Community integrated energy system scheduling method

Abstract

The invention discloses a community integrated energy system scheduling method, and relates to the field of community integrated energy systems. The method comprises the following steps: determining that transaction subjects of the community integrated energy system comprise energy suppliers and load aggregators; a step carbon transaction mechanism is considered, and an optimized operation model of the energy supplier is established with the purpose of maximizing the operation income; a demand side response is considered, and an optimized operation model of the load aggregator is established by taking the minimum operation cost as a target; according to the two optimized operation models, establishing a Stackelberg game model taking an energy supplier as a leader and a load aggregator as a follower; and solving the Stackelberg game model by adopting an alternating direction multiplier method distribution, obtaining an optimal scheduling strategy of the community integrated energy system, and scheduling the community integrated energy system. The method improves the calculation efficiency, and achieves the efficient and economical operation of the community integrated energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of community integrated energy systems, in particular to a community integrated energy system scheduling method. BACKGROUND

[0002] Integrated Energy Systems (IES) as an innovative model of energy transformation aims to achieve multi-energy complementation and resource sharing by integrating renewable energy, traditional energy and energy storage devices, so as to improve energy utilization efficiency and reduce carbon emissions. Through decision optimization, IES can achieve a comprehensive balance of economy, environmental protection and reliability while ensuring energy supply, and is an inevitable choice for future energy industry development. At present, the energy market is changing from a traditional vertical integrated structure to an interactive competitive structure, and the distributed characteristics of IES are becoming more and more obvious. Existing research mainly focuses on centralized optimization operation of IES. The characteristics of multi-energy flow coupling and multi-agent participation of IES result in a large number of parameters and variables of the optimization problem. Centralized optimization puts high requirements on data transmission, communication and processing capacity of the scheduling center. With the increase of problem size, centralized optimization scheduling will bring problems such as insufficient computing and storage capacity to the scheduling center.

[0003] Community Integrated Energy System (CIES) is the landing practice of the concept of integrated energy in the spatial dimension and is becoming a research hotspot. CIES is a comprehensive optimization system that integrates multiple energy forms (such as electricity, heat, cold, gas, etc.) and realizes the production, conversion, storage, transmission and consumption of energy in the community. Through intelligent power grids, heat pipe networks and natural gas pipe networks, energy is delivered to each user in the community. The centralized optimization scheduling of CIES also has the problem of insufficient computing capacity. SUMMARY

[0004] The purpose of the present application is to provide a community integrated energy system scheduling method, which can improve the computing efficiency and realize efficient and economic operation of the community integrated energy system.

[0005] To achieve the above purpose, the present application provides the following solutions:

[0006] The application provides a community integrated energy system scheduling method, comprising the following steps: determining a transaction subject of a community integrated energy system; the transaction subject comprises an energy supplier and a load aggregator; considering a ladder carbon transaction mechanism, an optimal operation model of the energy supplier is established with the maximum operation benefit as a target; considering demand side response, an optimal operation model of the load aggregator is established with the minimum operation cost as a target; a Stackelberg game model with the energy supplier as a leader and the load aggregator as a follower is established according to the optimal operation model of the energy supplier and the optimal operation model of the load aggregator; the Stackelberg game model is solved by using an alternating direction multiplier method, and an optimal scheduling strategy of the community integrated energy system is obtained; the optimal scheduling strategy comprises an energy price issued by the energy supplier to the load aggregator and energy purchasing of the load aggregator to the energy supplier; and the community integrated energy system is scheduled according to the optimal scheduling strategy.

[0007] According to the specific embodiments provided in the application, the application has the following technical effects:

[0008] The application provides a community integrated energy system scheduling method, the alternating direction multiplier method decomposes a large-scale calculation task into multiple subtasks, distributes the subtasks to different calculation nodes and executes the subtasks in parallel, significantly shortens the calculation time, improves the calculation efficiency, and realizes efficient operation of the community integrated energy system; the Stackelberg game model with the energy supplier as a leader and the load aggregator as a follower is constructed, multiple participants in the community integrated energy system interact through game, and finally realize low-carbon economic optimal scheduling of each transaction subject, so as to realize economic operation of the community integrated energy system. BRIEF DESCRIPTION OF DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the application or the related art, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0010] Figure 1 A flowchart of a community integrated energy system scheduling method provided by an embodiment of the application is shown in the figure;

[0011] Figure 2 An alternating direction multiplier method flowchart provided by another embodiment of the application is shown in the figure;

[0012] Figure 3 A CIES structure diagram is shown in the figure;

[0013] Figure 4 A game architecture diagram of energy transaction of each operator is shown in the figure;

[0014] Figure 5 This is a schematic diagram of load and photovoltaic power generation prediction;

[0015] Figure 6 This is a schematic diagram of outdoor temperature prediction;

[0016] Figure 7 Schematic diagram of the convergence results of the objective functions of the energy supplier (Energy Generation Operator, EGO) and the load aggregator (Load Aggregator, LA);

[0017] Figure 8 Publish electricity price diagram for EGO;

[0018] Figure 9 A diagram of the heat price situation for EGO;

[0019] Figure 10 This is a schematic diagram of the EGO electrical energy and thermal energy optimization scheduling results;

[0020] Figure 11 Schematic diagram of the optimized scheduling results of electric energy and thermal energy. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0023] In an exemplary embodiment, Figure 1 As shown, a community integrated energy system scheduling method is provided, including the following steps 101 to 106.

[0024] Step 101: Determine the transaction entities of the community integrated energy system; the transaction entities include energy suppliers and load aggregators.

[0025] Step 102: Considering the tiered carbon trading mechanism and aiming at maximizing operating profits, an optimal operation model for energy suppliers is established.

