Shared energy storage optimal configuration method and system based on load-locus type demand response

Abstract

The application discloses a kind of based on load quasi-linear type demand response's shared energy storage optimization configuration method and system, belong to new energy grid-connected control technology.The application first constructs a user side shared energy storage operation mode, then proposes the quasi-line forming mode based on performance incentive mechanism and incentive subsidy strategy, fully tap microgrid user and shared energy storage common value, simplify the difficulty of guiding user to change power consumption mode.To make up the rigidity load and the user-friendly degree of weak decision-making ability is not good, it is not conducive to attract mass users to participate in the limitation.Further, the user side shared energy storage configuration model with the sum of shared energy storage operator and energy storage user maximum social welfare best as target is constructed, the model considers two links of planning and operation, pricing is carried out according to the substitution benefit of each market member to other market members in running link, satisfies social welfare maximization, incentive compatibility and individual rationality etc.

Description

Technical Field

[0001] This invention belongs to the field of new energy grid connection control technology, specifically involving a shared energy storage optimization configuration method and system based on load quasi-linear demand response. Background Technology

[0002] With the development of distributed energy resources and diverse loads, the surge in intermittent renewable energy generation and controllable loads presents a significant challenge: how to fully utilize these renewable energy sources and coordinate and match flexible loads on the user side. Utilizing demand response planning to unlock the potential of flexible loads to improve operational technology and economics is currently a hot research topic.

[0003] Currently, there are two incentive mechanisms for demand response. Price-based demand response is a non-voluntary adjustment method for users. For example, based on establishing a numerical relationship between carbon emissions and photovoltaic power generation, it introduces the concept of a discount factor, allowing producers and consumers to actively participate in demand response at different times to obtain lower discounted prices. It also includes a dynamic pricing scheme with an allocable electricity price designed through linear regression, maximizing the profits of microgrid load users. However, these methods are not very user-friendly for rigid loads and users with weak decision-making capabilities, making it difficult to attract a large number of users and limiting their application to small-scale systems. Incentive-based demand response primarily compares load data when users do not participate in demand response with the actual load curves of users to evaluate the contribution of user participation in demand response.

[0004] "Optimal Energy Storage Configuration in Photovoltaic-Storage Microgrids Considering Demand Response and Source-Load Uncertainty" addresses the capacity optimization configuration problem of grid-connected photovoltaic-storage microgrid energy storage systems considering demand response. It establishes a load guidance mechanism based on load scheduling priorities and proposes an energy storage planning and configuration strategy that considers demand response guidance. "Impact of Renewable Energy Integration on a Novel Method for Pricing Incentive Payments of Incentive-Based Demand Response Program" proposes a method for pricing incentive payments for incentive-based demand response programs using a social welfare maximization framework, ensuring the interests of all participants. However, this method requires the dispatch center to calculate the load baseline for each user, placing enormous pressure on its communication and computational capabilities. Therefore, there is an urgent need to research incentive methods that enable large-scale and standardized implementation of demand response.

[0005] While demand response improves the flexibility of energy management, its unpredictable behavior exacerbates the supply-demand mismatch in energy consumption. Shared energy storage, which addresses the needs of multiple users, not only has the advantages of improving the reliability and economy of load power consumption, but also can revitalize the scheduling space of limited flexible resources within the system, thereby improving resource utilization and system reliability.

[0006] Due to the aforementioned advantages, shared energy storage has been extensively studied. The paper "A Coordinated Control Strategy for Multiple Loads in Distribution Areas Based on Shared Energy Storage" proposes a coordinated control strategy for multiple loads in distribution areas based on shared energy storage. On the user side, load scheduling is optimized intraday based on shared energy storage prices and time-of-use pricing, combined with the user's own needs. On the distribution area side, coordinated scheduling is achieved by adjusting shared energy storage prices and user flexible loads. Regarding the equilibrium state of supply and demand in the point-to-point market model of residential shared energy storage units, the paper "Peer-to-peer transactive mechanism for residential shared energy storage" proposes a service pricing and load scheduling method. However, shared operation requires a fair and efficient resource allocation mechanism to balance the interests of multiple stakeholders; effective profit allocation is a necessary way to more economically configure energy storage systems.

[0007] In the electricity market environment, accurate information such as the construction, operation, and maintenance costs of energy storage systems, as well as the energy costs for energy storage users, is not readily disclosed, leading to information asymmetry among market participants. In mechanism design theory, the Vickrey-Clarke-Groves (VCG) mechanism is a design method that incentivizes market participants to disclose accurate information. This method can guide market participants to report accurate information and achieve a fair allocation of payments to each market participant. Summary of the Invention

[0008] Purpose of the invention: Under the construction goal of new power systems, in order to promote the coordinated operation of flexible resources, this invention provides a method and system for optimizing the configuration of shared energy storage based on load quasi-linear demand response, so as to give full play to the demand response capability of the load side.

