointly formulate the collaborative planning scheme and trading mechanism of the energy system, as Figure 4 shown. In the Nash bargaining game, there are reconcilable interest conflicts among the game participants. Each game participant will try to stay away from the breakdown point of the negotiation as much as possible. The utility function of the multi-agent Nash bargaining game of the rural energy system can be expressed as:

[0141]

[0142] In the formula, m ∈ {IRES1, IRES2, PSPS}, F m is the economic benefit of game participant m after establishing a collaborative mechanism through Nash bargaining, is the breakdown point of the negotiation of game participant m, that is, the economic benefit of independent construction and operation of subject m.

[0143] The Nash bargaining game model is essentially a typical non-linear optimization problem. The present invention equivalently transforms the multi-agent Nash bargaining problem into two bargaining sub-problems, then uses the alternating direction method of multipliers to construct a distributed optimization framework, and adopts a sequential optimization method to solve the two sub-problems by cyclic iteration in turn to obtain the optimal solution of the original problem.

[0144] Further, the solution of the energy trading volume bargaining sub-problem includes:

[0145] By introducing Lagrange factors and penalty factors, transform the energy trading volume bargaining sub-problem into a distributed form of the augmented Lagrangian function;

[0146] Through iterative cyclic optimization of the energy trading strategies of each subject until the convergence condition is met, the solution of the energy trading volume bargaining sub-problem is completed.

[0147] Specifically, sub-problem 1: The energy trading volume negotiation sub-problem, which is the problem of maximizing the economic benefits of game participants, can be expressed by the following mathematical function:

[0148]

[0149] In the formula, represents the expected electricity trading volume from the IRES1 operator to the IRES2 operator at time t in the r-th quarter, represents the expected value of the electricity trading volume from the IRES2 operator to the IRES1 operator at time t in the r-th quarter; when , it can be considered that the electricity trading volumes of IRES1 and IRES2 reach a consensus; is the expected trading volume of the i-th type of biomass from IRES1 to IRES2 in the r-th quarter. Similarly, the actual meanings of variables such as can be known.

[0150] Sub-problem 2: The energy trading price negotiation sub-problem;

[0151] After the energy trading volumes of each entity reach a consensus, the energy trading volumes of each entity are used as parameters for the energy trading price negotiation sub-problem. In this paper, a non-linear function method based on the natural logarithm is used for decoupling. The objective function for solving the energy trading price sub-problem is as follows:

[0152]

[0153] In the formula, is the economic benefit of entity m under the optimal energy trading volume parameter obtained in sub-problem 1, is the economic benefit of entity m's independent construction and operation, which is a constant. represents the expected electricity trading price from IRES1 to IRES2 at time t in the r-th quarter, represents the expected electricity trading price from IRES2 to IRES1 at time t in the r-th quarter. When , it can be considered that the electricity trading prices of IRES1 and IRES2 reach a consensus, is the expected trading price of the i-th type of biomass from IRES1 to IRES2 in the r-th quarter. Similarly, the actual meanings of variables such as can be known.

[0154] The alternating direction method of multipliers is a numerical optimization algorithm. After equivalently transforming the multi-agent Nash negotiation problem into two negotiation sub-problems in this paper, a distributed optimization framework is constructed using the alternating direction method of multipliers, and a sequential optimization method is used to iteratively solve the two sub-problems in turn.

[0155] In the solution of the energy trading volume negotiation problem, P m,n ,P n,m ,Mm,n , M n,m As a coupled solution variable, according to the principle of the alternating direction method of multipliers, the formula of sub-problem 1 is transformed into the distributed form of the augmented Lagrangian function, and the Lagrangian factor and the penalty factor are introduced. The scheme and energy trading strategy are optimized through continuous iterative loops. When the convergence condition is met, the iteration terminates, that is, the solution of the energy trading sub-problem is completed. The specific formula is as follows:

[0156]

[0157] In the formula, ζ1 and δ1 represent the primal residual and dual residual of the multi-agent energy trading price negotiation sub-problem, respectively.

[0158] To solve the energy trading price sub-problem of each agent, by taking R m,n , R n,m , as the coupling variables. According to the principle of the alternating direction method of multipliers, the formula of sub-problem 2 is decomposed into the augmented Lagrangian function of each agent's revenue, and the Lagrangian multiplier of the price negotiation problem and the penalty factor are introduced. Each agent optimizes through iterative loops. When the residual meets the convergence condition, the transaction price negotiation problem is completed. The specific formula is as follows:

[0159]

[0160]

[0161] In the formula, ζ2 and δ2 represent the primal residual and dual residual of the energy trading price sub-problem, respectively.

