Joint planning method based on shared energy storage supplier and local integrated energy system

Abstract

The invention discloses a joint planning method based on shared energy storage suppliers and a local integrated energy system, and relates to the technical field of energy optimization. The invention aims to solve the problems of cost distribution and efficiency in the planning process of a regional integrated energy system and a shared energy storage supplier. The method comprises the steps of establishing a joint planning framework of a two-stage multi-cooperative game, improving a Nash bargaining method by using a boundary benefit contribution rate to quantify a bargaining level, and proposing an improved alternating direction multiplier A-M-ADMMD based on a double-update acceleration iteration strategy to improve solving efficiency. A post-event shared energy storage cost distribution model is constructed to realize reasonable cost allocation; according to the invention, by optimizing energy storage planning and service pricing, the overall operation cost is reduced, and the overall planning benefit maximization of the regional integrated energy system and the shared energy storage supplier is realized; the method effectively improves the economical efficiency and reliability of the energy system, and is suitable for various comprehensive energy system scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and optimization, and in particular to a joint planning method based on a shared energy storage supplier and a local integrated energy system. Background Art

[0002] With the rapid development of energy technology and the continuous growth of energy demand, the optimization and integration of energy systems has become a hot topic in current research. In energy systems, energy storage technology is an important means to balance supply and demand and improve energy efficiency, and its importance is becoming increasingly prominent. However, traditional energy storage methods often have problems such as high cost, low utilization rate, and complex management, which makes it difficult to meet the modern energy system's requirements for efficiency, flexibility, and economy.

[0003] In recent years, shared energy storage, as an innovative energy storage model, has achieved optimal configuration and efficient utilization of energy storage resources by centrally managing and coordinating the energy storage equipment of multiple users. Shared energy storage not only reduces the energy storage cost of a single user, but also improves the overall utilization rate of energy storage equipment, providing a strong guarantee for the stable operation of the energy system. However, how to effectively plan and manage the cooperation between shared energy storage suppliers and local integrated energy systems to maximize overall benefits is still a problem that needs to be solved urgently.

[0004] In the prior art, there have been some studies on the joint planning of shared energy storage and regional integrated energy systems. For example, some patents or literature have proposed a joint planning model based on a single objective function, which reduces energy storage costs and improves energy efficiency by optimizing the configuration and scheduling strategy of energy storage equipment. However, these models often ignore the interest game and cost allocation issues between different participants, making it difficult for planning results to be implemented in practical applications.

[0005] In addition, some studies have attempted to solve the problem of interest distribution between shared energy storage suppliers and regional integrated energy systems through cooperative game theory. However, most of these studies use the traditional Nash bargaining method, which fails to fully consider the differences in bargaining power and contribution between participants, resulting in a lack of fairness and rationality in the bargaining results.

[0006] In summary, the existing technologies still have many deficiencies in the joint planning of shared energy storage suppliers and local integrated energy systems, especially in the lack of effective solutions in terms of interest game and cost allocation. Therefore, there is an urgent need for a joint planning method that can comprehensively consider the interests of all parties and maximize the overall benefits, so as to promote the widespread application and in-depth development of shared energy storage technology in energy systems.

[0007] It is in this context that the present invention proposes a joint planning method based on shared energy storage suppliers and local integrated energy systems. The method aims to achieve effective cooperation and reasonable cost allocation between shared energy storage suppliers and regional integrated energy systems through an improved Nash bargaining method and a distributed solution algorithm, thereby providing strong support for the optimized operation of the energy system. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a joint planning method based on shared energy storage suppliers and local integrated energy systems, so as to solve the core problems existing in the joint planning process between shared energy storage suppliers and local integrated energy systems in the prior art, such as complex interest game, unreasonable cost allocation and difficulty in maximizing the overall benefits of the system.

[0009] In order to solve the above technical problems, the technical solution adopted by the present invention is a joint planning method based on a shared energy storage supplier and a local integrated energy system, comprising the following steps: Step 1: Establish a joint planning framework for regional integrated energy systems and shared energy storage suppliers based on a two-stage multi-cooperative game; Step 2: Solve the ex-post allocation problem of shared energy storage investment and operating costs within the planning cycle of the regional integrated energy system; In a preferred solution, the specific steps of step 1 include: Step 1.1: Establish a joint planning model for the regional integrated energy system and shared energy storage providers in the first phase of the joint planning framework; Step 1.2: Use the marginal benefit contribution rate to quantify the bargaining level of the regional integrated energy system and shared energy storage suppliers to improve the original Nash bargaining method; Step 1.3: Establish planning and operation constraints of the joint planning model; Step 1.4: Improve the alternating direction multiplier method AM-ADMMD based on the double update accelerated iteration strategy.

[0010] In the preferred solution, the specific steps of establishing the joint planning model in step 1.1 include: Step 1.1.1: Shared energy storage suppliers need to customize reasonable energy storage planning and scheduling strategies based on the shared energy storage needs of the regional integrated energy system; Step 1.1.2: The shared energy storage supplier is the price setter for shared energy storage, including the energy unit price and power capacity unit price of shared energy storage; Step 1.1.3: The shared energy storage charging and discharging power is provided by the distribution network, and the shared energy storage supplier needs to pay a certain fee; Step 1.1.4: The regional integrated energy system of multiple integrated energy systems rents shared energy storage from the shared energy storage provider; Step 1.1.5: Select a representative from the electric vehicle cluster to summarize the daily discharge strategies of each electric vehicle and notify other electric vehicles; Step 1.1.6: Each integrated energy system customizes its own energy conversion equipment planning strategy and independently pays for the investment and operating costs of the energy conversion equipment.

