A comprehensive energy multi-layer game scheduling method and system for park power scheduling

Abstract

The embodiment of the present application provides a comprehensive energy multi-layer game scheduling method for park power scheduling, including the following steps: obtaining parameters and variables centered on shared energy storage, comprehensive energy suppliers, and production and consumer alliances, and respectively forming constraint model one, constraint model two, and constraint model three; constructing the first-level master-slave game, the second-level master-slave game, and the third-level cooperative game between shared energy storage, comprehensive energy suppliers, and production and consumer alliances. By fully considering the energy and strategy management of each party in the game, including shared energy storage, comprehensive energy suppliers, and production and consumer alliances, a reasonable game model is constructed, a multi-layer orderly game process is designed, and energy scheduling is optimized; for each layer of the game model, different analysis methods are adopted to reasonably and effectively improve energy utilization.

Description

Technical Field

[0001] The present application relates to the technical field of power system optimization operation, and in particular to a multi-layer game scheduling method for integrated energy. Background Art

[0002] With the continuous development of integrated energy systems, in order to safeguard the maximum interests of different entities, game theory is increasingly being applied to the optimal operation of integrated energy systems. Traditional approximate methods usually adopt a two-layer game model, one layer is the transaction between shared energy storage and consumers or consumer alliances, and the other layer is the pricing strategy within consumers or consumer alliances, but they may not fully consider the intermediary role of integrated energy operators. Integrated energy operators play the role of a bridge between shared energy storage and production consumers, and can play the role of a bridge between shared energy storage and production consumers, making energy transactions smoother and more efficient.

[0003] In practical applications, different participants may adopt different game strategies according to their respective interests, which increases the complexity of the model. Although existing game methods provide a certain theoretical basis for energy trading of shared energy storage, they may not meet the needs of fairness and efficiency in the face of more complex practical scenarios. Therefore, in order to adapt to these complex and changing application scenarios, it is necessary to further study and develop new game models to meet the needs of fair and efficient transactions and adapt to the needs of these complex scenarios. For example, in existing applications, operators set energy sales prices through master-slave games, and after receiving the prices, user aggregators conduct cooperative game processes based on Nash negotiation theory. This game process with multiple parties involved and diverse strategies is precisely the challenge and opportunity faced in the current optimized operation of integrated energy systems. Summary of the invention

[0004] The embodiment of the present application provides a comprehensive energy multi-layer game scheduling method for park power scheduling, which meets the needs of greater fairness and efficiency in energy trading to cope with more complex scenarios.

[0005] The embodiment of the present application provides a comprehensive energy multi-layer game scheduling method for park power scheduling, which is characterized by comprising the following steps:

[0006] Obtain parameters and variables centered on shared energy storage, integrated energy suppliers, and producer-consumer alliances, and respectively construct constraint model 1 composed of parameters and variables centered on the shared energy storage, constraint model 2 composed of parameters and variables centered on the integrated energy supplier, and constraint model 3 composed of parameters and variables centered on the producer-consumer alliance;

[0007] Construct a first-level master-slave game between constraint model 1 and constraint model 2, with the buying and selling electricity prices as the main optimization variables in Fun_ESO and Fun_LA, and the electricity purchasing strategy as the optimization target, which are mutually input and output, and perform multiple iterations within the initial electricity price upper and lower bounds set by the shared energy storage to reach an equilibrium state;

[0008] Construct a second-level master-slave game between constraint model 2 and constraint model 3, using the power consumption fed back to the integrated energy supplier by the production and consumption alliance and participating in the first-level master-slave game and the power purchase strategy and power purchase amount obtained by the integrated energy supplier in the first-level master-slave game to adjust the power price and energy price of the production and consumption alliance;

[0009] Construct the third-level cooperative game based on the electricity price strategy game between producers and consumers within the producer-consumer alliance.

[0010] Furthermore, the first-level master-slave game adopts a dichotomy analysis of the master-slave game between the power purchasing strategy provided by the integrated energy supplier and the electricity price adjustment of the shared energy storage, and iterates multiple times in the constructed upper and lower bounds of the electricity price to achieve equilibrium between the power purchasing strategy and the electricity price, so as to obtain the electricity price and power purchase amount at the equilibrium moment.