[0026] Step 103: Considering the demand-side response and taking the minimum operating cost as the goal, an optimized operation model of the load aggregator is established.

[0027] Step 104: according to the optimized operation model of the energy supplier and the optimized operation model of the load aggregator, a Stackelberg game model is established with the energy supplier as the leader and the load aggregator as the follower.

[0028] Step 105: the Stackelberg game model is solved by using an alternating direction multiplier method to obtain an optimal scheduling strategy of the community integrated energy system; the optimal scheduling strategy includes an energy price issued by the energy supplier to the load aggregator and energy purchased by the load aggregator from the energy supplier.

[0029] Step 106: according to the optimal scheduling strategy, the community integrated energy system is scheduled.

[0030] By implementing the above steps 101 to 106, the present application considers factors such as step-by-step carbon trading, integrated demand response and energy storage devices from the perspective of economic benefits of each transaction subject (also referred to as interest subject) in the community integrated energy system, embeds the energy supplier and load aggregator transaction process into a master-slave game framework, constructs a multi-participant energy transaction mechanism with EGO as the leader and LA as the follower, and finally realizes low-carbon economic optimal scheduling of each interest subject in the system through game interaction of multiple participants in the CIES.

[0031] In another exemplary embodiment of the present application, in the community integrated energy system structure model, a gas boiler (GB) and a gas turbine (GT) are used as energy supply devices of the energy supplier. A battery is used as an energy storage device in the system. A photovoltaic panel is installed on the roof of each user community, and an electric heater is provided indoors. A load aggregator is an agent for users in the community to participate in energy transactions. The internal energy transaction subjects of the community integrated energy system include EGO and LA.

[0032] In another exemplary embodiment of the present application, the step-by-step carbon trading mechanism is divided into three stages of allocating an initial carbon emission quota (free carbon emission quota), calculating an actual carbon emission amount and calculating a carbon trading cost. The step-by-step carbon trading mechanism includes steps 201 to 203.

[0033] Step 201: using a production-based benchmark method, an initial carbon emission quota allocated to the energy supplier is determined by using a formula and ; in the formula, E G is the initial carbon emission quota allocated to the energy supplier, E G,grid is a carbon emission quota obtained by the energy supplier from a superior power grid, and E G,gtgb is a carbon emission quota obtained by the energy supplier from internal unit output power; χ gridThe carbon emission quota coefficient of the external coal-fired power unit per unit of electric power, respectively, the power purchased and sold by the energy supplier from the external power grid at time t, Δt is a time interval, and T is the time scheduling period; h The carbon emission quota coefficient of the gas turbine and the gas boiler per unit of heat power, e,h The electric-heat conversion coefficient, respectively, the electric power and the heat power output by the gas turbine at time t, The heat power output by the gas boiler at time t.

[0034] Step 202: determining the carbon emission of the energy supplier by using the formula and The carbon emission of the energy supplier, The carbon emission of the energy supplier, The carbon emission of the external coal-fired power unit, The carbon emission of the internal power unit of the energy supplier; The carbon emission coefficient of the external coal-fired power unit per unit of electric power; The carbon emission coefficient of the internal power unit of the energy supplier per unit of heat power, gt,t , g gb,t respectively, the natural gas volume consumed by the gas turbine and the gas boiler at time t.

[0035] Step 203: determining the carbon trading cost of the energy supplier by using the formula according to the initial carbon emission quota allocated to the energy supplier and the carbon emission of the energy supplier; wherein, F G,c The carbon trading cost of the energy supplier, γ is the carbon trading base price, l is the length of the carbon emission interval, and σ is the carbon trading penalty coefficient.

[0036] In another exemplary embodiment of the present application, the EGO optimizes the sold electric power, the sold heat power, the sold electric price and the sold heat price published to the LA, aiming at the maximum operation benefit, according to the time-of-use electricity price and the scheduling plan of the LA.

[0037] The optimization operation model of the energy supplier includes: the objective function of the energy supplier and the constraint condition of the energy supplier. The constraint condition of the energy supplier includes: the energy price publishing constraint of the energy supplier, the internal energy supply device constraint of the energy supplier and the power balance constraint of the energy supplier.

[0038] The objective function of the energy supplier is:

[0039]

[0040] F G,gas,t =C gas (g gt,t +ggb,t )Δt;

[0041] where F G is the energy supplier's revenue in a dispatch cycle, F G,int,t is the energy supplier's revenue from selling energy to the load aggregator at time t, F G,grid,t is the energy supplier's cost of trading with the external grid at time t; F G,gas,t is the energy supplier's cost of purchasing energy at time t, F G,c is the energy supplier's carbon trading cost; is the energy supplier's selling electricity price, selling heat price to the nth load aggregator at time t, is the energy supplier's selling electricity power, selling heat power to the nth load aggregator at time t, Δt is a time interval, and N represents the set of load aggregators in the community integrated energy system; is the time-of-use price of the grid at time t, the on-grid price, is the energy supplier's purchasing power from the grid, selling power to the grid at time t, respectively; gas is the gas price, g gt,t , g gb,t is the volume of natural gas consumed by the gas turbine, gas boiler at time t, respectively.

[0042] The energy supplier publishes the energy price constraint as:

[0043]

[0044] where C h,min , C h,max are the minimum and maximum heat energy prices in the heat energy market.

[0045] The energy supplier's internal energy supply equipment constraints are:

[0046]

[0047]

[0048] wherein is the output power of the gas turbine at time t, the output heat power of the gas turbine, is the output heat power of the gas boiler at time t, is the power generation efficiency coefficient of the gas turbine, the heat generation efficiency coefficient of the gas turbine, is the heat generation efficiency coefficient of the gas boiler, q gas is the calorific value of natural gas; is the power generation capacity of the gas turbine, is the heat generation capacity of the gas boiler.