[0011] In the preferred solution, the specific steps of step 1.2 to improve the Nash bargaining method include: Step1.2.1: Establish the original joint planning objective function; The original Nash bargaining method is shown below, and its goal is to maximize the overall benefits of the cooperative group; (1) In the formula, is the number of participants in the cooperative game; and Respectively The benefits of each participant before and after participating in the cooperative game; Step1.2.2: Improve the original Nash bargaining method; The improved Nash bargaining method reflects the differences in bargaining power of participants by introducing bargaining levels. Based on the formula of the original Nash bargaining method, the joint planning objective function proposed by the improved Nash bargaining method maximizes the overall benefits of the regional integrated energy system and shared energy storage suppliers, as shown in the following formula: (2) In the formula, and To share the benefits of energy storage suppliers before and after participation, and are the bargaining levels of shared energy storage suppliers and regional integrated energy systems, and They are the total costs of the integrated energy system before and after the formation of the integrated energy system alliance; Shared storage provider revenue during planning period It is related to the revenue from shared energy storage services, energy storage investment costs, energy storage maintenance costs, operating costs and the residual use value of energy storage equipment, which is specifically expressed by the following formula: (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) in, For the Annual shared energy storage service revenue; and They are the investment cost and maintenance cost of energy storage; represents the electricity transaction cost; The residual use value of the energy storage equipment; , and They are the number of planning years, the number of typical planning scenarios, and the scheduling time; For the scenario Probability of occurrence; is the discount factor; and are the unit rental prices of energy storage capacity and power capacity of shared energy storage services respectively; and They are shared energy storage energy and power capacity leased by the regional integrated energy system from shared energy storage suppliers; among them, and are the unit investment prices of the energy capacity and power capacity of the physical energy storage equipment respectively; among them, and The energy and power capacity of the energy storage devices invested by the shared energy storage providers, respectively; Mainly the unit maintenance price of physical energy storage equipment; among them, and Indicates in the scene middle In 2018, the number of physical energy storage devices of shared energy storage providers was The charging and discharging power at each moment; is the time interval between two adjacent scheduling times; and are the unit prices of electricity sold and electricity repurchased by the power company; and It is the power that shared energy storage providers buy and sell to electric utilities; , are the residual value and service life of the energy storage device, respectively; Assumptions The integrated energy system and shared energy storage are used together, then the objective function of each subsystem is The aim is to minimize the sum of the costs of each subsystem planning cycle of the regional integrated energy system, as shown in the following formula: (3) In the formula, the total cost of sharing energy storage for regional integrated energy systems; and Respectively The planned cost of energy conversion equipment and electricity purchase costs for a comprehensive energy system; For the The residual use value of energy conversion equipment invested in a comprehensive energy system; and It can be quantified using the marginal contribution rate of the regional integrated energy system and shared energy storage supplier, that is, the rate of change in income before and after the participant participates, expressed by the following formula: (4) (5).

[0012] In the preferred solution, the planning and operation constraints of the joint planning model established in step 1.3 include: Step 1.3.1: Planning and operating constraints of shared energy storage providers, including: Energy and power capacity constraints for shared energy storage services: (25) (26) Constraints on shared energy storage charging and discharging demand in regional integrated energy systems: (27) Capacity and power planning constraints of energy storage devices: (28) (29) Operation constraints of energy storage devices: (30) (31) (32) (33) (34) In the formula, and are the maximum and minimum values ​​of the shared energy storage capacity customized by the shared energy storage supplier, and are the maximum and minimum values ​​of the shared energy storage power capacity customized by the shared energy storage supplier, is the number of integrated energy systems, and Respectively represent The charging and discharging power of the shared energy storage of the integrated energy system, and They represent the maximum values ​​of energy capacity and power capacity invested by the shared energy storage supplier respectively; and Respectively indicate time The state of charge (SOC) of the shared energy storage at the initial time; and They represent the ratio of the maximum and minimum SOC of the shared energy storage device respectively; , and They are the self-loss coefficient, charge loss coefficient and discharge loss coefficient of shared energy storage; Step 1.3.2: Planning and operation constraints of regional integrated energy systems, including: Energy balance constraints: (35) (36) (37) Energy conversion constraints: (38) (39) (40) In the formula, and Respectively The heat load of a comprehensive energy system; and Respectively The electricity provided by the combined heat and power generation unit and renewable energy generation in the integrated energy system, and They are The charging and discharging power of the self-built energy storage device of the integrated energy system; For the The electrical power consumed by a comprehensive energy system, , and They are Thermal power provided by a comprehensive energy system of cogeneration units, electric heaters and gas boilers; and Respectively represent Natural gas consumed by combined heat and power units and gas boilers in the integrated energy system; , , and They are the electricity-to-heat efficiency, gas-to-heat efficiency of the cogeneration unit, the electricity-to-heat efficiency of the electric heater and the gas-to-heat coefficient of the gas boiler.

[0013] In a preferred solution, the specific steps of step 1.4 include: Step 1.4.1: Use the reductio ad absurdum method to process the joint planning objective function; The objective function of the joint planning after the reductio ad absurdum method is as follows: (6) In the formula, and They are the revenues of shared energy storage providers and regional integrated energy systems; Step 1.4.2: Use the logarithmic method to process the objective function; The objective function after logarithmic processing is shown as follows: (7) Step 1.4.3: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the reductio ad absurdum method; (8) (9) In the formula, and are the Lagrange multipliers of the energy and power capacities of leased shared energy storage, respectively; and are the Lagrange multipliers of the shared energy storage charging and discharging power respectively; and are the penalty coefficients for leasing the energy and power capacity of shared energy storage, respectively; and are the penalty coefficients for the shared energy storage charging and discharging power respectively; and They are the shared energy storage energy and power capacity that the shared energy storage provider hopes to lease to the regional integrated energy system; and are the shared energy storage energy and power capacity that the regional integrated energy system expects to lease, respectively; and They are the shared energy storage charging and discharging power that suppliers expect the regional integrated energy system to use; and They are the shared energy storage charging and discharging power that the regional integrated energy system expects to use; Step 1.4.4: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the logarithmic method; (10) (11) In the formula, and are the Lagrange multipliers of the rental unit price of shared storage energy and power capacity, respectively; and are the Lagrange multipliers of the rental unit price of the shared energy storage charging and discharging power, respectively; and are the unit prices for shared storage energy and power capacity that suppliers expect to be paid by the regional integrated energy system, respectively; and are the unit prices of shared energy storage energy and power capacity that the regional integrated energy system expects to pay, respectively.