[0011] Furthermore, in the second-level master-slave game, KKT conditions are set for the constraint model 2, and the Big-M method is used to linearize the nonlinear constraints of the KKT conditions to obtain the shared power of the production and consumer alliance.

[0012] Furthermore, the third-level cooperative game is to decompose the electricity price problem to be solved into sub-problems in which each producer-consumer in the producer-consumer alliance can independently optimize the electricity price strategy, and iterate the objective function multiple times to obtain the latest electricity price.

[0013] Furthermore, the constraint model 1 is a series of constraints consisting of the energy consumption-output relationship between the integrated energy supplier and the cogeneration unit, the relationship between the power generation power and gas consumption of the cogeneration unit and its heat generation power and the power consumption and heat generation power of the gas boiler, the actual power output of the renewable energy equipment, its abandoned power and predicted power output, the upper and lower limits of the equipment rated power, the electricity selling price, the heat selling price, the power balance constraints constructed for each supplier, the power exchange constraints for each pair of suppliers and the power exchange balance constraints.

[0014] Furthermore, the constraint model 2 is a series of constraints consisting of the upper and lower limit constraints on the electric load that can be reduced by each production consumer in the consumer alliance, the power balance constraint on the amount of electricity purchased on the load side, the upper and lower limit constraints on the heat load that can be reduced, the power balance constraint on the amount of heat purchased on the load side, the upper and lower limit constraints on the amount of electricity purchased, and the upper and lower limit constraints on the electric energy interaction of each pair of production consumers.

[0015] Furthermore, the constraint model three is a series of constraints consisting of the boundary constraints of the electricity purchase price in the shared energy storage, the boundary constraints of the electricity sales price, the average purchase / sales price constraints, the energy balance constraints, the upper and lower bounds of energy constraints, and the charging and discharging power constraints.

[0016] On the one hand, a comprehensive energy multi-layer game scheduling system for park power scheduling is provided, and the multi-layer game scheduling system includes the following modules:

[0017] Construct a multi-party constraint model, obtain parameters and variables centered on shared energy storage, integrated energy suppliers, and producer-consumer alliances, and respectively construct a constraint model 1 composed of parameters and variables centered on the shared energy storage, a constraint model 2 composed of parameters and variables centered on the integrated energy supplier, and a constraint model 3 composed of parameters and variables centered on the producer-consumer alliance;

[0018] Construct a multi-layer game relationship model, respectively construct the first-layer master-slave game between constraint model 1 and constraint model 2, the second-layer master-slave game between constraint model 2 and constraint model 3, and the third-layer cooperative game within constraint model 3;

[0019] Output the game results, and output the power purchase strategy and electricity price at a certain moment after the multi-layer game of the relational model.

[0020] The beneficial effects of this application are as follows: (1) It fully considers the energy and strategy management of each party in the game, including shared energy storage, integrated energy suppliers, and production and consumer alliances, builds a reasonable game model, designs a multi-layer orderly game process, and optimizes energy scheduling;

[0021] (2) For each layer of the game model, different analysis methods are adopted to reasonably and effectively improve energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only the embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0023] Figure 1 A block diagram of a comprehensive energy multi-layer game scheduling method for park power scheduling provided in Example 1 of the present application;

[0024] Figure 2 The process of gradually updating the upper and lower bounds of electricity prices in multiple rounds of iterations in the first-level master-slave game provided in the second embodiment of the present application;

[0025] Figure 3 A flow chart of a comprehensive energy multi-layer game scheduling system for park power scheduling provided in Example 2 of the present application; DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] Embodiment 1

[0028] A comprehensive energy multi-layer game scheduling method for park power scheduling includes the following steps:

[0029] S01: Constraint models are constructed for the integrated energy supplier, producer alliance, and shared energy storage. The operations are as follows:

[0030] First: Obtain parameters and variables centered on integrated energy suppliers, including heat and electricity sales variables, and build a constraint model:

[0031] (1) Establish the relationship between energy consumption and output of integrated energy suppliers and cogeneration units, and set power output limits for renewable power equipment:

[0032] P CHP,i,t =η elec ·CV·f CHP,i,t

[0033] Among them, P CHP,i,t is the electrical power output of the CHP unit of the i-th integrated energy supplier in time period t, η elec is the electrical efficiency of the cogeneration unit, CV is the calorific value of the gas, f CHP,i,t is the gas consumption of the CHP unit of the i-th energy supplier in time period t.