[0009] Technical Solution: A method for optimizing the configuration of shared energy storage based on load quasi-linear demand response. This method includes constructing a user-side shared energy storage operation mode. Under this mode, users meet their electricity demand through their own wind and solar power generation, purchasing electricity from shared energy storage, and purchasing electricity from the grid; and achieving electricity consumption through their own load absorption and selling surplus electricity to shared energy storage. The shared energy storage refers to charging (purchasing electricity from the grid and purchasing surplus wind and solar power from users) and discharging (selling electricity to users to provide electricity services).

[0010] This method includes the following steps for optimizing the configuration of shared energy storage:

[0011] (1) Based on the tracking accuracy between the actual load curve and the load baseline, participants with outstanding performance will be given tiered rewards.

[0012] Let the benchmark incentive price be p. m The benefit of user i participating in the quasi-linear demand response is defined as follows:

[0013] I CDL,i =p m ·P D ·ε i

[0014] Among them, P D The total adjustable power capacity declared by the user up to the date of this report, ε i The similarity index is used to describe the similarity between the user's actual load pattern and the adjustment target;

[0015] (2) Construct a shared energy storage configuration model based on maximizing social welfare. The shared energy storage configuration takes into account the economics of both investment and operation, which are composed of user revenue and shared energy storage costs, respectively. The overall objective function is to maximize social welfare, expressed as:

[0016] W all =max(I user -C ses )

[0017] In the formula, user revenue I user With shared energy storage cost C ess Maximizing the difference corresponds to social welfare W all Optimal;

[0018] (3) Establish a VCG-based resource allocation mechanism to encourage shared energy storage investors to report their actual operation and maintenance costs during operation, and to incentivize users of energy storage charging and discharging services to truthfully report their energy consumption revenue, including:

[0019] (3.1) Stage 1: The value of market members is reflected in their substitution benefits for other members and their contribution to the overall benefits of the system. The value of shared energy storage is reflected in the change of total social welfare of the market before and after participation in the market. Similarly, the value of a user load is also measured by the change of total social welfare of the market before and after participation in the market.

[0020] make For the social welfare of all members participating in the system market, The expression for the payment received by shared energy storage, excluding the social welfare of the new market for shared energy storage systems, is as follows:

[0021]

[0022] make For the social welfare of the new system market that does not include user load i, the payment received by user i is:

[0023]

[0024] (3.2) Second stage: Consider the operating costs and revenues of shared energy storage and users to calculate the initial revenue, and calculate the initial investment cost of the shared energy storage power station. Introduce a revenue redistribution factor to redistribute the initial cost, thereby obtaining the final revenue of users and shared energy storage. The specific expression is as follows:

[0025]

[0026] In the formula, To share the benefits of the energy storage system; Payment for user i; β This is a profit redistribution factor.

[0027] Furthermore, the similarity ε used in step (1) to describe the user's actual load pattern and the adjustment target i The calculation is as follows:

[0028]

[0029] In the formula: d i Used to describe the Euclidean distance between the actual load line and the load baseline; ε0 is the similarity standard value for determining whether a demand response reward is obtained; This represents the actual load pattern of user i's flexible load. This represents the unadjustable load of user i during time period t. The total adjustable power declared by the i-th user before the current date; The shape of the load guideline after standardization is calculated as follows:

[0030]

[0031] in Let be the total load at time t; in addition, the load profile is subject to the following constraints:

[0032]

[0033] In the formula, where For the amount of electricity purchased, The actual output of new energy sources during period t. Let t be the predicted power generation from wind and solar power within the system during time period t. This represents the maximum amount of electricity that can be purchased.

[0034] Furthermore, considering the defined similarity ε iSince it's a piecewise function, if the similarity doesn't reach the set threshold, the problem will eventually transform into a linear programming problem; once the similarity reaches or exceeds the threshold, a variable C needs to be introduced for calculation.

[0035]

[0036] In the formula, d is transformed into the form of L2 norm. The corresponding term in the objective function of the benefit after user i participates in the quasi-linear demand response is replaced with a new variable C. The above formula is then transformed into an inequality and added as a constraint, thus obtaining a new formulation of the optimization problem:

[0037] I CDL,i =p m P D -C

[0038]

[0039] Furthermore, step (2) includes constructing a user revenue model and a shared energy storage cost model, as detailed below:

[0040] (2.1) The user revenue model has the following objective function:

[0041]

[0042] The total user revenue consists of the demand response revenue of each user, the reduced cost of curtailment penalties, the reduced cost of purchasing electricity from the grid, and the revenue from selling curtailed electricity to shared energy storage; where λ ab1 For the penalty fee for abandoning electricity, and These represent the amount of electricity abandoned by the i-th user during time period t when there is no energy storage and when there is energy storage, respectively. and Let λ represent the electricity purchased by the i-th user during time period t without energy storage and with energy storage, respectively; ab2 The price of electricity sold off as waste power. This represents the amount of electricity abandoned by the i-th user during time period t.