[0014] In a preferred solution, the specific steps of step 2 include: Step 2.1: Propose a shared energy storage cost allocation model for regional integrated energy systems based on an improved cooperative game theory framework; Step 2.2: Improve the cooperative game method based on constraint generation technology.

[0015] In a preferred solution, the specific steps of step 2.1 include: Step 2.1.1: Establish a model of the deviation motivation and dissatisfaction degree of subsystems in the regional integrated energy system towards the large system; Set As a A collection of regions composed of integrated energy systems, defined Reason Any sub-alliance composed of regional integrated energy systems, The number of The cost allocation strategy is composed of vector Indicates that the The cost of an integrated energy system; Refers to all that satisfy overall and individual rationality , as shown below: (12) nuclear The set of refers to the attribution that satisfies the rationality of the alliance, that is, the sub-alliance has no motivation to deviate from the regional integrated energy system; (13) For cooperative games with the goal of minimizing costs, the sub-coalition The excess Reflects the sub-alliance Cost allocation strategy The dissatisfaction is shown in the following formula: (14) Step 2.1.2: Establish a shared energy storage cost allocation model for regional integrated energy systems; The cost allocation model can be expressed as: (15) In the formula, is an auxiliary variable.

[0016] In a preferred solution, the specific steps of step 2.2 include: Step 2.2.1: Find the dissatisfied sub-alliance in the sub-alliance set; (16) In the formula, For the The binding constraints set at the iteration hour, , in getting and Then, by solving the following formula, we can Find the largest dissatisfied sub-alliance among the sub-alliance sets other than (17) In the formula, is the most unsatisfactory optimal allocation strategy, and we get Then you can generate constraints; Step 2.2.2: Find all satisfied sub-alliances under the optimal allocation strategy; Repeat Step 2.2.1 to get a new , and ,until Less than 0, we can further obtain the optimal allocation strategy that satisfies the remaining sub-alliances. , all satisfied sub-coalitions are represented by: (18); Step 2.2.3: Obtain constraints; According to Step 2.2.2, determine the binding constraints Is the total greater than , if greater than , the solution is uniquely determined, and the optimal cost allocation strategy can be obtained ,otherwise , Repeat the above step until the total number of bound constraints is greater than or equal to .

[0017] The joint planning method based on shared energy storage suppliers and local integrated energy systems provided by the present invention has the following beneficial effects: 1. The present invention solves the core problems in the prior art, such as the complex game of interests, unreasonable cost allocation, and difficulty in maximizing the overall benefits of the system, which exist in the joint planning process between shared energy storage suppliers and local integrated energy systems; 2. The improved Nash bargaining method of the present invention: by introducing bargaining levels to reflect the differences in bargaining power of participants, the original Nash bargaining method is improved to make cost allocation more fair and reasonable; 3. The double-update accelerated iteration strategy of the present invention adopts the double-update accelerated iteration strategy when solving the joint planning model, which improves the convergence speed and solution efficiency of the algorithm; 4. The cost allocation model of the present invention is innovative. It establishes a cost allocation model based on an improved cooperative game method. By quantifying the deviation motivation and dissatisfaction degree of the subsystem, the rationality and fairness of cost allocation are ensured. 5. The present invention takes into account a variety of factors, including shared energy storage service revenue, energy storage investment cost, energy storage maintenance cost, operating cost and residual use value of energy storage equipment in the planning model, making the planning results more comprehensive and accurate; 6. The present invention maximizes the overall planning benefits of shared energy storage suppliers and regional integrated energy systems by constructing a joint planning model; 7. The present invention solves the problem of unfair cost allocation through an improved Nash bargaining method and cost allocation model, ensuring that the interests of all participants are reasonably protected; 8. The present invention improves the efficiency of solving the joint planning model and reduces the calculation time and cost by optimizing the algorithm; 9. The present invention reduces the overall operating cost and improves economic benefits through joint planning and optimized scheduling; 10. The present invention realizes the efficient utilization and complementarity of energy resources and improves the efficiency of energy utilization through the centralized management and coordination of shared energy storage; 11. The present invention provides a scientific basis and effective support for the planning and decision-making of shared energy storage suppliers and regional integrated energy systems; 12. The present invention has achieved technical innovation in cost allocation model and algorithm optimization, and provided new ideas and methods for research and application in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention will be further described below in conjunction with the accompanying drawings and implementation examples: Figure 1The joint planning framework of the regional integrated energy system and shared energy storage supplier based on two-stage multi-cooperative game of the present invention; Figure 2 The AM-ADMMD solution process of the present invention; Figure 3 is a flow chart of A-CGT of the present invention; Figure 4 A shared energy storage scheduling strategy for different integrated energy systems in implementation case 2 of the present invention; Figure 5 The planning strategy for different equipment of the regional integrated energy system in the implementation case 3 of the present invention; Figure 6 This is the shared energy storage supplier scheduling strategy for the last year of the planning period in implementation case 4 of the present invention. DETAILED DESCRIPTION

[0019] The technical solution of the present invention is further described below in conjunction with the embodiments and drawings: Example 1 like Figure 1 As shown, the joint planning method based on shared energy storage suppliers and local integrated energy systems includes the following steps: Step 1: Establish a joint planning framework for regional integrated energy systems and shared energy storage suppliers based on a two-stage multi-cooperative game; Step 2: Solve the ex-post allocation problem of shared energy storage investment and operating costs within the planning cycle of the regional integrated energy system; In this embodiment, the specific steps of step 1 include: Step 1.1: Establish a joint planning model for the regional integrated energy system and shared energy storage providers in the first phase of the joint planning framework; Step 1.2: Use the marginal benefit contribution rate to quantify the bargaining level of the regional integrated energy system and shared energy storage suppliers to improve the original Nash bargaining method; Step 1.3: Establish planning and operation constraints of the joint planning model; Step 1.4: Improve the alternating direction multiplier method AM-ADMMD based on the double update accelerated iteration strategy.