[0034] (2) Set a relationship between the power generation capacity and gas consumption of the cogeneration unit, and a relationship between the heat generation capacity of the cogeneration unit and the power consumption and heat generation capacity of the gas boiler.

[0035] h CHP,i,t =η heat ·f CHP,i,t

[0036] Among them, h CHP,i,t is the thermal power output of the CHP unit of the i-th energy supplier in time period t, η heat is the thermal efficiency of the cogeneration unit.

[0037] (3) Renewable power equipment includes actual power output, abandoned power, predicted power output, etc.

[0038] 0≤p rew,i,t ≤pre_rew i,t

[0039] 0≤p cur_rew,i,t ≤pre_rew i,t

[0040] p rew,i,t +p cur_rew,i,t =pre_rew i,t

[0041] Among them, p rew,i,t is the actual electric power output of the renewable energy equipment of the i-th energy supplier in time period t, p cur_rew,i,t is the amount of electricity that is abandoned by the renewable energy equipment of the i-th energy supplier in time period t, pre_rew i,t is the predicted electric power output of the renewable energy equipment of the i-th energy supplier in time period t.

[0042] (4) The operation of each device shall not exceed the rated power of:

[0043] 0≤p CHP,i,t ≤P CHP,max

[0044] 0≤h GB,i,t ≤H GB,max

[0045] (5) The conditional expression of electricity selling price is:

[0046] pri_sell≤pri_e j,i,t ≤pri_buy

[0047]

[0048] Among them, pri_ej,i,t is the electricity selling price of the i-th energy supplier in the j-th level game in time period t, pri_sell is the lower limit of the electricity selling price, pri_buy is the upper limit of the electricity selling price, T=24 is the number of time periods in a day, and avg_pri_e_max is the maximum average of the electricity selling price.

[0049] (6) The conditional expression of hot selling price is:

[0050] pri_h_min≤pri_h j,i,t ≤pri_h_max

[0051]

[0052] Among them, pri_h j,i,t is the heat selling price of the i-th energy supplier in the j-th level game in time period t, pri_h_min is the lower limit of the heat selling price, pri_h_max is the upper limit of the heat selling price, and avg_pri_h_max is the maximum average heat selling price.

[0053] (7) For each supplier i, time period t, the power balance constraint is:

[0054] P buy,i,t -P sell,i,t +P CHP,i,t -Σ j≠i P e,ji,t =Σ n＝1 P load,n,t -P rew,i,t

[0055] Among them, Σ j≠i P e,ji,t represents the power shared by supplier i and other suppliers, P buy,i,t It is to purchase electricity from shared energy storage sell,i,t is the amount of electricity sold to shared energy storage. CHP,i,t is the power generation of the cogeneration unit. load,n,t is the amount of electricity purchased by producer consumer n, P rew,i,t It is the amount of electricity generated by renewable energy.

[0056] For each pair of suppliers (i, j), the power exchange constraint is: e,max ≤P e,ij,t ≤P e,max

[0057] Among them, P e,max is the maximum value of power exchange.

[0058] For each pair of suppliers (i, j), the power exchange balance constraint is: e,ij,t +P e,ji,t=0

[0059] Second: Obtain parameters and variables centered on the producer alliance and build a constraint model:

[0060] (1) The producer alliance model is assumed to include upper and lower limit constraints on load. For each producer consumer i, the upper and lower limit constraints on time reduction of electricity load are: 0 ≤ p cut,i,t ≤α·pL,i,t

[0061] Among them, p cut,i,t is the electric load that can be reduced, α is the ratio of the electric load that can be reduced, and pL,i,t is the electric load.

[0062] (2) For each producer and consumer i, the load-side power purchase power balance constraint is:

[0063]

[0064] Among them, p load,i,t is the amount of electricity purchased, p PV,i,t is the photovoltaic power generation, Σ j≠i p tran,ji,t is the electric energy transferred by other prosumers j.

[0065] (3) For each producer and consumer i, the upper and lower limits of the heat load that can be reduced are:

[0066] 0≤h cut,i,t ≤β·hL,i,t

[0067] Among them, h cut,i,t is the heat load that can be reduced, hL,i,t is the heat load, and β is the ratio of the heat load that can be reduced.