[0043] Regarding user collaborative operation, it can avoid simultaneous power curtailment and power purchase from the grid, power balance, the scope of renewable energy power use, the range of charging and discharging power, and the constraints of avoiding simultaneous charging and discharging and only being able to purchase power from the grid. The constraints are as follows:

[0044]

[0045]

[0046] In the formula: and These represent the load and the output of the new energy source for the i-th user during time period t, respectively. and These are Boolean variables that represent the status of users abandoning or purchasing electricity, respectively. If demand exceeds supply, users need to purchase electricity; otherwise, they abandon electricity. and Let represent the charging amount and discharging amount of the i-th user during time period t, respectively; The upper limit of new energy output allocated to the i-th user during time period t; The maximum charging and discharging power for the i-th user; and These represent the user's charging and discharging status bits, which are 0-1 variables;

[0047] (2.2) Shared energy storage cost model, the objective function of which is:

[0048]

[0049] The total cost of shared energy storage consists of the cost of purchasing abandoned electricity from users, the cost of purchasing electricity from the grid, and the operation and maintenance costs; among which, N represents the amount of electricity purchased from the grid for shared energy storage during time period t; om Shared energy storage daily operation and maintenance rate; and These represent the charging and discharging amounts of the shared energy storage during time period t, respectively.

[0050] During the collaborative operation between users and shared energy storage power stations, the specific constraints that need to be ensured are as follows:

[0051]

[0052] In the formula, This represents the total charging amount, including both user-side charging and electricity purchased from the grid using shared energy storage. Maximum charging and discharging power for shared energy storage; and These represent the status bits for charging and discharging of shared energy storage, respectively. η represents the capacity state of the shared energy storage during time period t; η represents the charging and discharging efficiency of the shared energy storage. and These represent the upper and lower limits of the shared energy storage capacity, respectively.

[0053] Based on the implementation of the above method, the present invention also provides a shared energy storage optimization configuration system based on load quasi-linear demand response. The system includes a memory, a processor, and a computer program stored in the memory. The processor executes the computer program to implement the above-mentioned shared energy storage optimization configuration method based on load quasi-linear demand response.

[0054] A computer-readable storage medium storing a computer program / instructions that, when executed by a processor, implement a method for optimizing the allocation of shared energy storage based on load-guideline demand response.

[0055] Beneficial effects: The essential features and significant effects of this invention include:

[0056] (1) Based on the spatiotemporal complementary characteristics of energy consumption by multiple types of energy storage users, a basic framework for the user-side shared energy storage operation mode is proposed to achieve economical energy supply and efficient electricity use.

[0057] (2) A guideline formation method and incentive subsidy strategy based on performance incentive mechanism are proposed to fully explore the common value of microgrid users and shared energy storage, and simplify the difficulty of guiding users to change their power consumption patterns. This is to compensate for the limitations of rigid loads and weak decision-making capabilities, which make it difficult to attract a large number of users.

[0058] (3) A user-side shared energy storage configuration model was constructed with the goal of maximizing the social welfare of the sum of the surplus of shared energy storage operators and energy storage users. The model considers two stages: planning and operation. In the operation stage, pricing is based on the substitution benefits of each market member to other market members, satisfying the properties of maximizing social welfare, incentive compatibility and individual rationality. Attached Figure Description

[0059] Figure 1 This is a user-side shared energy storage operation framework diagram;

[0060] Figure 2 This is a diagram showing the wind and solar power output of each industrial user in the embodiment;

[0061] Figure 3 These are the load curves for each industrial user in the embodiment;

[0062] Figure 4 It is the optimal energy storage capacity curve under the quasi-linear demand response of various industrial users;

[0063] Figure 5 It is a comparison curve of load adjustment before and after user participation in demand response (CDL, CBL);

[0064] Figure 6 It includes the load baseline and load curves for various industrial users (with energy storage involved);

[0065] Figure 7 It is the user's charging and discharging power versus the shared energy storage's SOC state curve;

[0066] Figure 8 This is a comparison chart of adjustable loads with and without energy storage participating in user shaping capacity adjustment;

[0067] Figure 9 This is a sensitivity analysis of the price factor on the optimal configuration size of shared energy storage;

[0068] Figure 10 It is the optimal energy storage capacity curve under different user participation conditions;

[0069] Figure 11 It describes the income and expenditure of each entity under different interest distribution schemes. Detailed Implementation

[0070] To illustrate the technical solution provided by this invention in detail, further description is provided below with reference to the accompanying drawings.

[0071] This embodiment takes a centralized shared energy storage system and three self-equipped new energy power generation equipment users in the region as examples to verify the proposed configuration strategy through a calculation example, and to quantitatively analyze the impact of factors such as price, load type, and allocation scheme on the configuration results.