[0162] To verify the effectiveness of the method proposed in the present invention, the case data of a rural area in Guangxi is selected for simulation analysis. The seasonal scenarios of the rural energy system include spring, summer, autumn and winter. The seasonal supply of biomass in the biogas station is shown in Table 1, Figure 5 and Figure 6 are the per-unit values of wind power and photovoltaic power in this area. The construction costs and maintenance costs of energy equipment are shown in Table 2, and the equipment discount rate is 8%.

[0163] Table 1

[0164]

[0165] Table 2

[0166]

[0167] To verify the rationality and practicality of the proposed method, the present invention sets up 4 different planning schemes for analysis.

[0168] Scenario 1: The multi-agent collaborative planning method for rural energy systems proposed in the present invention.

[0169] Scenario 2: Multi-agent collaborative planning for rural energy systems, without considering the biomass trading between IRES1 operator and IRES2 operator.

[0170] Scenario 3: Multi-agent collaborative planning for rural energy systems, without considering the biomass trading and electricity trading between IRES1 operator and IRES2 operator.

[0171] Scenario 4: Without considering the energy trading between agents, each agent independently solves its own optimal planning scheme.

[0172] (1) Analysis of the equipment construction of each agent;

[0173] Table 3 shows the equipment construction of each operator in the rural energy system under different scenarios. "-" indicates not constructed or not leased. Figure 7 It is the equipment construction of the rural energy system in Scenario 1. In Scenario 1, the equipment constructed by IRES1 operator includes BS, PV, WT, EB, BB, and the equipment constructed by IRES2 operator are BS, PV, WT, EB, BB. Neither of the two IRES operators constructs EES, indicating that when multi-agent collaborative planning is carried out, the economy of electricity trading between IRES1, IRES2 operators and PSPS operator is better than independent configuration of EES. Among the four scenarios, neither IRES1 nor IRES2 operator chooses to construct HES, indicating that HES is not economical in the rural scenarios of Guangxi.

[0174] Comparing Scenario 1 with Scenario 2, the PV and WT equipment capacities of IRES1 and IRES2 operators and the leased capacity of PSPS operator all decrease. At the same time, combined with Figure 13 It can be seen that as the purchasing entity of biomass trading between IRES operators, IRES2 operator not only increases the BS capacity but also constructs BB equipment. While IRES1 operator, as the selling entity of biomass trading between IRES operators, its BS capacity is reduced by 471m 3 compared with Scenario 2, and at the same time reduces the construction capacity of BB equipment. It can be seen that biomass trading enables IRES operators to build more reasonable BS capacity according to the biomass development scale.

[0175] As can be seen from Table 4, in Plan 2, the biomass transaction between IRES1 operator and IRES2 operator is not considered, and the annualized equipment configuration cost of the rural energy system is 290,000 yuan higher than that in Plan 1; in Plan 3, due to the non-consideration of the biomass transaction and electricity transaction between IRES1 operator and IRES2 operator, the annualized equipment configuration cost of its rural energy system is 5% higher than that in Plan 1; in Plan 4, the collaborative planning and energy interaction among the main bodies of the rural energy system are not considered, and the capacity leasing service of the PSPS operator is no longer introduced into the rural energy system. Compared with Plan 4, the annualized equipment configuration cost of the rural energy system in Plan 1 is reduced by 18%. The comparison of multiple plans verifies the economy and effectiveness of the multi-agent collaborative planning method in equipment configuration.

[0176] Table 3

[0177]

[0178] Table 4

[0179]

[0180] (2) Analysis of energy transactions and operation conditions of operators;

[0181] Figure 8 is the electric power output of the IRES1 operator in Plan 1. The IRES1 operator has built large-capacity PV and WT new energy equipment, which can generate relatively sufficient electric energy. In terms of electricity transactions, the trading volume between the IRES1 operator and the IRES2 operator is small, while the energy transactions with the PSPS operator are more and more frequent, verifying the peak shaving service attribute of the PSPS operator in the rural energy system. In spring, the IRES1 sells electric energy to other operators during the two time periods from 11:00 to 15:00 and from 17:00 to 20:00, and purchases electric energy at 16:00. This sudden change in state reflects the flexibility of the market-based electricity transaction under multi-agent collaborative planning. Figure 9 is the thermal energy output of the IRES1 operator's equipment in Plan 1. In spring, autumn and winter, the EB heating mainly meets the heat load demand of rural users, while in summer, the BB and EB jointly output to meet the heat load demand of users in the valley villages. The reasons for this result are as follows: firstly, the summer electric load level is relatively high, and the IRES1 operator chooses to activate the BB heating; secondly, the biomass resources of the IRES1 operator are relatively sufficient in summer, and the BS has relatively sufficient gas production for the BB heating. Figure 10 is the gas power output of the IRES1 operator in Plan 1. The BS of the IRES1 operator converts agricultural waste in rural areas into biogas, providing a sufficient amount of gas supply for users.