[0068] (4) For each producer and consumer i, the load-side heat purchase power balance constraint is:

[0069] h load,i,t =h L,i,t -h cut,i,t

[0070] Among them, h load,i,t It is the purchase of heat.

[0071] (5) For each producer and consumer i, the upper and lower limits of the purchased electricity heat are:

[0072] 0≤p load,i,t ≤P load,max ,0≤h load,i,t ≤H load,max

[0073] Among them, P load,max , H load,max are the maximum values ​​of electricity and heat purchased respectively.

[0074] (6) For each pair of producers and consumers (i, j), the upper and lower limits of power interaction are:

[0075] -P tran,max ≤p tran,ij,t ≤P tran,max

[0076] Among them, P tran,max is the maximum value of the electric energy interaction, p tran,ij,t is the electrical energy transferred to prosumer j.

[0077] Third: Obtain parameters and variables centered on shared energy storage and build a constraint model:

[0078] (1) Shared energy storage is the publisher of electricity prices. The shared equipment modeling includes the purchase price, sales price and average electricity price. The boundary constraints of the purchase price are:

[0079] γe_buy,min≤γe_buy,i,t≤γe_buy,max

[0080] γe_buy,i,t is the price at which shared energy storage sells electricity to integrated energy supplier i; γe_buy,min is the minimum purchase price; γe_buy,max is the maximum purchase price.

[0081] The boundary constraints of the electricity price are:

[0082] γe_sell,min≤γe_sell,i,t≤γe_sell,max

[0083] γe_sell,i,t is the price at which integrated energy supplier i sells electricity to shared energy storage;

[0084] γe_sell,min is the minimum value of the electricity selling price;

[0085] γe_sell,max is the maximum value of the electricity selling price.

[0086] The average purchase / sale price constraint is:

[0087]

[0088] γe_buy,ave_max is the upper limit of the average price of electricity purchased by integrated energy supplier i from shared energy storage;

[0089] γe_sell,ave_max is the upper limit of the average price that integrated energy supplier i sells electricity to shared energy storage.

[0090] (2) Energy state of shared energy storage The energy balance constraints of shared energy storage are as follows:

[0091]

[0092] is the amount of electricity in the shared energy storage at time t = 1; is the charging efficiency of shared energy storage; t is the charging power of the shared energy storage; represents the discharge power of the shared energy storage; is the discharge efficiency of shared energy storage.

[0093] (3) The energy upper and lower bounds of shared energy storage are as follows:

[0094] in, is the energy value of the shared energy storage at time t, is the minimum value of shared energy storage energy; is the maximum value of the shared energy storage energy.

[0095] Charge and discharge power constraints:

[0096]

[0097] Among them, S flag It is the charge and discharge flag of shared energy storage, which is a 0-1 variable and is used to limit charging / discharging to only allowed at the same time; and They are the maximum charging power and the maximum discharging power respectively.

[0098] S02: A multi-layer game is constructed for the integrated energy supplier, producer alliance, and shared energy storage. The operation is as follows:

[0099] First: In "Fun_ESO", the price of shared energy storage is adjusted according to the power purchasing strategy of the integrated energy supplier. Multiple rounds of master-slave games are set up between shared energy storage and integrated energy operators, and prices and strategies are continuously adjusted until an equilibrium state is reached, and the equilibrium point is judged to be at the best point of supply and demand.

[0100] (1) Set an initial upper and lower bound for the electricity price of shared energy storage.

[0101] (2) Narrow the upper and lower bounds of the electricity price for the next iteration.

[0102] (3) Set Q (k-2,t) is the electricity price at time t in the k-2th iteration, Q (k-1,t) is the electricity price at time t in the k-1th iteration. The electricity price range for the kth iteration is set to be, Q (k-2,t) is the upper bound, Q (k-1,t)As the lower bound, the kth iteration obtains the electricity price Q (k,t) .

[0103] like Figure 2 As shown in the figure, the upper and lower bounds of the electricity price are gradually updated in multiple rounds of iterations. (k,t) =Q (k-2,t) When the k+1th round electricity price Q (k+1,t) =(Q (k,t) +Q (k-1,t) ) / 2, and the price range of the k+2th round is not updated; when Q (k,t) ≠Q (k-2,t) When , the electricity price range of the k+1th round is updated.