[0072] 1. User-side shared energy storage operation model

[0073] The framework of the user-side centralized shared energy storage operation model is as follows: Figure 1 As shown, there are two types of operating entities: users and shared energy storage operators.

[0074] The internal energy supply relationships of each operating entity are as follows:

[0075] 1) Users: meet their electricity needs by generating electricity from wind and solar power, purchasing electricity from shared energy storage, and purchasing electricity from the grid; and consume electricity by absorbing their own load and selling surplus electricity to shared energy storage.

[0076] 2) Shared energy storage: charging, purchasing electricity from the grid and buying surplus wind and solar power from users; discharging, selling electricity to users to provide energy services.

[0077] 2. Formation of the excitation load guideline

[0078] Taking into account the response performance compensation in the frequency regulation incentive measures, the regional power grid will provide tiered rewards to participants with excellent performance based on the tracking accuracy between the actual load curve and the load baseline.

[0079] Let the benchmark incentive price be p. m The benefit of user i participating in the quasi-linear demand response is defined as follows:

[0080] I CDL,i =p m ·P D ·ε i (1)

[0081] Among them, P DThe total adjustable power capacity declared by the user up to the date of this report, ε i The similarity index is used to describe the similarity between the user's actual load pattern and the adjustment target. The specific calculation is as follows:

[0082]

[0083] In the formula: d i The Euclidean distance between the actual load line and the load baseline is used to describe the similarity standard value for determining whether a demand response reward is obtained. This represents the actual load pattern of user i's flexible load. This represents the unadjustable load of user i during time period t. This represents the total adjustable power consumption declared by the i-th user on the current day. The shape of the load guideline after standardization is calculated as follows:

[0084]

[0085] in Let be the total load at time t; in addition, the load profile is subject to the following constraints:

[0086]

[0087] In Equation (5), the nodal power balance constraint abandons the traditional supply-demand balance equation constraint form and instead adopts a new expression of "adjustable" balancing "non-adjustable". For the amount of electricity purchased, The actual output of new energy sources during period t. Equation (6) emphasizes that the load baseline aims to adjust the original load pattern, rather than increase or decrease the total electricity consumption; Equation (7) imposes constraints on the electricity purchased by users and the upper limit of new energy output, in which Let t be the predicted power generation from wind and solar power within the system during time period t. This represents the maximum amount of electricity that can be purchased.

[0088] Considering the defined similarity ε i Since it is a piecewise function, if the similarity does not reach the set threshold, the problem will eventually transform into a linear programming problem; once the similarity reaches or exceeds the threshold, variable C needs to be introduced for further analysis.

[0089]

[0090] The d in the formula is transformed into the form of the L2 norm. The corresponding terms of the objective function in equation (1) are replaced with the new variable C, and equation (8) is transformed into an inequality and added as a constraint, thus obtaining a new formulation of the optimization problem:

[0091]

[0092] 3. Construct a shared energy storage configuration model based on maximizing social welfare.

[0093] The configuration of shared energy storage takes into account the economics of both investment and operation, which are composed of user revenue and the cost of shared energy storage, respectively. The overall objective function is to maximize social welfare.

[0094] W all =max(I user -C ses (10)

[0095] In the formula, user revenue I user With shared energy storage cost C ess Maximizing the difference corresponds to social welfare W all Optimal.

[0096] 3.1 User Revenue Model

[0097] As the user subject in the proposed social model, its objective function is:

[0098]

[0099] In the formula: the total user revenue consists of the demand response revenue of each user, the reduced cost of curtailment penalties, the reduced cost of purchasing electricity from the grid, and the revenue from selling curtailed electricity to shared energy storage. Wherein, λ ab1 For the penalty fee for abandoning electricity, and These represent the amount of electricity abandoned by the i-th user during time period t when there is no energy storage and when there is energy storage, respectively. and Let λ represent the electricity purchased by the i-th user during time period t without energy storage and with energy storage, respectively; ab2 The price of electricity sold off as waste power. This represents the abandoned electricity (electricity charged into shared energy storage) sold by the i-th user during time period t.

[0100] Regarding user collaborative operation, there are constraints on avoiding simultaneous power curtailment and purchasing power from the grid, power balance, the scope of renewable energy power use, the range of charging and discharging power, avoiding simultaneous charging and discharging, and only being able to purchase power from the grid. The constraints are as follows:

[0101]

[0102]

[0103] In the formula: and These represent the load and the output of the new energy source for the i-th user during time period t, respectively. and These are Boolean variables that represent the status of users abandoning or purchasing electricity, respectively. If demand exceeds supply, users need to purchase electricity; otherwise, they abandon electricity. and Let represent the charging amount and discharging amount of the i-th user during time period t, respectively; The upper limit of renewable energy output allocated to the i-th user during time period t. Constraints (17) include balance constraints, renewable energy output constraints, constraints to prevent users from charging and discharging simultaneously, and constraints on purchasing electricity. The maximum charging and discharging power for the i-th user; and These represent the user's charging and discharging status bits, which are 0-1 variables.