[0020] Furthermore, the specific steps of establishing the joint planning model in step 1.1 include: Step 1.1.1: Shared energy storage suppliers need to customize reasonable energy storage planning and scheduling strategies based on the shared energy storage needs of the regional integrated energy system; Step 1.1.2: The shared energy storage supplier is the price setter for shared energy storage, including the energy unit price and power capacity unit price of shared energy storage; Step 1.1.3: The shared energy storage charging and discharging power is provided by the distribution network, and the shared energy storage supplier needs to pay a certain fee; Step 1.1.4: The regional integrated energy system of multiple integrated energy systems rents shared energy storage from the shared energy storage provider; Step 1.1.5: Select a representative from the electric vehicle cluster to summarize the daily discharge strategies of each electric vehicle and notify other electric vehicles; Step 1.1.6: Each integrated energy system customizes its own energy conversion equipment planning strategy and independently pays for the investment and operating costs of the energy conversion equipment.

[0021] Furthermore, the specific steps of improving the Nash bargaining method in step 1.2 include: Step1.2.1: Establish the original joint planning objective function; The original Nash bargaining method is shown below, and its goal is to maximize the overall benefits of the cooperative group; (1) Step1.2.2: Improve the original Nash bargaining method; It can be seen from the above formula that the cost saving index of each participant is 1, which means that the original Nash bargaining method allocates equal profits to each participant; in fact, each participant's contribution to the grand alliance is usually different, which makes it difficult for the original Nash bargaining method to ensure the fairness of the bargaining results; the improved Nash bargaining method reflects the differences in the bargaining power of the participants by introducing the bargaining level. Based on the formula of the original Nash bargaining method, the joint planning objective function proposed by the improved Nash bargaining method maximizes the overall benefits of the regional integrated energy system and shared energy storage suppliers, as shown in the following formula: (2) Shared storage provider revenue during planning period Related to shared energy storage service revenue, energy storage investment costs, energy storage maintenance costs, operating costs and the residual use value of energy storage equipment; Assumptions The integrated energy system and shared energy storage are used together, then the objective function of each subsystem is The aim is to minimize the sum of the costs of each subsystem planning cycle of the regional integrated energy system, as shown in the following formula: (3) and It can be quantified using the marginal contribution rate of the regional integrated energy system and shared energy storage supplier, that is, the rate of change in income before and after the participant participates, expressed by the following formula: (4) (5) Furthermore, the planning and operation constraints of the joint planning model established in step 1.3 include: Step 1.3.1: Planning and operation constraints of shared energy storage providers, including: constraints on the energy and power capacity of shared energy storage services, constraints on the charging and discharging demand of shared energy storage in regional integrated energy systems, constraints on the capacity and power planning of energy storage devices, and operation constraints of energy storage devices; Step 1.3.2: Planning and operation constraints of regional integrated energy systems, including: energy balance constraints and energy conversion constraints.

[0022] Furthermore, the specific steps of step 1.4 include: Step 1.4.1: Use the reductio ad absurdum method to process the joint planning objective function; The objective function of the joint planning after the reductio ad absurdum method is as follows: (6) In the formula, and They are the revenues of shared energy storage providers and regional integrated energy systems; Step 1.4.2: Use the logarithmic method to process the objective function; The objective function after logarithmic processing is shown as follows: (7) Step 1.4.3: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the reductio ad absurdum method; (8) (9) Step 1.4.4: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the logarithmic method; (10) (11) Furthermore, the specific steps of step 2 include: Step 2.1: Propose a shared energy storage cost allocation model for regional integrated energy systems based on an improved cooperative game theory framework; Step 2.2: Improve the cooperative game method based on constraint generation technology.

[0023] Furthermore, the specific steps of step 2.1 include: Step 2.1.1: Establish a model of the deviation motivation and dissatisfaction degree of subsystems in the regional integrated energy system towards the large system; gather Refers to all that satisfy overall and individual rationality ,nuclear The set of refers to the attribution that satisfies the rationality of the alliance. For the cooperative game with the goal of minimizing costs, the sub-alliance The excess Reflects the sub-alliance Cost allocation strategy The dissatisfaction is shown in the following formula: (12) (13) (14) Step 2.1.2: Establish a shared energy storage cost allocation model for regional integrated energy systems; The cost allocation model can be expressed as: (15) Furthermore, the specific steps of step 2.2 include: Step 2.2.1: Find the dissatisfied sub-alliance in the sub-alliance set; (16) (17) Step 2.2.2: Find all satisfied sub-alliances under the optimal allocation strategy; Repeat Step 2.2.1 to get a new , and ,until Less than 0, we can further obtain the optimal allocation strategy that satisfies the remaining sub-alliances. , all satisfied sub-coalitions are represented by: (18); Step 2.2.3: Obtain constraints; According to Step 2.2.2, determine the binding constraints Is the total greater than , if greater than , the solution is uniquely determined, and the optimal cost allocation strategy can be obtained ,otherwise , Repeat the above step until the total number of bound constraints is greater than or equal to .

[0024] Example 2 In another preferred embodiment, based on the above embodiment 1, in this embodiment, a shared energy storage supplier and an alliance consisting of six integrated energy systems are used to verify the effectiveness and advantages of the method proposed in the present invention. Some technical parameters are shown in Table 1: .