[0104] Q (k,t) ≤Q (k+1,t) ≤Q (k-1,t)

[0105] (4) Initialize various parameters, including the number of iterations, the maximum number of iterations, the convergence threshold, historical transaction price records, historical transaction power records, the price at which the service provider purchases electricity from the main grid and sells electricity to the main grid, the upper and lower limits of the transaction price between the service provider and the shared energy storage, and the constraints required for the dichotomy method.

[0106] (5) During the iteration process, each iteration first checks whether the maximum number of iterations has been reached or the convergence condition has been met. If so, the iteration ends.

[0107] If the transaction price remains unchanged in consecutive iterations, the price is adjusted by narrowing the upper and lower bounds of the electricity price for the next iteration as mentioned above; if the price changes, the energy purchase strategy and transaction price are updated respectively until the strategy converges.

[0108] Second: In the model, “Fun_LA” receives the output of “Fun_ESO” as input; conversely, “Fun_ESO” receives the output of “Fun_LA” as input, and the convergence strategy reaches equilibrium.

[0109] Update transaction power and interaction price in the following ways:

[0110] X(iter+1)=Fun_ESO(Power_buy(iter),Power_sell(iter)),

[0111] Power_buy(iter),Power_sell(iter)=Fun_LA(X(iter)),X(iter-1)

[0112] Among them, iter represents the iteration number, Power_buy and Power_sell are the purchase and sale of electricity from shared energy storage by the integrated energy supplier, and X represents the electricity price.

[0113] Based on the electricity price of the parameters in the previous step, shared energy storage and operators update their strategies respectively, and the first level of the game process ends.

[0114] Third: Construct the second-level master-slave game between constraint model 2 and constraint model 3, that is, the master-slave game between operators and production consumers, which is nested in the first-level game.

[0115] When operators adjust their electricity usage and purchasing strategies based on shared energy storage, they price the energy usage of prosumers, and prosumers feed back their electricity usage to the operators.

[0116] (1) Set the KKT conditions, including the producer-consumer constraints. The Lagrangian function with respect to p cut,i The partial derivative expression of 0.5-λ e,i -μ i1,lb +μ i1,ub =0.

[0117] Among them, p cut,i is the electricity curtailed by prosumer i, λ e,i is the Lagrange multiplier of the electricity price of producer consumer i, μ i1,lb and μ i1,ub It is about p cut,i The lower and upper bounds of the Lagrange multipliers, 0.5 is the benefit per unit of electricity reduction.

[0118] Lagrangian function with respect to p tran,ij The partial derivative expression of: p tran,ij Represents the electric energy transferred from the i-th producer and consumer to the j-th producer and consumer.

[0119] λ e,i -λ e,j -μ ij,lb +μ ij,ub =0

[0120] Among them, μ ij,lb and μ ij,ub It is about p ("tran",ij) The lower and upper Lagrange multipliers of λ e,i is the Lagrange multiplier of the electricity price of producer consumer i, λ e,j is the Lagrange multiplier of the electricity price for producer consumer j.

[0121] Lagrangian function with respect to h cut,i The partial derivative expression of: h cut,iis the power load of prosumer i 0.4-λ h,i -μ i5,lb +μ i5,ub =0.

[0122] Among them, λ h,i is the Lagrange multiplier of the heat price of prosumer i, μ i5,lb and μ i5,ub It's about h ("cut",i) The lower and upper Lagrange multipliers of , 0.4 is the gain per unit heat reduction.

[0123] Lagrangian function with respect to p load,i The partial derivative expression of: p load,i is the heat pri reduced by producer consumer i e,i -λ e,j -μ i6,lb +μ i6,ub =0

[0124] Among them, pri e,i is the electricity price of producer consumer i, μ i6,lb and μ i6,ub It is about p ("load",i) The lower and upper bounds of Lagrange multipliers.

[0125] Lagrangian function with respect to h load,i The partial derivative expression of: h load,i is the heat load of producer consumer i: pri h,i -λ h,j -μ i7,lb +μ i7,ub =0.

[0126] Among them, pri h,i is the heat price of prosumer i, μ i7,lb and μ i7,ub It's about h load,i The lower and upper bounds of Lagrange multipliers.

[0127] λ e,i and λ h,j are the Lagrange multipliers of production consumers i and j, respectively, corresponding to the electricity and heat balance constraints of production consumers i and j.

[0128] μ is the Lagrange multiplier for the lower and upper bounds of each decision variable.