[0104] 3.2 Shared Energy Storage Cost Model

[0105] As the shared energy storage entity in the proposed social model, its objective function is:

[0106]

[0107] In the formula, the total cost of shared energy storage consists of the cost of purchasing abandoned electricity from users (user charging), the cost of purchasing electricity from the grid, and the operation and maintenance costs. N represents the amount of electricity purchased from the grid for shared energy storage during time period t; om Shared energy storage daily operation and maintenance rate; and These represent the charging and discharging amounts of the shared energy storage during time period t, respectively.

[0108] During the collaborative operation between users and shared energy storage power stations, the specific constraints that need to be ensured are as described in equation (18):

[0109]

[0110] The formula includes power balance constraints, upper and lower limits of charging and discharging power constraints, and constraints to prevent simultaneous charging and discharging. This represents the total charging amount, including both user-side charging and electricity purchased from the grid using shared energy storage. Maximum charging and discharging power for shared energy storage; and These represent the status bits for charging and discharging of shared energy storage, respectively. η represents the capacity state of the shared energy storage during time period t; η represents the charging and discharging efficiency of the shared energy storage. and These represent the upper and lower limits of the shared energy storage capacity, respectively.

[0111] 4. VCG-based resource allocation mechanism

[0112] To encourage shared energy storage investors to report their actual operation and maintenance costs during operation, and to incentivize users of energy storage charging and discharging services to truthfully report their energy consumption revenue, this section proposes a VCG-based resource allocation mechanism.

[0113] 4.1 Profit Distribution Based on VCG Mechanism (Phase I)

[0114] The value of market participants is reflected in their substitution benefits for other participants and their contribution to the overall system benefits. The value of shared energy storage is expressed as the change in total social welfare of the market before and after participation; similarly, the value of a user's load is also measured by the change in total social welfare of the market before and after its participation.

[0115] make For the social welfare of all members participating in the system market, This excludes the social welfare of the new market for shared energy storage systems. The expression for the payment received from shared energy storage is:

[0116]

[0117] make For the social welfare of the new system market that does not include user load i, then under the VCG mechanism proposed in this invention, the payment received by user i is:

[0118]

[0119] 4.2 Two-stage profit distribution based on VCG mechanism

[0120] Calculating initial revenue requires considering not only the operating costs and revenues of shared energy storage and users, but also the initial investment cost of the shared energy storage power station. Therefore, a revenue redistribution factor needs to be introduced to redistribute the initial costs, thereby obtaining the final revenue for users and shared energy storage. The specific expression is as follows:

[0121]

[0122] In the formula, To share the benefits of the energy storage system; β is the payment for user i; β is the revenue redistribution factor.

[0123] The rapid development of microgrid systems has led to the emergence of distributed power generation resources on the user side. To encourage users to absorb more renewable energy, shared energy storage systems are introduced to coordinate with the user's controllable load. To verify the effectiveness of the proposed configuration strategy and benefit distribution model, a case study analysis was conducted using three industrial users and one shared energy storage system within the same region. The system parameters for the case study are shown in Table 1. The wind and solar power output, total load, and unadjustable load within each industrial user are as follows: Figure 2 , Figure 3As shown in Table 2, the time-of-use electricity price on the grid side within a day is as follows.

[0124] Table 1 Basic Parameters Table

[0125]

[0126] Table 2 Time-of-use Electricity Prices on the Grid Side

[0127]

[0128] Energy storage configuration result analysis: such as Figure 4 As shown, with all users participating in demand response and using shared energy storage, the social welfare reached 41,297.71 yuan per day, and the final optimized capacity was 3,400 kWh.

[0129] 5.2.2 Quasi-linear Demand Response Strategy

[0130] Microgrid operators publish load guidelines based on adjustable load information and predicted renewable energy output. Upon receiving these load guidelines, users proactively adjust their electricity consumption patterns to improve their similarity to the load guidelines, thereby maximizing incentive benefits. The demand response model proposed in this invention is compared with the baseline model.

[0131] like Figure 5 The first column of the graphs compares the load curves of each user before and after participating in the linear demand response (CDL) model, following the publication of the load baseline by the microgrid system within the region. The second column compares the curves of each user before and after participating in the baseline demand response (CBL) model based on their own flexible load coefficient. The third column compares the normalized load curve shapes of each user after participating in both demand responses with the load baseline published by the microgrid system. It is evident that the linear demand response proposed in this invention significantly alters the user load. Compared to the baseline demand response, the linear demand response achieves better tracking of the load baseline and exhibits a more pronounced load shaping effect.