[0049] The energy supplier's power balance constraint is:

[0050]

[0051] In another exemplary embodiment of the present application, the above step 103 can be replaced by steps 301-305.

[0052] Step 301: Establish the electric energy load of the load aggregator after demand response as:

[0053]

[0054] In the formula, P is the electric energy load of the user after demand response at time t, P is the predicted power of the user load, P and P are the transferred load power and the curtailed load power of the nth load aggregator at time t, respectively. are the maximum transferred load power and the maximum curtailed load power, respectively, and T is the time scheduling period.

[0055] Step 302: Establish the objective function of the load aggregator as:

[0056]

[0057] In the formula, F n is the operation cost of the nth load aggregator in a scheduling period, F n,int,t is the operation cost of the nth load aggregator at time t, F n,ess,t is the energy storage capacity leasing cost of the nth load aggregator at time t, F n,com,t is the user comfort penalty cost of the nth load aggregator at time t, F n,dr,t is the demand response cost of the nth load aggregator at time t. are the electricity purchase price and the heat purchase price of the nth load aggregator at time t from the energy supplier, respectively. are the electricity purchase power and the heat purchase power of the nth load aggregator at time t from the energy supplier, respectively, and Δt is a time interval. ess is the energy storage operation and maintenance cost coefficient; η com is the user comfort penalty coefficient. are the charging power and the discharging power of the energy storage device of the nth load aggregator at time t. is the indoor temperature of the user at time t, τ best is the optimal indoor temperature of the user; η tran , η cut are the transferred load compensation coefficient and the curtailed load compensation coefficient, respectively.

[0058] Step 303: Establish the electric-thermal power balance constraint according to the electric energy load of the demand response of the load aggregator:

[0059]

[0060]

[0061] wherein, is the electric energy power consumed by the electric heater of the user; is the predicted output power of the community roof photovoltaic panel; is the thermal energy power output by the electric heater of the user; η e,h is the electric heating efficiency of the electric heater; is the indoor heating power of the user.

[0062] Step 304: Establish the energy storage device constraint as:

[0063]

[0064] μ ch + μ dis ≤ 1;

[0065] wherein, S soe,t+1 is the energy state of the energy storage device at t+1, S soe,t is the energy state of the energy storage device at t; κ ch , κ dis are the charging efficiency and discharging efficiency of the energy storage device, respectively; are the minimum state of charge and maximum state of charge of the energy storage device, respectively; is the maximum electric energy transmission power of the energy storage device and the nth load aggregator; μ ch , μ dis are Boolean variables representing the charging and discharging states of the energy storage device, respectively.

[0066] Step 305: The objective function of the load aggregator, the electric-thermal power balance constraint and the energy storage device constraint are combined to form the optimization operation model of the load aggregator.

[0067] For example, the heat inertia difference equation of the indoor temperature of the community user is:

[0068]

[0069] wherein, k1, k2, k3 are the coefficients of the heat inertia difference equation; is the indoor and outdoor temperature of the user; is the indoor heating power of the user; the indoor temperature range of the user is

[0070] The thermal inertia difference equation describes the relationship between a user's indoor and outdoor temperatures and the user's indoor heating power. The user's indoor heating power is proportional to the temperature difference between the indoor and outdoor temperatures. When the outdoor temperature remains constant, the variation in indoor temperature within a reasonable range determines the amount of heat purchased by the LA from the EGO.

[0071] The thermal inertia difference equation indirectly affects the indoor temperature through the purchase of heat power from LA to EGO, and the objective function of LA F n The user comfort penalty cost F is included n,com,t . F n,com,t is a function of the indoor temperature, so the thermal inertia difference equation is related to F n Established contact.

[0072] In another exemplary embodiment of the present application, the Stackelberg game model is:

[0073] {(EGO∪LA);(F EGO ∪F LA );(I EGO ∪I LA )};

[0074] Where EGO represents the energy supplier, LA represents the load aggregator, and F EGO represents the objective function of the energy supplier, F LA represents the objective function of the load aggregator; I EGO represents the game strategy of the energy supplier, I LA Represents the gaming strategy of the load aggregator.

[0075] Among all strategy combinations, if there is a strategy such that the objective functions of EGO and LA satisfy:

[0076]

[0077] This means that the Stackelberg game has reached equilibrium. represents the Nash equilibrium strategies of EGO and LA.

[0078] In another exemplary embodiment of the present application, in order to reduce the number of branch and boundary iterations required to solve complex nonlinear problems and reduce computing time and consumption of computing resources, before the above step 105, the method may further include the following steps 401 to 403.

[0079] Step 401: Linearize the nonlinear terms in the Stackelberg game model based on the McCormick envelope method, and obtain the linearized terms on the energy supplier side: And the linearization term on the load aggregator side is: Where, is the nonlinear term replacement variable for the energy supplier side regarding the energy i transaction, is the energy i selling price of the energy supplier to the nth load aggregator at time t, is the energy i selling power of the energy supplier to the nth load aggregator at time t; is the nonlinear term replacement variable for the load aggregator side regarding the energy i transaction, is the energy i buying price of the nth load aggregator from the energy supplier at time t, is the energy i buying power of the nth load aggregator from the energy supplier at time t;

[0080] Step 402: the relaxation supplementary constraint satisfied by the linearization term of the energy supplier side is established as:

[0081]

[0082]

[0083] wherein, are the maximum power and the minimum power of the energy i transmission between the energy supplier and the nth load aggregator, respectively; are the maximum value and the minimum value of the energy i price at time t.