[0129] (2) To linearize nonlinear constraints, the Big-M method was used.

[0130] Set a very large constant M and a variable b, b∈[0-1], to simulate absolute value and inequality constraints. Introduce an artificial variable b and set inequality constraints.

[0131] The KKT conditions after linearization based on the Big-M method are as follows:

[0132] 0≤p cut,i ≤M·b i1,jb ,0≤u i1,jb ≤M·(1-b i1,jb )

[0133] 0≤p cut,i -0.15p L,i ≤M·b i1,ub ,0≤u i1,ub ≤M·(1-b i1,ub )

[0134] 0≤h load,j -2000≤M·b i7,ub ,0≤u i7,ub ≤M·(1-b i7,ub )

[0135] Among them, p cut,i and p load,i are the electricity and heat cut by producer consumer i, h cut,i and h load,i are the electricity and heat loads of producer consumer i respectively. L,i The load of producer and marketer i, b i1,ub , b i1,ub , b i7,ub The value of is 0 or 1, and is the variable used to linearize the inequality.

[0136] For each inequality constraint, artificial variables and the big M method are introduced for linearization. For example, for the inequality p_(cut,i)≤0.15p_(L,i):

[0137] Introduce the variable b_(i1,ub), whose value is 0 or 1.

[0138] Set constraints:

[0139] p_(cut,i)-0.15p_(L,i)≤M·b_(i1,ub)

[0140] u_(i1,ub)≤M·(1-b_(i1,ub))

[0141] Among them, u_(i1,ub) is the slack variable used to measure the degree of violation of the inequality.

[0142] Construct a new objective function, add the penalty term of artificial variables on the basis of the original objective function, and ensure that M is large enough to encourage the b variable to take the minimum value.

[0143] minimize Z=c T x+M·b, where c T x is the original objective function.

[0144] (3) Set the linear optimization algorithm conditions. For the linear constraints of electricity cost and heat cost, for all production consumers i∈{1,2,3} and all time periods t∈{1,2,...,24}, the electricity cost X ei (i,t) constraints satisfy:

[0145] X ei (i,t)≥"pri_sell"(t)·p "load"(i,t) +"pri_e"i(i,t)·L-"pri_sell"(t)·L

[0146] represents the lower bound of the electricity cost of prosumer i at time t, taking into account the sales price and the local electricity price.

[0147] X ei (i,t)≥"pri_buy"(t)·p "load"(i,t) +"pri_e"i(i,t)·U-"pri_buy"(t)·U

[0148] represents the upper bound on the electricity cost of prosumer i at time t, taking into account the purchase price and the local electricity price.

[0149] X ei (i,t)≤"pri_buy"(t)·p "load"(i,t) +"pri_e"i(i,t)·L-"pri_buy"(t)·L

[0150] represents the lower bound of the electricity cost of prosumer i at time t, taking into account the purchase price and the local electricity price.

[0151] X ei (i,t)≤"pri_e"i(i,t)·U+"pri_sell"(t)·p "load"(i,t) -"pri_sell"(t)·U

[0152] represents the lower bound of the electricity cost of prosumer i at time t, taking into account the sales price and the local electricity price.

[0153] Heat costX hi (i,t) satisfies:

[0154] Xhi (i,t)≥α·h "load" (i,t)+"pri_h"i(i,t)·L-α·L

[0155] X hi (i,t)≥β·h "load" (i,t)+"pri_h"i(i,t)·U-β·U

[0156] X hi (i,t)≤β·h "load" (i,t)+"pri_h"i(i,t)·L-β·L

[0157] X hi (i,t)≤"pri_h"i(i,t)·U+α·h "load" (i,t)-α·U

[0158] In the formula, X ei (i, t) and X hi (i, t) represent the electricity and heat costs of production consumer i at time t, “pri_sell” (t) and “pri_buy” (t) represent the electricity selling price and purchase price at time t, “pri_e” i (i, t) and “pri_h” i (i, t) represent the electricity and heat prices of production consumer i at time t, respectively. "load" (i,t) and h "load" (i, t) represents the electricity and heat loads of production consumer i at time t, L and U represent the lower and upper bounds of the decision variables, such as the lower and upper bounds of the electricity load, and α and β represent the lower and upper bounds of the heat load, respectively.