[0132] Based on the quasi-linear demand response proposed in this invention, an analysis of user load changes is performed: User 1 and User 3 adjust relatively high load power, while User 2 adjusts relatively less. According to the analysis of adjustable load reporting by each user in the original data of the example, User 3 reports the most adjustable load, followed by User 1, and User 2 reports the least. This is also consistent with... Figure 4 The load adjustment situation after each user participated in the demand response was consistent.

[0133] 5.2.3 Shared energy storage participates in user shaping capacity regulation

[0134] To improve the energy efficiency of users in the entire regional microgrid system, this section introduces the process of shared energy storage participating in user load shaping. After shared energy storage participates in the regulation, the three users in the regional microgrid system changed their electricity consumption habits by increasing energy storage charging and discharging activities, thereby shaping their own load curves. The similarity of users 1 and 3 both changed from below 70% to 74.96% and 70.69%, respectively. (See...) Figure 6 .

[0135] As shown in Table 3, after introducing shared energy storage to participate in industrial user demand response, the load curve similarity of the three users all reached above the threshold, and they received incentive benefits. Based on a comparison of data before and after the shared energy storage was used to adjust user load shaping capabilities, the electricity purchase cost and curtailment fees for each user decreased after using shared energy storage. Specifically, user 1's net expenditure decreased by RMB 10,949.42, user 2's net expenditure decreased by RMB 503.51, and user 3's net expenditure decreased by RMB 8,933.77.

[0136] Table 3 Comparison of User Demand Response Expenditure for Shared Energy Storage Participation

[0137]

[0138]

[0139] Figure 7 The graph shows the daily charging and discharging of shared energy storage when participating in user capacity adjustment. As can be seen, User 1 has higher charging and discharging demands during the periods of 12:00-17:00 and 18:00-20:00, User 2 has higher discharging demands during the period of 10:00-12:00, and User 3 has lower charging and discharging demands throughout the day. This is related to the fact that User 1's photovoltaic power generation equipment generates excess power during the midday and afternoon periods, and that User 2's wind power generation equipment cannot meet its load demand during the morning period.

[0140] Figure 8 The adjustable load reported by each user to the regional power grid system before and after using shared energy storage is compared with the three sub-graphs. It can be seen that user 1 has the largest increase in adjustable load under the influence of shared energy storage in shaping capacity adjustment, followed by user 3, and user 2 has the smallest increase. This is because user 1 has the lowest load curve similarity among the three before the shared energy storage participates in shaping capacity adjustment, and the largest gap from the threshold. Therefore, user 1 needs to report more adjustable load and use shared energy storage to assist in adjusting its shaping capacity. User 2 has the smallest utilization of shared energy storage. Since user 3's load curve similarity is very close to the threshold before the shared energy storage participates in shaping capacity adjustment, it only needs to report a small amount of adjustable load and use shared energy storage to assist in adjusting its shaping capacity to achieve the purpose of obtaining incentive benefits.

[0141] Analysis of Revenue Distribution Results: According to Table 4, based on the core logic of the VCG benefit distribution mechanism, it can be calculated that User 1 contributes the most to this microgrid system and receives the highest revenue after utilizing energy storage services for demand response. Therefore, User 1 also pays the most, accounting for 98.75% of the user-level expenditure. This distribution scheme also introduces a redistribution factor to redistribute the initial revenue, thereby shortening the investment payback period. The final calculated investment payback period for shared energy storage is approximately 5.32 years.

[0142] Table 4. Distribution Results of Shared Energy Storage and User Payments

[0143]

[0144] Price Factors: Treating price factors as uncertainties, we analyze the sensitivity of the benchmark incentive unit price, the curtailment penalty unit price, and the energy storage operation and maintenance cost to the shared energy storage capacity configuration results. Using the price parameters in Table 3 as benchmarks, we explore shared energy storage configuration schemes when these prices vary from -0.4% to 0.4%. The results are shown in [Table 3]. Figure 9 .

[0145] according to Figure 9 It can be observed that among the three uncertainties, the unit price of curtailment penalties and the operation and maintenance costs of energy storage have a more significant impact on the final operating cost of shared energy storage. Furthermore, since shared energy storage power stations serve different users, fluctuations in the benchmark incentive unit price, the unit price of curtailment penalties, and the operation and maintenance costs of energy storage may have varying degrees of impact on the scale of shared energy storage deployment. Therefore, from the perspective of the optimal configuration size of shared energy storage power stations, more attention should be paid to the impact of the unit price of curtailment penalties and the operation and maintenance costs of energy storage on the configuration results, while less attention should be paid to the benchmark incentive unit price.

[0146] Sensitivity analysis is conducted on the size of shared energy storage capacity configuration based on different proportions of user load participation in quasi-linear demand response. The specific analysis scenarios are as follows: In three scenarios where the regional microgrid system contains only user 1 and user 2, only user 1 and user 3, and only user 2 and user 3, the changes in the final shared energy storage configuration or social welfare are compared.