[0084] Step 403: the relaxation supplementary constraint satisfied by the linearization term of the load aggregator side is established as:

[0085]

[0086] wherein, are the maximum power and the minimum power of the energy i purchase of the nth load aggregator from the energy supplier.

[0087] In another exemplary embodiment of the present application, with reference to Figure 2 the above step 105 can be replaced by the following steps 501-511.

[0088] Step 501: the consistency constraint of the energy power and price transaction between the energy supplier and the load aggregator is established as:

[0089]

[0090] wherein, is the energy i selling power of the energy supplier to the nth load aggregator at time t, is the energy i buying power of the nth load aggregator from the energy supplier at time t, is the virtual variable of the energy i power between the nth load aggregator and the energy supplier at time t; the energy i sold by the energy supplier to the nth load aggregator at time t, the price of energy i bought by the nth load aggregator from the energy supplier at time t, the virtual variable of the price of energy i between the nth load aggregator and the energy supplier at time t.

[0091] When the transaction is reached, the power and price of energy bought by the LA from the EGO need to be consistent with the power and price of energy sold by the EGO to the LA.

[0092] Step 502: according to the Stackelberg game model and the consistency constraint, an energy supplier side sub-problem and a load aggregator side sub-problem are established.

[0093] Step 503: global variables, Lagrange multipliers and iteration numbers are initialized; the global variables include and

[0094] Figure 2 the input related parameters shown, including iteration numbers, convergence margin values, penalty factors and iteration number initial values.

[0095] Step 504: according to the initialized global variables and the initialized Lagrange multipliers, the energy supplier side sub-problem is solved to obtain the price of energy sold by the energy supplier to the load aggregator at this iteration.

[0096] Step 505: according to the price of energy sold by the energy supplier to the load aggregator, the initialized global variables and the initialized Lagrange multipliers, the load aggregator side sub-problem is solved to obtain the power of energy bought by the load aggregator from the energy supplier at this iteration; the power of energy bought is the power of energy.

[0097] Step 506: according to the price of energy sold by the energy supplier to the load aggregator at this iteration and the power of energy bought by the load aggregator from the energy supplier at this iteration, the global variables are updated by using the formula and ; in the formula, is the virtual variable of the power of energy i between the nth load aggregator and the energy supplier at time t in the k+1th iteration, is the power of energy i sold by the energy supplier to the nth load aggregator at time t in the k+1th iteration, is the power of energy i bought by the nth load aggregator from the energy supplier at time t in the k+1th iteration, is the virtual variable of the price of energy i between the nth load aggregator and the energy supplier at time t in the k+1th iteration, is the energy i price at which the energy supplier sells energy to the nth load aggregator at time t in the k+1th iteration, is the energy i price at which the nth load aggregator buys energy from the energy supplier at time t in the k+1th iteration.

[0098] Step 507: updating the Lagrange multiplier according to the updated global variable.

[0099] For example, the updating formula of the Lagrange multiplier is:

[0100]

[0101] wherein, are the Lagrange multipliers of the energy i power sold by the energy supplier to the nth load aggregator at time t in the kth and k+1th iteration respectively, and p is the penalty factor; are the Lagrange multipliers of the energy i power bought by the nth load aggregator from the energy supplier at time t in the kth and k+1th iteration respectively, are the Lagrange multipliers of the energy i price sold by the energy supplier to the nth load aggregator at time t in the kth and k+1th iteration respectively, are the Lagrange multipliers of the energy i price bought by the nth load aggregator from the energy supplier at time t in the kth and k+1th iteration respectively.

[0102] Step 508: updating the residual according to the updated global variable.

[0103] For example, the updating formula of the residual is:

[0104]

[0105] wherein, is the residual of the energy i power sold by the energy supplier to the load aggregator in the k+1th iteration, is the residual of the energy i power bought by the load aggregator from the energy supplier in the k+1th iteration, is the residual of the energy i price sold by the energy supplier to the load aggregator in the k+1th iteration, is the residual of the energy i price bought by the load aggregator from the energy supplier in the k+1th iteration; || ||2 represents L2 norm.

[0106] Step 509: updating the dual residual according to the updated global variable.

[0107] For example, the updating formula of the dual residual is:

[0108]

[0109] wherein, is the dual residual of the traded energy price in the k+1th iteration, is the dual residual of the traded energy price in the k+1th iteration; is the virtual variable of the n-th load aggregator and the energy supplier regarding the power of energy i in the kth iteration, is the virtual variable of the n-th load aggregator and the energy supplier regarding the price of energy i in the kth iteration.

[0110] Step 510: If the updated residual or the updated dual residual does not satisfy the convergence determination condition, the iteration number is increased by 1, the initialized global variable is replaced by the updated global variable, the initialized Lagrange multiplier is replaced by the updated Lagrange multiplier, and the step 504 is returned.

[0111] Step 511: If the updated residual and the updated dual residual both satisfy the convergence determination condition, it is determined that the convergence is achieved, and the optimal dispatching strategy of the community integrated energy system is output.

[0112] For example, the convergence determination condition is:

[0113] ||r k+1 || 2 ≤r p ,||s k+1 || 2 ≤r c ;

[0114] wherein, ||r k+1 || 2 is the sum of the power residuals in the k+1th iteration, ||s k+1 || 2 is the sum of the price residuals in the k+1th iteration; r p is the power residual convergence margin, r c is the price residual convergence margin.