[0159] Set the conversion relationship between electrical load and thermal load

[0160] For example, p_"load"(i,t)=k·h_"load"(i,t), where k is the thermoelectric conversion coefficient.

[0161] After the optimization of the previous two layers, the electricity shared between producers and consumers can be obtained, but the electricity price when sharing electricity between producers and consumers still needs to be determined.

[0162] A new variable is introduced to represent the shared electricity price, “pri_share” (i,j,t): the electricity sharing price between production consumers i and j at time t.

[0163] “pri_share”(i,j,t)≥“pri_sell”(t): the shared electricity price is not lower than the market selling price.

[0164] “pri_share”(i,j,t)≤“pri_buy”(t): the shared electricity price is not higher than the market purchase price.

[0165] The objective function is set to minimize the total cost of electricity and heat for all production consumers in all time periods.

[0166] minimize Z=∑(X_ei(i,t)+X_hi(i,t))

[0167] Considering power sharing, the objective function is as follows:

[0168] minimize Z=∑(X_ei(i,t)+X_hi(i,t))+∑(“pri_share”(i,j,t)·p_share(i,j,t))

[0169] Where p_share(i,j,t) is the shared power between production consumers i and j at time t.

[0170] Fourth: Construct the third-level cooperative game of electricity price strategy game among producers and consumers within the producer-consumer alliance.

[0171] By using ADMM to decompose the electricity price problem into multiple sub-problems, each producer and consumer can independently optimize their own electricity price strategy. The original optimization problem is transformed into an augmented Lagrangian function and iterated through alternating optimization strategies; in each iteration, ADMM updates the Lagrangian multiplier. The details are as follows:

[0172] (1) Initialization parameters, including electricity price, Lagrange multiplier, objective function value, etc.

[0173] (2) Enter the iterative process until any of the following conditions is met and the maximum number of iterations is reached;

[0174] (3) End the iteration and output warning information;

[0175] (4) Calling the electricity price optimization function to solve the latest electricity price and objective function value;

[0176]

[0177] C i (p i )=a i ·p i 2 +b i ·p i +c i

[0178] enter:

[0179] The current electricity price vector p k .

[0180] The current Lagrange multiplier λ k .

[0181] The current objective function coefficient (such as a i ,b i ,c i ).

[0182] Output:

[0183] The latest electricity price vector p k+1 .

[0184] The latest objective function value f(p k+1 ).

[0185] (5) Update the Lagrange multiplier and save the objective function value to historical data.

[0186] (6) Calculate the residual, which is the norm of the difference between the current electricity interaction price and the electricity interaction price of the previous iteration.

[0187] If the residual is less than the preset convergence accuracy, the iteration ends and the convergence information is output. If the end condition is not met, the number of iterations is increased, and then the solution of the electricity price and the objective function value is returned for the next iteration.

[0188] When the residual is less than the preset convergence accuracy, the iteration is considered to have converged and the convergence information is output.

[0189] When the maximum number of iterations is reached but the convergence condition is not met, an alarm message is output

[0190] The above steps take into account the degree of constraint violation in the current iteration and gradually reduce the difference between the constraints of the original problem and the optimized solution.

[0191] Embodiment 2

[0192] like Figure 3 As shown, a multi-layer game scheduling system for an integrated energy system includes the following modules:

[0193] Construct a multi-party constraint model, obtain parameters and variables centered on shared energy storage, integrated energy suppliers, and producer-consumer alliances, and respectively construct constraint model 1 composed of parameters and variables centered on the shared energy storage, constraint model 2 composed of parameters and variables centered on the integrated energy supplier, and constraint model 3 composed of parameters and variables centered on the producer-consumer alliance;

[0194] Construct a multi-layer game relationship model, respectively construct the first-layer master-slave game between constraint model 1 and constraint model 2, the second-layer master-slave game between constraint model 2 and constraint model 3, and the third-layer cooperative game within constraint model 3;

[0195] Output the game results, and output the power purchase strategy and electricity price at a certain moment after the multi-layer game of the relational model.

[0196] The beneficial effects of this application are as follows: (1) It fully considers the energy and strategy management of each party in the game, including shared energy storage, integrated energy suppliers, and production and consumer alliances, builds a reasonable game model, designs a multi-layer orderly game process, and optimizes energy scheduling;

[0197] (2) For each layer of the game model, different analysis methods are adopted to reasonably and effectively improve energy utilization.