[0182] Figure 11For the electricity trading prices of each operator in the rural energy system, it can be seen that the electricity trading prices among IRES1 operator, IRES2 operator and PSPS operator are lower than the time-of-use electricity price of the power grid. IRES1 operator and IRES2 operator can buy electricity at a price lower than the grid electricity price to meet their own needs. At the same time, PSPS operator can also purchase electricity at a relatively low price and sell it when the electricity price is high to obtain certain economic benefits.

[0183] Figure 12 For the output of PSPS operator in Scheme 1, it can be known that the proportion of electricity trading volume of PSPS operator in summer is relatively small, and the leased capacity state curve is relatively smooth. While in spring and winter, the trading volume of PSPS is larger and the leased capacity curve fluctuates greatly. Combining Figure 13 it can be known that the seasonal proportions of the electricity trading volume of PSPS operator are 27%, 22%, 24% and 27% respectively. The trading volume in summer is less because the electricity load is higher in summer and the IRES operator BB operates flexibly. Therefore, the electricity trading demand of IRES operator is relatively small. Combining Figure 12 and Figure 13 , it can be known that the internal electricity trading in the rural energy system also has seasonal characteristics.

[0184] (3) Analysis of biomass trading in the rural energy system;

[0185] Figure 14 For the agricultural waste output and trading of the valley village and the hillside village, there are greenhouse greenhouses and paddy fields in the valley village. Chili peppers are planted in the greenhouse greenhouses of this village, and the paddy is double-cropped a year. After the early rice is harvested, farmers will plant other cash crops. Therefore, the available amount of late rice straw is less than that of early rice straw. In summer and autumn, IRES1 operator has a relatively abundant output of rice straw, which can not only meet its own BS material needs, but also be traded to IRES2 operator in summer and autumn. In spring and winter, IRES1 operator needs to purchase some straw from the straw operator to meet its own material needs.

[0186] There is a pig farm inside IRES2 operator, and there is a relatively stable source of livestock and poultry manure. It can obtain more economical biomass straw from IRES1 operator in summer and autumn. Compared with spring, in winter, as citrus is in season, IRES2 operator reduces the amount of straw purchased from the dealer by developing and utilizing citrus rotten fruits.

[0187] Therefore, for the investors in the rural energy system, by carrying out the biomass trading of IRES1 and IRES2, the "commercialized" development and utilization of biomass resources can be realized, making it a resource that can be flexibly scheduled and participating in the planning of the rural integrated energy system.

[0188] Figure 15It is a graph of the seasonal electricity trading volume of IRES operators. Combined with Figure 14 Analysis shows that the biomass trading seasons of IRES1 operator and IRES2 operator are mainly in summer and autumn, and the trading type is rice straw. Compared with Scheme 2, the electricity trading volume in each season in Scheme 1 has decreased. It can be seen that the biomass trading among IRES operators helps to reduce the electricity trading volume between IRES operators and other entities. In terms of trading seasons, the electricity trading volumes of IRES1 operator and IRES2 operator in summer and autumn are lower in Scheme 1 than in spring and winter, while in Scheme 2 they are higher than in spring and winter. Thus, it can be seen that the season when biomass trading occurs can cause a significant decrease in the electricity trading volume in that season. Further analysis shows that the multi-agent planning of the rural energy system considering biomass trading can reduce the electricity trading volume of IRES operators, and this reduction phenomenon is more obvious in the season when biomass trading occurs.