[0115] In an example, the process of establishing the energy supplier side sub-problem and the load aggregator side sub-problem in step 402 can be replaced by the following steps 601-603:

[0116] Step 601: According to the Stackelberg game model and the consistency constraint, the augmented Lagrange function is established as:

[0117]

[0118] wherein, L is the expression of the augmented Lagrange function, F G,tF is the operation cost of the energy supplier at time t, n,t F is the operation cost of the energy supplier at time t, is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint. is the Lagrange multiplier corresponding to the coupling constraint.

[0119] Step 602: According to the augmented Lagrange function, the energy supplier side sub-problem is determined as follows:

[0120]

[0121] Step 603: According to the augmented Lagrange function, the load aggregator side sub-problem is determined as follows:

[0122]

[0123] Based on the energy supplier side sub-problem given above, it can be seen that when the energy supplier side sub-problem is solved in step 505, the external grid time-of-use price, the user energy purchasing power (the first iteration considers the user energy purchasing power as 0), the internal unit operation parameters and the fuel price are used for solving. The internal unit output of the energy supplier, the interaction with the external grid and the energy selling price published to the LA are obtained. The energy selling price published to the LA is used for the LA to optimize its operation strategy in step 506.

[0124] Based on the load aggregator subproblem presented above, it can be seen that the solution to the load aggregator subproblem in step 506 is based on the energy price published by the EGO, the user load forecast, the community's PV output forecast, and the outdoor temperature. This results in the LA's energy purchases from the EGO, the charging and discharging of energy storage devices, and the user's demand response. This information is used by the EGO to optimize its operating strategy in the next iteration. Once the algorithm converges, the LA's energy purchases from the EGO, the charging and discharging of energy storage devices, and the demand response will guide the LA's optimized operation.

[0125] Global variables are used to coordinate the decomposed subproblems and enforce consistent solutions. Lagrange multipliers enforce constraints through a dynamic penalty mechanism, ensuring feasibility and accelerating convergence. Residuals and dual residuals are used to determine whether the algorithm has reached convergence. If the algorithm does not converge, the global variables and Lagrange multipliers are used in the next operator optimization subproblem.

[0126] EGO optimization strategy includes: interactive power with external power grid Operation status of units in EGO and Energy prices published to LA

[0127] LA optimization strategies include: Energy purchase from EGO Response to user needs and Charging and discharging to (from) energy storage devices

[0128] by Figure 3 The CIES structure shown and Figure 4 Taking the energy trading game architecture of each operator of CIES as an example, the load, photovoltaic power generation and outdoor temperature prediction obtained by the method of this application are as follows Figures 5-6 As shown, the convergence results of EGO and LA objective functions are as follows Figure 7 As shown, the electricity and heat prices published by EGO are as follows Figures 8-9 As shown, the optimization scheduling results of EGO, LA1 electric energy and thermal energy are as follows Figures 10-11 shown. Figure 10 Part (a) shows the EGO power optimization scheduling results. Figure 10 Part (b) shows the EGO thermal energy optimization scheduling results. Figure 11 Part (a) shows the LA1 power optimization scheduling results. Figure 11 Part (b) shows the results of LA1 thermal energy optimization scheduling.

[0129] To verify the superiority of the method proposed in the application in the field of CIES optimal scheduling, MATLAB modeling is used for simulation test, and the following comparative experimental scenarios are set:

[0130] Scenario 1: The CIES multi-agent distributed optimal scheduling method considering ladder carbon trading, energy storage devices and integrated demand response is adopted.

[0131] Scenario 2: The CIES multi-agent distributed optimal scheduling method of energy storage devices and integrated demand response is adopted, without considering the ladder carbon trading mechanism.

[0132] Scenario 3: The CIES multi-agent distributed optimal scheduling method considering ladder carbon trading and energy storage devices is adopted, without considering integrated demand response.

[0133] Scenario 4: The CIES multi-agent distributed optimal scheduling method considering ladder carbon trading and integrated demand response is adopted, without considering energy storage devices.

[0134] The EGO operating income, total energy cost of multiple LAs and CIES carbon emission under the four scenarios are shown in Table 1.

[0135] Table 1 Comparison of four scenarios

[0136]

[0137] Comparing scenario 1 and scenario 2, although considering the ladder carbon trading mechanism will cause the operating income of EGO to decrease by 8.3%, this is within an acceptable range, and the total energy cost of the two LAs only increases by 1.4%, the operating benefits and electricity cost of the two parties remain within the normal fluctuation range. After introducing the ladder carbon trading mechanism, the carbon emission of the system is significantly reduced by 590 kg, which shows that the ladder carbon trading introduced in the application can price carbon emissions, so that carbon emission subjects need to pay economic cost when emitting carbon, thereby prompting enterprises to pay attention to their own carbon emission behavior, reducing carbon emission, and promoting the overall society to change to a low-carbon lifestyle.

[0138] Comparing scenario 1 and scenario 3, the electric and thermal integrated demand response will increase the three indicators of EGO operator income, total energy cost of LA and system carbon dioxide emission, the EGO operator income increases by 4.5%, the total energy cost of LA decreases by 8.3%, and the system carbon emission decreases by 3.5%. This shows that the introduction of electric and thermal integrated demand response in the community integrated energy system can adjust the power demand according to the electricity price published by EGO, improve the overall flexibility of the system under the premise of achieving energy supply and demand balance, and improve the comprehensive benefits of enterprises and individuals; at the same time, the introduction of demand response can also indirectly reduce the dependence of the energy system on fossil fuels, which is helpful to reduce greenhouse gas emissions.