[0198] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with the art within the technical scope disclosed in the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A comprehensive energy multi-layer game scheduling method for park power scheduling, characterized in that: The following steps are involved: Obtain parameters and variables centered on shared energy storage, integrated energy suppliers, and producer-consumer alliances, and respectively construct constraint model 1 composed of parameters and variables centered on the shared energy storage, constraint model 2 composed of parameters and variables centered on the integrated energy supplier, and constraint model 3 composed of parameters and variables centered on the producer-consumer alliance; Construct a first-level master-slave game between constraint model 1 and constraint model 2, with the buying and selling electricity prices as the main optimization variables in Fun_ESO and Fun_LA, and the electricity purchasing strategy as the optimization target, which are mutually input and output, and perform multiple iterations within the initial electricity price upper and lower bounds set by the shared energy storage to reach an equilibrium state; Construct a second-level master-slave game between constraint model 2 and constraint model 3, using the power consumption fed back to the integrated energy supplier by the production and consumption alliance and participating in the first-level master-slave game and the power purchase strategy and power purchase amount obtained by the integrated energy supplier in the first-level master-slave game to adjust the power price and energy price of the production and consumption alliance; Construct the third-level cooperative game based on the electricity price strategy game between producers and consumers within the producer-consumer alliance.

2. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: The first-level master-slave game adopts the dichotomy method to analyze the master-slave game between the power purchasing strategy provided by the integrated energy supplier and the electricity price adjustment of the shared energy storage, and iterates multiple times in the constructed upper and lower bounds of the electricity price to balance the power purchasing strategy and the electricity price, so as to obtain the electricity price and the purchased electricity volume at the equilibrium moment.

3. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: In the second-level master-slave game, KKT conditions are set for the constraint model 2, and the Big-M method is used to linearize the nonlinear constraints of the KKT conditions to obtain the shared power of the production and consumer alliance.

4. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: The third-level cooperative game is to decompose the electricity price problem to be solved into sub-problems in which each producer and consumer in the producer and consumer alliance can independently optimize the electricity price strategy, and iterate the objective function multiple times to obtain the latest electricity price.

5. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: The constraint model 1 is a set of constraints consisting of the energy consumption-output relationship between the comprehensive energy supplier and the cogeneration unit, the relationship between the power generation power and gas consumption of the cogeneration unit and the heat generation power and the power consumption and heat generation power of the gas boiler, the actual power output of the renewable energy equipment, its abandoned power and predicted power output, the upper and lower limits of the equipment rated power, the electricity selling price, the heat selling price, the power balance constraints constructed for each supplier, the power exchange constraints for each pair of suppliers and the power exchange balance constraints.

6. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: The constraint model 2 is a set of constraints consisting of the upper and lower limit constraints on the electric load that can be reduced by each production consumer in the consumer alliance, the power balance constraint on the load-side purchased electricity, the upper and lower limit constraints on the heat load that can be reduced, the power balance constraint on the load-side purchased heat, the upper and lower limit constraints on the purchased electricity heat, and the upper and lower limit constraints on the electric energy interaction of each pair of production consumers.

7. The multi-layer game scheduling method for comprehensive energy according to claim 1, characterized in that: The constraint model three is a set of constraints consisting of the boundary constraints of the electricity purchase price, the boundary constraints of the electricity sales price, the average purchase / sales price constraints, the energy balance constraints, the upper and lower bounds of energy constraints, and the charging and discharging power constraints in the shared energy storage.

8. A comprehensive energy multi-layer game dispatching system for park power dispatching, characterized in that: The multi-layer game scheduling system includes the following modules: Construct a multi-party constraint model, obtain parameters and variables centered on shared energy storage, integrated energy suppliers, and producer-consumer alliances, and respectively construct constraint model 1 composed of parameters and variables centered on the shared energy storage, constraint model 2 composed of parameters and variables centered on the integrated energy supplier, and constraint model 3 composed of parameters and variables centered on the producer-consumer alliance; Construct a multi-layer game relationship model, respectively construct the first-layer master-slave game between constraint model 1 and constraint model 2, the second-layer master-slave game between constraint model 2 and constraint model 3, and the third-layer cooperative game within constraint model 3; Output the game results, and output the power purchase strategy and electricity price at a certain moment after the multi-layer game of the relational model.

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