[0025] A joint planning approach based on shared energy storage providers and local integrated energy systems, including: Step 1: Establish a joint planning model for the regional integrated energy system and shared energy storage providers in the first phase of the joint planning framework; Step 2: Improve the original Nash bargaining method using the marginal revenue contribution rate; Step 3: Establish planning and operation constraints of the joint planning model; Step 4: Improve the alternating direction multiplier method (AM-ADMMD) based on the double-update accelerated iteration strategy; Step 5: Establish a post-shared energy storage cost allocation model for regional integrated energy systems under the framework of an improved cooperative game method; Step 6: Improve the cooperative game method based on constraint generation technology; The data used in the present invention comes from the operating data of a regional integrated energy system in Jiangsu. The specific implementation methods of the present invention are as follows: Step 1 The specific steps are as follows: The equipment planning strategy of the regional integrated energy system is analyzed. Since the economic performance of electric heaters in planning and operation is better than that of gas boilers, investment is made in gas boilers. Electric heaters and cogeneration units are built every year instead of being completed in the first year of planning. The regional integrated energy system uses shared energy storage services every year instead of building its own energy storage. Different integrated energy systems have the characteristics of complementary energy demand. Therefore, a joint planning model for shared energy storage services of a regional integrated energy system is established, which can take advantage of the characteristics of complementary energy demand and reduce the cost of planning and operation.

[0026] Analyzing the energy storage planning and scheduling strategy, Table 2 shows the shared energy storage demand of the regional integrated energy system and the optimal strategy for suppliers to invest in physical energy storage equipment:

[0027] As can be seen from Table 2, there is no need for suppliers to invest in physical energy storage equipment, because the shared energy storage peak demand of the regional integrated energy system is only a small part of the entire leasing period. Therefore, suppliers can meet electricity demand through power interaction with the power supply company, thereby reducing the investment cost of physical energy storage and increasing the supplier's income.

[0028] Step 2 The specific steps are as follows: JPM-INB (Joint Planning Model Under the Improved Nash Bargaining Framework) is compared with JPM-IS (the Planning Model Considering That LIESs Form a Cooperative Coalition to Collectively Invest in and Share Physical Energy Storage Facilities), PM-INB (the Planning Model Considering that LIES Opts for Lease SES Service From SESP Independently), and PM-IS (the Planning Model Considering that LIES Opts for The Individual Establishment of Physical Energy Storage). The results are shown in Table 3.

[0029] As shown in Table 3, PM-IS and JPM-IS did not consider renting shared energy storage services from suppliers, but chose to invest in physical energy storage facilities independently or collectively. Multiple integrated energy systems in JPM-IS jointly invest and share physical energy storage, which can build a larger energy storage capacity at a lower price and make full use of the energy demand complementarity of multiple integrated energy systems. Therefore, the total cost in JPM-IS is 9.3% lower than that in PM-IS. The PM-INB integrated energy system rents shared energy storage services from suppliers alone, and the energy storage cost is lower than that of PM-IS when it independently builds physical energy storage. The total cost of PM-INB's integrated energy system is 3.5% lower than that of PM-IS, but 6.5% higher than that of PM-IS. JPM-INB combines the advantages of JPM-IS and PM-INB to obtain shared energy storage services from suppliers at a lower price. Therefore, compared with JPM-IS, PM-INB and PM-IS, the total cost of JPM-INB's regional integrated energy system is reduced by 2.3%, 8.1% and 11.9%, respectively.

[0030] Step 3 The specific steps are as follows: According to the technical parameters of the shared energy storage suppliers and the six integrated energy systems, the planning and operation constraints of the shared energy storage suppliers and the planning and operation constraints of the regional integrated energy systems are established respectively, including the energy and power capacity constraints of the shared energy storage services, the shared energy storage charging and discharging demand constraints, the capacity and power constraints of the energy storage devices, and the operating condition constraints of the energy storage devices; energy balance constraints and energy conversion constraints.

[0031] Step 4 The specific steps are as follows: The AM-ADMMD algorithm is used to solve the model of the first stage and compared with other solving algorithms, including EA (Enumeration Algorithm), AIA (Artificial Intelligence Algorithms) and A-ADMM (Improved Alternating Direction Multiplier Method) solving algorithms to verify the advantages of the AM-ADMMD algorithm. The comparison results are shown in Table 4:

[0032] As shown in Table 4, AIA The calculation time is the worst among the three solution algorithms. This is because AIA is prone to local optimality and cannot be parallelized. Theoretically, EA can find the optimal solution, but the contradiction between the enumeration interval and the solution efficiency is inevitable. Since the calculation time increases with the decrease of the enumeration interval, the calculation time of EA is 68.9 minutes longer than that of AIA. , The calculation time and computation time of the AM-ADMMD proposed in the present invention are better than those of EA and AIA because they transform the improved Nash bargaining framework into two convex optimization sub-models through mathematical methods, so that the optimal solution can be found at a faster calculation speed through commercial solvers; in addition, the AM-ADMMD proposed in the present invention introduces a double-update accelerated iteration strategy, and the calculation time is shortened by 41.9% compared with that of AM-TADMM; in summary, the AM-ADMMD proposed in the present invention has the best calculation and optimization performance among the four methods.

[0033] Step 5 The specific steps are as follows: Firstly, the deviation motivation of sub-alliances in the regional integrated energy system is modeled according to whether the integrated energy system meets the overall and individual rationality. Then, the degree of dissatisfaction of sub-alliances with the cost allocation strategy is modeled under the cooperative game goal of cost minimization. Finally, a shared energy storage cost allocation model for the regional integrated energy system is established.