[0147] like Figure 10 As shown, the following results are obtained:

[0148] (1) With all users participating in demand response and using shared energy storage, its social welfare reached RMB 41,297.71 per day, and the final optimized capacity was 3,400 kWh.

[0149] (2) The social welfare of the new system without user 2 is second only to the first case, and the optimal capacity is 3200 kWh;

[0150] (3) The social welfare of the new system without user 3 is second only to the second case, and the optimal capacity is 2000kWh;

[0151] (4) The social welfare of the new system without user 1 is second only to the third scenario, and the optimal capacity is 200 kWh. Therefore, for user 1, who is equipped with photovoltaic power generation equipment and whose load shaping is most pronounced before and after participating in quasi-linear demand response, the contribution to improving social welfare is greater, and the impact on the size of the shared energy storage configuration is also more significant. 5.3.3 Allocation Scheme Factors

[0152] The benefit allocation scheme based on Shapley value is compared with the benefit allocation scheme based on VCG proposed in this invention, and the payback period of the corresponding shared energy storage configuration is analyzed.

[0153] according to Figure 11 As shown, it is easy to see that:

[0154] (1) Compared with the VCG Phase 1 scheme, the scheme based on Shapley value only highlights the marginal contribution of user 1, so that user 1 pays relatively more for shared energy storage under this benefit distribution scheme. Under this scheme, the payback period of shared energy storage is 7.97 years.

[0155] (2) The profit distribution scheme of VCG I stage did not take into account the initial investment cost of shared energy storage. It only reorganized the income and expenditure of each user entity in the microgrid system. Under this scheme, the payback period of shared energy storage is 7.98 years.

[0156] The profit-sharing scheme for the VCGⅡ phase takes into account the initial investment cost of shared energy storage. User 1, with the highest contribution, should bear more of the payment costs. Under this scheme, the payback period for shared energy storage is 5.32 years.

[0157] This invention proposes a shared energy storage configuration method that considers the quasi-linear demand response on the user side. It analyzes the operation and cost-sharing processes of a centralized shared energy storage system, leading to the following conclusions:

[0158] 1) Based on the concept of load baselines, a baseline-based demand response strategy is presented, along with a baseline formation model and incentive subsidy strategy based on a performance-based incentive mechanism. This enables energy storage system users to jointly discover value and maximize their benefits. Compared to baseline-based demand response, baseline-based demand response achieves better load tracking and has a more significant load shaping effect.

[0159] 2) To incentivize user participation in quasi-linear demand response, a performance-based incentive mechanism was adopted to provide income subsidies. With the participation of shared energy storage, user similarity based on quasi-linear demand response increased by 8.59% and 0.99%, respectively, effectively improving the system's ability to absorb new energy sources.

[0160] 3) A market clearing mechanism based on VCG is proposed to meet the incentive requirements of individual rationality, incentive compatibility, and maximization of social welfare in market clearing, and to shorten the payback period of the shared energy storage system by 33.33%. User 1's net expenditure decreased by 143%, User 2's net expenditure decreased by 35%, and User 3's net expenditure decreased by 304%.

Claims

1. A method for optimal allocation of shared energy storage based on load quasi-linear demand response, characterized in that, The method includes constructing a user-side shared energy storage operation model; under this model, users meet their electricity needs by using their own wind and solar power generation, purchasing electricity from shared energy storage, and purchasing electricity from the grid; and achieve electricity consumption through their own load absorption and selling surplus electricity to shared energy storage; the shared energy storage refers to charging, purchasing electricity from the grid, and purchasing surplus wind and solar power from users; Discharging electricity and selling it to users to provide electrical energy services; This method includes the following steps for optimizing the configuration of shared energy storage: (1) Based on the tracking accuracy between the actual load curve and the load baseline, participants with outstanding performance will be given tiered rewards, specifically: Let the benchmark incentive price be p. m The benefit of user i participating in the quasi-linear demand response is defined as follows: I CDL,i =p m ·P D ·ε i Among them, P D The total adjustable power capacity declared by the user up to the date of this report, ε i It is a similarity index used to describe the similarity between the user's actual load pattern and the adjustment target; (2) Construct a shared energy storage configuration model based on maximizing social welfare. The shared energy storage configuration takes into account the economics of both investment and operation, which are composed of user revenue and shared energy storage costs, respectively. The overall objective function is to maximize social welfare, expressed as: W all =max(I user -C ses ) In the formula, user revenue I user With shared energy storage cost C ess Maximizing the difference corresponds to social welfare W all Optimal; (3) Establish a VCG-based resource allocation mechanism to encourage shared energy storage investors to report their actual operation and maintenance costs during operation, and to incentivize users of energy storage charging and discharging services to truthfully report their energy consumption revenue. This includes the following two stages: (3.1) Stage 1: The value of market participants is reflected in their substitution benefits for other participants and their contribution to the overall benefits of the power system. The value of shared energy storage is reflected in the change of total social welfare of the market before and after participation in the market. The value of a user load is also measured by the change of total social welfare of the market before and after participation in the market. make For the social welfare of all members participating in the system market, The expression for the payment received by shared energy storage, excluding the social welfare of the new market for shared energy storage systems, is as follows: make For the social welfare of the new system market that does not include user load i, the payment received by user i is: (3.2) Second stage: Consider the operating costs and revenues of shared energy storage and users to calculate the initial revenue, and calculate the initial investment cost of the shared energy storage power station. Introduce a revenue redistribution factor to redistribute the initial cost, thereby obtaining the final revenue of users and shared energy storage, the expression of which is as follows: In the formula, To share the benefits of the energy storage system; Payment for user i; β This is a profit redistribution factor.