[0189] (4) Comparative analysis of the economy of multiple schemes;

[0190] Table 5 shows the annualized costs of each operator under different schemes, where positive values represent costs and negative values represent revenues. The annualized profits of the PSPS operator in Scheme 1, Scheme 2, and Scheme 3 are 690,000, 940,000, and 1,050,000 respectively. It can be seen that the more diverse the energy trading of IRES1 operator and IRES2 operator, the less profit the PSPS operator of the rural energy system will make. In Scheme 2, there is no biomass trading for IRES1 operator and IRES2 operator, and their annualized costs increase by 11% and 3% respectively, and the annualized cost of the rural energy system increases by 4% compared with Scheme 1; comparing Scheme 1 with Scheme 3, the annualized cost of the rural energy system is reduced by 16%; comparing Scheme 1 with Scheme 4, it can be seen that the total planning cost of the multi-agent of the rural integrated energy system is reduced by 32% after joint planning and operation. Comparing the four schemes, the following conclusions can be drawn: 1) As the energy storage service entity of the system, the profit situation of the PSPS operator is affected by the energy trading modes of other operators, and the more diverse the energy trading types of IRES1 operator and IRES2 operator, the less profit the PSPS operator of the rural energy system will make. 2) The biomass trading and electricity trading between IRES1 and IRES2 are of great significance for reducing the economic cost of the rural energy system, and each operator can improve the economic benefit of the rural energy system through collaborative planning and energy interaction.

[0191] Table 5

[0192]

[0193] The above are only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present application should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A multi-agent collaborative planning method for rural energy systems considering biomass trading, characterized in that: The following steps are involved: Establish a multi-agent collaborative planning model for rural energy systems, analyze the seasonal characteristics of biomass in rural areas, and build a biomass availability model and a biogas production model for biogas stations; According to the biomass availability model and the gas production model, combined with the interests and transaction models of various entities, the objective function and constraint model of the rural integrated energy system operator and the pumped storage power station operator are constructed; According to the objective function and constraint model, the multi-agent collaborative planning model is solved to obtain the optimal planning scheme and transaction mechanism of the rural energy system.

2. The method according to claim 1, characterized in that The process of establishing a multi-agent collaborative planning model for rural energy systems includes: Integrate rural comprehensive energy systems and pumped storage power stations, use electricity and biomass trading as a link, and achieve coordinated planning and operation through energy trading and information interconnection mechanisms; Based on the differences in agricultural industries, load conditions and investment operators in the regions where different operators are located, and based on the principle of "self-production and self-use, and trading of surplus energy", we participate in trading operations.

3. The method according to claim 2, characterized in that The rural comprehensive energy system includes biogas stations, biogas engines, biogas boilers, photovoltaics, wind power, electric boilers, electric energy storage and thermal energy storage equipment, which are reasonably configured according to the distribution of biomass resources in rural areas.

4. The method according to claim 1, characterized in that The construction of the biomass availability model includes: Analyze the seasonal output of agricultural biomass and construct a biomass availability model based on the seasonal and cyclical characteristics of agricultural activities in rural areas; The theoretical total available amount of biomass is estimated using the predicted crop yields and predicted livestock and poultry stocks, and a cost model for IRES operators to collect local biomass is calculated.

5. The method according to claim 1, characterized in that The construction of the gas production model of the biogas station includes: Calculate the gas production of the biogas station every quarter based on the quality of various biomass materials input into the biogas station; The construction capacity of the biogas digester is determined based on the relationship between the construction capacity of the biogas equipment and the quarterly biogas production.

6. The method according to claim 1, characterized in that The construction of the objective function and constraint model of the rural integrated energy system operator includes: The annualized cost is used as the objective function, including annualized construction cost, maintenance cost, energy transaction cost, power grid electricity purchase cost, biomass dealer straw purchase cost, and biomass collection cost; Set equipment construction constraints, equipment ramp constraints, power constraints, and biomass constraints.

7. The method according to claim 1, characterized in that The construction of the objective function and constraint model of the pumped storage power station operator includes: The objective function is to take capacity leasing fee and electricity trading income, including leasing capacity and unit capacity leasing cost; Set constraints on electricity sales and purchases to ensure that the energy storage leasing capacity status of pumped-storage power station operators is within a reasonable range.

8. The method according to claim 1, characterized in that: Solving the collaborative planning model includes: The multi-agent Nash negotiation problem is equivalently transformed into the energy transaction quantity negotiation sub-problem and the energy transaction price negotiation sub-problem; The alternating multiplier method is used to construct a distributed optimization framework, and the two sub-problems are solved iteratively in turn to obtain the optimal planning scheme and trading mechanism of the energy system.

9. The method according to claim 8, characterized in that The solution to the energy transaction volume negotiation sub-problem includes: By introducing Lagrangian factors and penalty factors, the energy transaction volume negotiation sub-problem is transformed into the distributed form of augmented Lagrangian function. The energy trading strategies of each entity are optimized through iterative cycles until the convergence conditions are met, thus completing the solution of the energy trading volume negotiation sub-problem.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 9 are implemented.

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