[0034] Step 6 The specific steps are as follows: The cooperative game method improved by constraint generation technology is different from the original cooperative game method. ( ) are compared with the solution iteration times and calculation time under the same condition, and the results are shown in Table 5:

[0035] It can be seen from Table 5 that when N is small, the difference in the number of iterations and calculation time between the improved algorithms is not large; however, when As the number continues to increase, the difference will become more and more obvious. This is because the improved cooperative game of the present invention uses constraint generation technology to reduce the model calculation time complexity from exponential order to linear order. Therefore, when the number of integrated energy systems is large, the reduction in calculation time is more obvious. This shows that under the premise of ensuring the same solution results, the improved cooperative game can significantly reduce the calculation time, which is more suitable for the actual situation with a large number of integrated energy systems.

[0036] In summary, a joint planning method based on shared energy storage suppliers and local integrated energy systems can maximize the overall planning benefits of shared energy storage suppliers and regional integrated energy systems.

[0037] Example 3 In another preferred embodiment, based on the above embodiments 1 and 2, in this embodiment, a shared energy storage supplier and 8 integrated energy systems are included, on which the economy and practicality of the planning method proposed in the present invention are verified. The technical parameters of each part are shown in Table 6: .

[0038] A joint planning approach based on shared energy storage providers and local integrated energy systems, including: Step 1: Establish a joint planning model of the first phase of regional integrated energy system and suppliers; Step 2: Propose a Nash bargaining method based on marginal yield; Step 3: Determine the constraints for joint planning of suppliers and regional integrated energy systems; Step 4: Improve the ADMM algorithm using the double update iteration strategy; Step 5: Establish an ex post shared energy storage cost allocation model based on improved cooperative game; Step 6: Propose a cooperative game method based on constraint generation technology The data used in the present invention comes from the operating data of a regional integrated energy system in Shandong. The specific implementation methods of the present invention are as follows: Step 1 The specific steps are as follows: Equipment planning strategies for regional integrated energy systems Figure 2 As shown, the integrated energy system includes cogeneration units and electric heaters. Both types of equipment have been under construction within the four-year plan. No physical energy storage is built inside the park. With the participation of shared energy storage suppliers, each integrated energy system achieves energy demand complementarity to reduce overall operating costs.

[0039] The optimal strategy for shared energy storage demand and supplier investment in physical energy storage equipment for the park's integrated energy system is shown in Table 7:

[0040] It can be seen from Table 7 that in order to reduce energy storage costs, suppliers do not need to build physical energy storage equipment. During peak electricity consumption periods, suppliers purchase electricity directly from power companies to meet electricity demand. However, this situation accounts for a very small proportion of the entire shared energy storage leasing cycle.

[0041] Step 2 The specific steps are as follows: The improved Nash bargaining framework is used to obtain the joint planning model. Under the same conditions, it is compared with the planning model that considers grid operators forming a cooperative alliance to collectively invest and share physical energy storage facilities (JPM-IS), the planning model that considers grid operators choosing to independently lease SES services from SESP (PM-INB), and the planning model that considers grid operators choosing to build physical energy storage facilities alone (PM-IS). The results show that compared with the above three models, the total cost of the regional integrated energy system under the JPM-INB model is reduced by 2.6%, 8.5% and 12.7%, respectively.

[0042] Step 3 The specific steps are as follows: Establish joint planning and operation constraints based on the technical parameters of shared energy storage suppliers and integrated energy systems. For shared energy storage suppliers, set energy and power capacity constraints for shared energy storage services, shared energy storage charging and discharging demand constraints, energy storage device capacity and power constraints, and energy storage device operating condition constraints. For regional integrated energy systems, set energy balance constraints and energy conversion constraints.

[0043] Step 4 The specific steps are as follows: The AM-ADMMD algorithm is used to solve the model of the first stage and compared with other solving algorithms, including EA, AIA and A-TADMM solving algorithms, to verify the advantages of the AM-ADMMD algorithm. Through comparison, it can be seen that the AM-ADMMD algorithm has the fastest calculation speed compared with other algorithms, and with the increase of integrated energy systems, the calculation advantage is more obvious.

[0044] Step 5 The specific steps are as follows: In order to reasonably allocate costs between different integrated energy systems and between integrated energy systems and suppliers, a cost allocation model should be established during joint planning. First, based on the rationality evaluation of the integrated energy system as a whole and individually, a deviation motivation model of sub-alliances in the regional integrated energy system is established; then, within the framework of cooperative game with cost minimization as the goal, the degree of dissatisfaction of sub-alliances in cost allocation strategies is modeled; finally, based on these models, a cost allocation model for shared energy storage in a regional integrated energy system is established.

[0045] Step 6 The specific steps are as follows: The cooperative game method improved by constraint generation technology is different from the original cooperative game method. ( ) are compared with the solution iteration times and calculation time under the same conditions. The results are shown in Table 8:

[0046] It can be seen from Table 8 that since the improved method reduces the computational time complexity from exponential to linear, as the number of integrated energy systems increases, the advantages of the improved method in terms of iteration times and computational time become more and more obvious, indicating that the improved cooperative game can improve the efficiency of model solving.

[0047] In summary, a joint planning method based on shared energy storage suppliers and local integrated energy systems has significant advantages in terms of model solving efficiency and cost allocation process.

[0048] Example 4 In another preferred embodiment, based on the above embodiments 1, 2, and 3, in this embodiment, a shared energy storage supplier and an alliance consisting of twelve integrated energy systems are used to verify the effectiveness and advantages of the method proposed in the present invention. Some technical parameters are shown in Table 9: .