2. The shared energy storage optimization configuration method based on load quasi-linear demand response according to claim 1, characterized in that, The similarity ε between the user's actual load pattern and the adjustment target is used in step (1). i The calculation is as follows: In the formula: d i Used to describe the Euclidean distance between the actual load line and the load baseline; ε0 is the similarity standard value for determining whether a demand response reward is obtained; This represents the actual load pattern of user i's flexible load. P represents the unadjustable load of user i during time period t. D i The total adjustable power declared by the i-th user before the current date; The shape of the load guideline after standardization is calculated as follows: in Let be the total load at time t; in addition, the load profile is subject to the following constraints: In the formula, where For the amount of electricity purchased, The actual output of new energy sources during period t. Let t be the predicted power generation from wind and solar power within the system during time period t. This represents the maximum amount of electricity that can be purchased.

3. The method for optimized allocation of shared energy storage based on load-linear demand response according to claim 1 or 2, characterized in that, Considering the defined similarity ε i Since it's a piecewise function, if the similarity doesn't reach the set threshold, the problem will eventually transform into a linear programming problem; once the similarity reaches or exceeds the threshold, a variable C needs to be introduced for calculation. In the formula, d is transformed into the form of L2 norm. The corresponding term in the objective function of the benefit after user i participates in the quasi-linear demand response is replaced with a new variable C. The above formula is then transformed into an inequality and added as a constraint, thus obtaining a new formulation of the optimization problem: I CDL,i =p m P D -C 4. The method for optimized allocation of shared energy storage based on load quasi-linear demand response according to claim 1 or 2, characterized in that, Step (2) includes constructing a user revenue model and a shared energy storage cost model, as detailed below: (2.1) The user revenue model has the following objective function: The total user revenue consists of the demand response revenue of each user, the reduced cost of curtailment penalties, the reduced cost of purchasing electricity from the grid, and the revenue from selling curtailed electricity to shared energy storage; where λ ab1 For the penalty fee for abandoning electricity, and These represent the amount of electricity abandoned by the i-th user during time period t when there is no energy storage and when there is energy storage, respectively. and P g t ,i Let λ represent the electricity purchased by the i-th user during time period t without energy storage and with energy storage, respectively; ab2 The price of electricity sold off as waste power. This represents the amount of electricity abandoned by the i-th user during time period t. Regarding user collaborative operation, it can avoid simultaneous power curtailment and power purchase from the grid, power balance, the scope of renewable energy power use, the range of charging and discharging power, and the constraints of avoiding simultaneous charging and discharging and only being able to purchase power from the grid. The constraints are as follows: In the formula: and These represent the load and the output of the new energy source for the i-th user during time period t, respectively. and These are Boolean variables that represent the status of users abandoning or purchasing electricity, respectively. If demand exceeds supply, users need to purchase electricity; otherwise, they abandon electricity. and Let represent the charging amount and discharging amount of the i-th user during time period t, respectively; The upper limit of new energy output allocated to the i-th user during time period t; The maximum charging and discharging power for the i-th user; and These represent the user's charging and discharging status bits, which are 0-1 variables; (2.2) Shared energy storage cost model, the objective function of which is: The total cost of shared energy storage consists of the cost of purchasing abandoned electricity from users, the cost of purchasing electricity from the grid, and the operation and maintenance costs; among which, N represents the amount of electricity purchased from the grid for shared energy storage during time period t; om Shared energy storage daily operation and maintenance rate; and These represent the charging and discharging amounts of the shared energy storage during time period t, respectively. During the collaborative operation between users and shared energy storage power stations, the specific constraints that need to be ensured are as follows: In the formula, This represents the total charging amount, including both user-side charging and electricity purchased from the grid using shared energy storage. Maximum charging and discharging power for shared energy storage; and These represent the status bits for charging and discharging of shared energy storage, respectively. η represents the capacity state of the shared energy storage during time period t; η represents the charging and discharging efficiency of the shared energy storage. and These represent the upper and lower limits of the shared energy storage capacity, respectively.

5. A shared energy storage optimization configuration method system based on load quasi-linear demand response, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method of any one of claims 1-4.

6. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the method described in any one of claims 1-4.

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