[0139] Comparing scenario 1 and scenario 4, the planning of energy storage devices in the integrated energy system can improve the EGO operator's income, the total energy cost of the LA, and the carbon dioxide emissions of the system, with the EGO operator's income increasing by 3.2%, the total energy cost of the LA decreasing by 17.6%, and the carbon dioxide emissions of the system decreasing by 1.3%. This shows that the planning of energy storage devices in the community integrated energy system according to the application can achieve optimized energy utilization and cost savings, improve energy use efficiency, and enhance power supply reliability.

[0140] The method of the application provides a community integrated energy system hierarchical distributed low-carbon economic dispatch strategy based on an alternating direction multiplier method. The application has obvious advantages in improving the economic benefits of all parties, reducing carbon emissions, and improving user energy flexibility. The beneficial effects of the method of the application mainly include the following points:

[0141] 1. The application effectively coordinates the interests of all stakeholders. The distributed algorithm decomposes large-scale computing tasks into multiple sub-tasks, distributes them to different computing nodes for parallel execution, significantly shortens the computing time, improves the computing efficiency, and realizes efficient and economic operation of the system.

[0142] 2. The application reduces the system carbon emissions by 5.2% by introducing a stepped carbon trading mechanism. This shows that by pricing different carbon emission intervals, the application encourages enterprises to pay attention to their carbon emission behavior, thereby effectively promoting the overall society to change to a low-carbon lifestyle. This mechanism not only helps achieve the "double carbon" goal, but also provides economic incentives for enterprises, promoting a win-win situation between environmental protection and economic benefits.

[0143] 3. The application optimizes the CIES energy utilization strategy by planning energy storage devices in the CIES, improving the CIES power supply reliability. In addition, the introduction of energy storage devices enables the load aggregator to store energy when the electricity price is low and release energy when the electricity price is high, thereby reducing the user's energy cost.

[0144] The technical features of the above embodiments can be combined in any way. To make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered within the scope of the present application.

[0145] The principles and implementation modes of the application are described by applying specific examples. The above descriptions of the embodiments are only used to help understand the method of the application and its core idea; meanwhile, for those skilled in the art, the specific implementation modes and application ranges will be changed according to the idea of the application. In conclusion, the content of the specification should not be understood as a limitation of the application.

Claims

1. A community integrated energy system scheduling method, characterized in that: include: Determine the transaction entities of the community integrated energy system; the transaction entities include energy suppliers and load aggregators; Considering the tiered carbon trading mechanism, with the goal of maximizing operating profits, an optimal operation model for energy suppliers is established; Considering the demand-side response and aiming at minimizing the operating cost, an optimal operation model of the load aggregator is established; Based on the optimal operation model of energy suppliers and the optimal operation model of load aggregators, a Stackelberg game model is established with energy suppliers as leaders and load aggregators as followers. The Stackelberg game model is solved using the alternating direction multiplier method to obtain the optimal scheduling strategy for the community integrated energy system; the optimal scheduling strategy includes the energy price issued by the energy supplier to the load aggregator and the energy purchase by the load aggregator from the energy supplier; The community integrated energy system is dispatched according to the optimal dispatching strategy.

2. The community integrated energy system scheduling method according to claim 1, characterized in that: The tiered carbon trading mechanism includes: Using the yield benchmark method, using formula E G =E G,grid +E G,gtgb 、 and Determine the initial carbon emission quota allocated by the energy supplier; where E G The initial carbon emission quota allocated to energy suppliers, E G,grid E is the carbon emission quota obtained by energy suppliers from purchasing electricity from the upper power grid. G,gtgb Carbon emission quota obtained for the output power of the internal unit of the energy supplier; grid is the carbon emission quota coefficient per unit of electricity generated by the external coal-fired power unit, are the power purchased and sold from the external grid by the energy supplier at time t, Δt is a time interval, and T is the scheduling period; h is the carbon emission quota coefficient per unit thermal power generated by gas turbines and gas boilers, χ e,h is the electrical-to-thermal coefficient, are the electrical power and thermal power output of the gas turbine at time t, is the thermal power output by the gas boiler at time t; Using the formula and Determine the carbon emissions of energy suppliers; where, Carbon emissions from energy suppliers, is the carbon emissions of external coal-fired power units, The carbon emissions of the units within the energy supplier; The carbon emission coefficient per unit of electricity generated by the external coal-fired power unit; is the carbon emission coefficient per unit thermal power generated by the energy supplier’s units, g gt,t 、g gb,t are the volumes of natural gas consumed by the gas turbine and gas boiler at time t respectively; According to the initial carbon emission quota allocated by the energy supplier and the carbon emissions of the energy supplier, the formula Determine the carbon trading cost of energy suppliers; where F G,c is the carbon trading cost of the energy supplier, γ is the carbon trading base price, l is the length of the carbon emission interval, and σ is the carbon trading penalty coefficient.

3. The community integrated energy system scheduling method according to claim 1, characterized in that: The energy supplier's optimal operation model includes: an energy supplier's objective function and energy supplier's constraints; The constraints of energy suppliers include: energy price constraints issued by energy suppliers, energy supplier internal energy supply equipment constraints and energy supplier power balance constraints; The objective function of the energy supplier is: F G,gas,t =C gas (g gt,t +g gb,t )Δt; Where, F G is the revenue of the energy supplier in a dispatch cycle, F G,int,t is the revenue from energy sales to load aggregators at time t, F G,grid,t F is the cost of energy supplier to trade electricity with external power grid at time t; G,gas,t is the gas purchase cost of the energy supplier at time t, F G,c Carbon trading costs for energy suppliers; is the electricity price and heat price sold by the energy supplier to the nth load aggregator at time t, is the electricity and heat power sold by the energy supplier to the nth load aggregator at time t, Δt is a time interval, and N represents the set of load aggregators in the community integrated energy system; is the time-of-use electricity price and on-grid electricity price of the power grid at time t, are the power purchased and sold from the external grid by the energy supplier at time t; C gas is the gas price, g gt,t 、g gb,t are the volumes of natural gas consumed by the gas turbine and gas boiler at time t respectively; The energy supplier publishes the energy price constraint as: Where C h,min 、C h,max is the minimum and maximum heat price in the heat energy market; The energy supplier's internal energy supply equipment constraints are: Where, are the electrical power and thermal power output of the gas turbine at time t, is the thermal power output by the gas boiler at time t, is the power generation efficiency coefficient and heating efficiency coefficient of the gas turbine, is the heating efficiency coefficient of the gas boiler, q gas is the calorific value of natural gas; is the power generation capacity of the gas turbine, is the heating capacity of the gas boiler; The energy supplier power balance constraint is:

4. The community integrated energy system scheduling method according to claim 1, characterized in that: Considering the demand-side response and aiming at minimizing the operating cost, an optimized operation model for load aggregators is established, specifically including: The electric energy load after demand response of load aggregator is: Where, is the electric energy load after the user's demand response at time t, Forecast power for user loads, and They are load transfer power and load reduction power for users of the nth load aggregator at time t, respectively; are the maximum load transfer power and the maximum load reduction power respectively, and T is the time scheduling period; With the goal of minimizing operating costs, the objective function of the load aggregator is established as: Where, F n is the operating cost of the nth load aggregator in one dispatch cycle, F n,int,t is the operating cost of the nth load aggregator at time t, F n,ess,t is the energy storage capacity rental fee of the nth load aggregator at time t, F n,com,t is the user comfort penalty cost of the nth load aggregator at time t, F n,dr,t is the demand response cost of the nth load aggregator at time t; are the electricity and heat purchase prices of the nth load aggregator from the energy supplier at time t, respectively; are the electricity and heat power purchased by the nth load aggregator from the energy supplier at time t; Δt is a time interval; η ess is the energy storage operation and maintenance cost coefficient; η com is the user comfort penalty coefficient; is the charging power and discharging power of the energy storage device from the nth load aggregator at time t; is the user's indoor temperature at time t, τ best The best indoor temperature for users; η tran ,η cut They are the transfer load compensation coefficient and the reduction load compensation coefficient respectively; According to the electric energy load of the load aggregator after demand response, the electric and thermal power balance constraint is established as: Where, The power consumed by the user's electric heater; Predicting the output of photovoltaic panels on community rooftops; The heat energy output by the electric heater for users; η e,h The electric heating efficiency of the electric heater; Indoor heating power for users; Establish energy storage device constraints as: m ch +m dis ≤1; Where S soe,t+1 is the energy state of the energy storage device at time t+1, S soe,t is the energy state of the energy storage device at time t; κ ch , κ dis They are the charging efficiency and discharging efficiency of energy storage equipment respectively; They are the minimum state of charge and maximum state of charge of the energy storage device respectively; is the maximum power transmission power between the energy storage device and the nth load aggregator; μ ch 、μ dis are Boolean variables representing the charging and discharging status of the energy storage device respectively; The load aggregator's objective function, electric and thermal power balance constraints, and energy storage device constraints are combined to form the load aggregator's optimal operation model.

5. The community integrated energy system scheduling method according to claim 1, characterized in that: The Stackelberg game model is: {(EGO∪LA);(F EGO ∪F LA );(I EGO ∪I LA )}; Where EGO represents the energy supplier, LA represents the load aggregator, and F EGO represents the objective function of the energy supplier, F LA represents the objective function of the load aggregator; I EGO represents the game strategy of the energy supplier, I LA Represents the gaming strategy of the load aggregator.

6. The community integrated energy system scheduling method according to claim 1, characterized in that: The Stackelberg game model is solved using the alternating direction multiplier method to obtain the optimal scheduling strategy for the community integrated energy system. Based on the McCormick envelope method, the nonlinear terms in the Stackelberg game model are linearized, and the linearized terms on the energy supplier side are obtained as follows: And the linearization term on the load aggregator side is: Where, is the nonlinear term replacement variable for energy i transaction on the energy supplier side, is the price of energy i that the energy supplier pays to the nth load aggregator at time t, The energy supplier sells power to the energy aggregator n at time t; is the nonlinear term replacement variable for energy i transaction on the load aggregator side, is the price at which the nth load aggregator purchases energy i from the energy supplier at time t, The nth load aggregator purchases the power of energy i from the energy supplier at time t; The relaxed supplementary constraints satisfied by the linearization term on the energy supplier side are established as follows: Where, are the maximum power and minimum power of energy i transmitted between the energy supplier and the nth load aggregator, respectively; are the maximum and minimum values ​​of the price of energy i at time t respectively; The relaxed supplementary constraints satisfied by the linearization term on the load aggregator side are established as follows: Where, are the maximum power and minimum power that the nth load aggregator purchases from the energy supplier for energy i, respectively.