[0049] A joint planning approach based on shared energy storage providers and local integrated energy systems, including: Step 1: Establish a joint planning model for the regional integrated energy system and shared energy storage providers to minimize the total operating cost; Step 2: Improve the original Nash bargaining method by obtaining the marginal revenue contribution rate; Step 3: Determine the constraints for model operation; Step 4: Improve the alternating direction multiplier method (AM-ADMMD) based on the double-update accelerated iteration strategy; Step 5: Establish a post-event shared energy storage allocation model based on the improved cooperative game method framework; Step 6: Improve the cooperative game method based on constraint generation technology; The data used in the present invention comes from the operating data of a regional integrated energy system in Liaoning. The specific implementation methods of the present invention are as follows: Step 1 The specific steps are as follows: In order to establish a joint planning model, it is necessary to establish the operation planning strategy of the regional integrated energy system and suppliers. The integrated energy system includes gas boilers and cogeneration units. In order to reduce the operating cost, the integrated energy system and suppliers do not build their own physical energy storage. Different integrated energy systems use the characteristics of resource complementarity to reduce planning and operating costs. Table 10 shows the shared energy storage demand of the regional integrated energy system and the optimal strategy for suppliers to invest in physical energy storage equipment: .

[0050] Step 2 The specific steps are as follows: JPM-INB was compared with JPM-IS, PM-INB and PM-IS. The results show that the total cost of the regional integrated energy system of JPM-INB was reduced by 2.8%, 8.9% and 12.6% respectively.

[0051] Step 3 The specific steps are as follows: Based on the technical parameters of suppliers and integrated energy systems, for shared energy storage suppliers, energy and power capacity constraints for shared energy storage services, shared energy storage charging and discharging demand constraints, energy storage device capacity and power constraints, and energy storage device operating condition constraints are established; for regional integrated energy systems, energy balance constraints and energy conversion constraints are established.

[0052] Step 4 The specific steps are as follows: The AM-ADMMD algorithm is used to solve the joint planning model of the first stage and compared with other solving algorithms. Through comparison, the improved algorithm can significantly reduce the solution time while ensuring that the cost is relatively fixed. The calculation speed is reduced by 95.23%, 90.36%, and 41.96% compared with the EA, AIA, and A-TADMM algorithms, respectively.

[0053] Step 5 The specific steps are as follows: In order to reasonably allocate costs between different integrated energy systems and between suppliers, firstly, the deviation motivation of sub-alliances is modeled by evaluating the overall and individual rationality of the integrated energy system. Secondly, the degree of dissatisfaction of sub-alliances with the cost allocation strategy is modeled with cost minimization as the cooperative game goal. Finally, based on these models, a cost allocation plan for shared energy storage is formulated.

[0054] Step 6 The specific steps are as follows: The cooperative game method improved by constraint generation technology is different from the original cooperative game method. ( ) are compared with the solution iteration times and calculation time under the same conditions. The results are shown in Table 11:

[0055] Table 11 shows that when the system scale is small, there is little difference in the number of iterations and calculation time between the improved algorithm and the improved algorithm; but as the scale increases, the improved algorithm has a significantly reduced calculation time due to reduced complexity, indicating that the improved algorithm is more suitable for large-scale systems.

[0056] In summary, the present invention provides a joint planning method based on shared energy storage suppliers and local integrated energy systems, which solves the core problems existing in the prior art in the joint planning process between shared energy storage suppliers and local integrated energy systems, such as complex interest games, unreasonable cost allocation, and difficulty in maximizing the overall benefits of the system, and brings higher shared energy storage benefits and lower total operating costs to suppliers and regional integrated energy systems; it improves the original Nash bargaining method by introducing bargaining levels, and provides a new idea for cost allocation; in terms of algorithm optimization, it adopts a double-update accelerated iteration strategy to improve the solution efficiency and accuracy, and has certain innovation; it is based on the improved cooperative game method The cost allocation model ensures the rationality and fairness of cost allocation by quantifying the deviation motivation and dissatisfaction degree of the subsystem, reflecting a high degree of creativity; a variety of factors are comprehensively considered in the planning model, including shared energy storage service revenue, energy storage investment cost, etc., making the planning results more comprehensive and accurate, demonstrating the author's innovative thinking; the economy and practicality of the proposed planning method are verified through specific examples, demonstrating the innovation and application value of the method; the planning method can be widely used in smart grids, microgrids and distributed energy systems, promoting the development of energy Internet in a more efficient, fairer and more sustainable direction, and promoting the optimization and upgrading of energy structure and the improvement of energy utilization efficiency.

Claims

1. A joint planning method based on shared energy storage providers and local integrated energy systems, characterized by: The method comprises the following steps: Step 1: Establish a joint planning framework for regional integrated energy systems and shared energy storage suppliers based on a two-stage multi-cooperative game; Step 2: Solve the ex post allocation problem of shared energy storage investment and operating costs within the planning cycle of the regional integrated energy system.

2. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 1 is characterized in that: The specific steps of step 1 include: Step 1.1: Establish a joint planning model for the regional integrated energy system and shared energy storage providers in the first phase of the joint planning framework; Step 1.2: Use the marginal benefit contribution rate to quantify the bargaining level of the regional integrated energy system and shared energy storage suppliers to improve the original Nash bargaining method; Step 1.3: Establish planning and operation constraints of the joint planning model; Step 1.4: Improve the alternating direction multiplier method AM-ADMMD based on the double update accelerated iteration strategy.

3. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 2 is characterized in that: The specific steps of establishing the joint planning model in step 1.1 include: Step 1.1.1: Shared energy storage suppliers need to customize reasonable energy storage planning and scheduling strategies based on the shared energy storage needs of the regional integrated energy system; Step 1.1.2: The shared energy storage supplier is the price setter for shared energy storage, including the energy unit price and power capacity unit price of shared energy storage; Step 1.1.3: The shared energy storage charging and discharging power is provided by the distribution network, and the shared energy storage supplier needs to pay a certain fee; Step 1.1.4: The regional integrated energy system of multiple integrated energy systems rents shared energy storage from the shared energy storage provider; Step 1.1.5: Select a representative from the electric vehicle cluster to summarize the daily discharge strategies of each electric vehicle and notify other electric vehicles; Step 1.1.6: Each integrated energy system customizes its own energy conversion equipment planning strategy and independently pays for the investment and operating costs of the energy conversion equipment.

4. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 2 is characterized in that: The specific steps of step 1.2 to improve the Nash bargaining method include: Step1.2.1: Establish the original joint planning objective function; The original Nash bargaining method is shown below, and its goal is to maximize the overall benefits of the cooperative group; （1）； In the formula, is the number of participants in the cooperative game; and Respectively The benefits of each participant before and after participating in the cooperative game; Step1.2.2: Improve the original Nash bargaining method; The improved Nash bargaining method reflects the differences in bargaining power of participants by introducing bargaining levels. Based on the formula of the original Nash bargaining method, the joint planning objective function proposed by the improved Nash bargaining method maximizes the overall benefits of the regional integrated energy system and shared energy storage suppliers, as shown in the following formula: （2）； In the formula, and are the benefits of shared energy storage suppliers before and after participation, and are the bargaining levels of shared energy storage suppliers and regional integrated energy systems, and They are the total costs of the integrated energy system before and after the formation of the integrated energy system alliance; Shared storage provider revenue during planning period Related to shared energy storage service revenue, energy storage investment costs, energy storage maintenance costs, operating costs and the residual use value of energy storage equipment; Assumptions The integrated energy system and shared energy storage are used together, then the objective function of each subsystem is The aim is to minimize the sum of the costs of each subsystem planning cycle of the regional integrated energy system, as shown in the following formula: （3）； In the formula, the total cost of sharing energy storage for regional integrated energy systems; and Respectively The planned cost of energy conversion equipment and electricity purchase costs for a comprehensive energy system; For the The residual use value of energy conversion equipment invested in a comprehensive energy system, For planning years, is the discount factor; and It can be quantified using the marginal contribution rate of the regional integrated energy system and shared energy storage supplier, that is, the rate of change in income before and after the participant participates, expressed by the following formula: （4）； （5）。 5. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 2 is characterized in that: The specific steps of step 1.4 include: Step 1.4.1: Use the reductio ad absurdum method to process the joint planning objective function; The objective function of the joint planning after the reductio ad absurdum method is as follows: （6）； In the formula, and They are the revenues of shared energy storage providers and regional integrated energy systems; Step 1.4.2: Use the logarithmic method to process the objective function; The objective function after logarithmic processing is shown as follows: （7）； Step 1.4.3: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the reductio ad absurdum method; （8）； （9）； In the formula, and are the Lagrange multipliers of the energy and power capacities of leased shared energy storage, respectively; and are the Lagrange multipliers of the shared energy storage charging and discharging power respectively; and are the penalty coefficients for leasing the energy and power capacity of shared energy storage, respectively; and are the penalty coefficients for the shared energy storage charging and discharging power respectively; and They are the shared energy storage energy and power capacity that the shared energy storage provider hopes to lease to the regional integrated energy system; and are the shared energy storage energy and power capacity that the regional integrated energy system expects to lease, respectively; and They are the shared energy storage charging and discharging power that suppliers expect the regional integrated energy system to use; and They are the shared energy storage charging and discharging power that the regional integrated energy system expects to use; Step 1.4.4: Construct the augmented Lagrangian function of the shared energy storage supplier and the regional integrated energy system based on the objective function processed by the logarithmic method; （10）； （11）； In the formula, and are the Lagrange multipliers of the rental unit price of shared storage energy and power capacity, respectively; and are the Lagrange multipliers of the rental unit price of the shared energy storage charging and discharging power, respectively; and are the unit prices for shared storage energy and power capacity that suppliers expect to be paid by the regional integrated energy system, respectively; and are the unit prices of shared energy storage energy and power capacity that the regional integrated energy system expects to pay, respectively.

6. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 1 is characterized in that: The specific steps of step 2 include: Step 2.1: Propose a shared energy storage cost allocation model for regional integrated energy systems based on an improved cooperative game theory framework; Step 2.2: Improve the cooperative game method based on constraint generation technology.

7. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 7 is characterized in that: The specific steps of step 2.1 include: Step 2.1.1: Establish a model of the deviation motivation and dissatisfaction degree of subsystems in the regional integrated energy system towards the large system; Set As a A collection of regions composed of integrated energy systems, defined Reason Any sub-alliance composed of regional integrated energy systems, The number of The cost allocation strategy is composed of vector Indicates that the The cost of an integrated energy system For each subsystem’s objective function, the set Refers to all that satisfy overall and individual rationality , as shown below: （12）； nuclear The set of refers to the attribution that satisfies the rationality of the alliance, that is, the sub-alliance has no motivation to deviate from the regional integrated energy system; （13）； For cooperative games with the goal of minimizing costs, the sub-coalition The excess Reflects the sub-alliance Cost allocation strategy The dissatisfaction is shown in the following formula: （14）； Step 2.1.2: Establish a shared energy storage cost allocation model for regional integrated energy systems; The cost allocation model can be expressed as: （15）； In the formula, is an auxiliary variable.

8. The joint planning method based on shared energy storage suppliers and local integrated energy systems according to claim 7 is characterized in that: The specific steps of step 2.2 include: Step 2.2.1: Find the dissatisfied sub-alliance in the sub-alliance set; （16）； In the formula, For the The binding constraints set at the iteration hour, , in getting and Then, by solving the following formula, we can Find the largest dissatisfied sub-alliance among the sub-alliance sets other than （17）； In the formula, is the most unsatisfactory optimal allocation strategy, and we get Then constraints can be generated; Step 2.2.2: Find all satisfied sub-alliances under the optimal allocation strategy; Repeat Step 2.2.1 to get a new , and ,until Less than 0, we can further obtain the optimal allocation strategy that satisfies the remaining sub-alliances. , all satisfied sub-coalitions are represented by: （18）； Step 2.2.3: Obtain constraints; According to Step 2.2.2, determine the binding constraints Is the total greater than , if greater than , the solution is uniquely determined, and the optimal cost allocation strategy can be obtained ,otherwise , Repeat the above step until the total number of bound constraints is greater than or equal to .

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