7. The community integrated energy system scheduling method according to claim 1, characterized in that: The Stackelberg game model is solved using the alternating direction multiplier method to obtain the optimal scheduling strategy for the community integrated energy system, which specifically includes: The consistency constraints for energy suppliers and load aggregators to trade energy power and prices are established as follows: in, The energy supplier sells power to the energy aggregator n at time t, The power of energy i purchased by the nth load aggregator from the energy supplier at time t is is a dummy variable for the power of energy i between the nth load aggregator and the energy supplier at time t; is the price of energy i that the energy supplier pays to the nth load aggregator at time t, is the price at which the nth load aggregator purchases energy i from the energy supplier at time t, is a dummy variable for the price of energy i between the nth load aggregator and the energy supplier at time t; According to the Stackelberg game model and the consistency constraint, establishing the energy supplier side subproblem and the load aggregator side subproblem; Initialize global variables, Lagrange multipliers and iteration times; the global variables include and Solve the energy supplier's side subproblem based on the initialized global variables and the initialized Lagrange multiplier to obtain the energy selling price issued by the energy supplier to the load aggregator in this iteration; Solve the load aggregator's subproblem based on the energy price published by the energy supplier to the load aggregator, the initialized global variables, and the initialized Lagrange multiplier to obtain the energy purchased by the load aggregator from the energy supplier in this iteration; the purchased energy is the power of the purchased energy; According to the energy selling price released by the energy supplier to the load aggregator in this iteration and the power of energy purchased by the load aggregator from the energy supplier in this iteration, the formula and Update global variables; where, is the dummy variable for the power of energy i between the nth load aggregator and the energy supplier at time t in the k+1th iteration, is the power sold by the energy supplier to the energy aggregator n at time t in the k+1th iteration, is the power of energy i purchased by the nth load aggregator from the energy supplier at time t in the k+1th iteration, is the dummy variable for the price of energy i between the nth load aggregator and the energy supplier at time t in the k+1th iteration, is the price of energy i that the energy supplier pays to the nth load aggregator at time t in the k+1th iteration, is the price at which the nth load aggregator purchases energy i from the energy supplier at time t in the k+1th iteration; Update the Lagrange multiplier according to the updated global variables; Update the residual according to the updated global variables; Update the dual residual according to the updated global variables; If the updated residual or the updated dual residual does not meet the convergence criteria, increase the number of iterations by 1, replace the initialized global variables with the updated global variables, replace the initialized Lagrange multipliers with the updated Lagrange multipliers, and return to step "Solve the energy supplier's side subproblem based on the initialized global variables and initialized Lagrange multipliers to obtain the energy sales price issued by the energy supplier to the load aggregator in this iteration"; If both the updated residual and the updated dual residual meet the convergence judgment conditions, convergence is determined and the optimal scheduling strategy of the community integrated energy system is output.

8. The community integrated energy system scheduling method according to claim 7, characterized in that: Based on the Stackelberg game model and the consistency constraint, the energy supplier side subproblem and the load aggregator side subproblem are established, specifically including: According to the Stackelberg game model and the consistency constraint, the augmented Lagrangian function is established as: Where L is the augmented Lagrangian function expression, F G,t is the operating cost of the energy supplier at time t, F n,t is the operating cost of the load aggregator at time t, Δt is a time interval, λ is the Lagrange multiplier, The Lagrange multiplier representing the energy supplier selling energy i to the nth load aggregator at time t, It represents the Lagrange multiplier of the energy i power purchased by the nth load aggregator from the energy supplier at time t, represents the Lagrange multiplier of the price at which the energy supplier sells energy i to the nth load aggregator at time t, represents the Lagrange multiplier of the price that the nth load aggregator pays for energy i from the energy supplier at time t; ρ is the penalty factor; The energy supplier sells power to the energy aggregator of the nth load, is a dummy variable for the power of energy i between the nth load aggregator and the energy supplier, The nth load aggregator purchases power of energy i from the energy supplier, is the price of energy i that the energy supplier pays to the nth load aggregator, is a dummy variable for the price of energy i between the nth load aggregator and the energy supplier, is the price at which the nth load aggregator purchases energy i from the energy supplier; T is the scheduling period, N is the set of load aggregators in the community integrated energy system, and I is the set of energy in the community integrated energy system; ||||2 represents the L2 norm; According to the augmented Lagrangian function, the subproblem of determining the energy supplier side is: According to the augmented Lagrangian function, the sub-problem of load aggregator is determined as:

9. The community integrated energy system scheduling method according to claim 7, characterized in that: The update formula of Lagrange multiplier is: Where, are the Lagrange multipliers of the energy supplier selling energy i power to the nth load aggregator at time t in the kth and k+1th iterations, respectively, and ρ is the penalty factor; are the Lagrange multipliers of the nth load aggregator purchasing energy i power from the energy supplier at time t in the kth and k+1th iterations, respectively. are the Lagrange multipliers of the price of energy i sold by the energy supplier to the nth load aggregator at time t during the kth and k+1th iterations, respectively. are the Lagrange multipliers of the price at which the nth load aggregator purchases energy i from the energy supplier at time t during the kth and k+1th iterations, respectively; The update formula for the residual is: Where, is the residual of the energy supplier selling energy i power to the load aggregator in the k+1th iteration process, is the residual of the power of energy i purchased by the load aggregator from the energy supplier in the k+1th iteration process, is the residual of the price of energy i sold by the energy supplier to the load aggregator in the k+1th iteration, is the residual of the price of energy i purchased by the load aggregator from the energy supplier in the k+1th iteration; ||||2 represents the L2 norm; The update formula of the dual residual is: Where, is the dual residual of the transaction energy power in the k+1th iteration, is the dual residual of the transaction energy price in the k+1th iteration; is the dummy variable for the power of energy i between the nth load aggregator and the energy supplier in the kth iteration, is a dummy variable for the price of energy i between the nth load aggregator and the energy supplier in the kth iteration.

10. The community integrated energy system scheduling method according to claim 7, characterized in that: The convergence judgment condition is: ||r k+1 || 2 ≤r p ,||s k+1 || 2 ≤r c ； Where, ||r k+1 || 2 is the sum of the power residuals of the k+1th iteration, ||s k+1 || 2 is the sum of the price residuals at the k+1th iteration; r p is the power residual convergence margin, r c is the price residual convergence margin.

Images