Shared energy storage multi-mode optimization configuration method based on master-slave game pricing

Abstract

A shared energy storage multi-mode optimal configuration method based on master-slave game pricing belongs to the technical field of configuration under shared energy storage tracking new energy output, and comprises the following steps: firstly, constructing a regional power grid system containing shared energy storage, and designing two sharing modes of electric quantity sharing and capacity sharing; secondly, in order to reduce the influence of the wind and light output uncertainty on the configuration model, a large number of scenes are generated by adopting a stochastic optimization method, and the required scenes are obtained through reduction; and then, based on a master-slave game pricing strategy, establishing a random optimization configuration model which takes a shared energy storage operator as a master, takes a new energy station as a slave and maximizes the net profit of each main body, and solving an optimal energy storage service price and optimal configuration through the game of both parties. According to the invention, benefits of both parties can be increased, the energy storage configuration amount can be reduced, and uncertainty of new energy output can be well dealt with; and due to the two sharing modes, the electric quantity sharing is more economical, and the capacity sharing investment return period is shorter. The method has the advantages of scientificity, reasonability, high applicability, good effect and the like.

Description

Technical Field

[0001] The present invention belongs to the technical field of new energy storage tracking and output configuration, and in particular relates to a shared energy storage multi-mode optimization configuration method based on master-slave game pricing. Background Art

[0002] In order to reduce carbon emissions, it is urgent to build a new power system based on new energy sources such as wind and light. However, the uncertainty and intermittent characteristics of new energy power generation and the problems brought by large-scale new energy grid connection to the power system are becoming more and more prominent, and more flexible resources are urgently needed to support the stable operation of the power grid. Energy storage has great advantages in tracking new energy power generation plans and participating in the peak-valley arbitrage market due to its high control accuracy and fast response speed. However, the current energy storage has disadvantages such as low utilization rate, single energy storage pricing and revenue model, and difficulty in recovering costs, which increases the challenge of promoting the reasonable allocation of energy storage. Therefore, how to take into account the benefits of both parties of shared energy storage at new energy sites and the pricing mechanism of shared energy storage, and realize the reasonable allocation of shared energy storage, is of great significance to promoting the consumption of new energy, the benefits of energy storage and the development of shared energy storage. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a shared energy storage multi-mode optimization configuration method based on master-slave game pricing, overcome the shortcomings of the existing technology, and provide a scientific and reasonable method with strong applicability, which can not only reduce the impact of problems such as single energy storage operation pricing and uncertainty of new energy output on the configuration, but also increase the benefits of both new energy stations and shared energy storage, and finally realize the optimized configuration of multi-mode shared energy storage at new energy stations.

[0004] A method for optimizing the configuration of shared energy storage multi-modes based on master-slave game pricing includes the following steps, which are performed in sequence:

[0005] Step 1: Construct a multi-mode shared energy storage master-slave game pricing operation mode. Under the shared energy storage master-slave game pricing operation framework, formulate a new energy site shared energy storage master-slave game pricing operation strategy, including two shared energy storage service sharing modes;

[0006] Step 2: Construct a shared energy storage optimization configuration model for new energy stations, formulate an output method for new energy stations with uncertain output, determine the output scenario of new energy stations for model optimization calculation, and the model includes a shared energy storage configuration model and a new energy station operation model;

[0007] Step 3: Establish the constraints of the shared energy storage configuration model and the new energy station operation model, use the improved particle swarm algorithm with adjusted learning factors and adaptive weights to solve the shared energy storage optimization configuration model of the new energy station, and embed the new energy station operation model into the improved particle swarm algorithm, and use the mature solver CPLEX to solve it.

[0008] The two shared energy storage service sharing modes include an electricity sharing pricing mode and a capacity sharing pricing mode.

[0009] The method for formulating the uncertainty output of new energy stations is as follows: using a scenario analysis method, firstly based on probability distribution constraints, Latin hypercube sampling method is used to generate wind and photovoltaic output scenarios that meet the requirements; secondly, the generated large-scale scenarios are reduced by using a synchronous back-substitution elimination algorithm based on Kantorovich distance, and a small number of typical scenarios that can restore the random variable characteristics of the original scenarios are extracted to participate in model optimization calculations.

[0010] The objective function of the shared energy storage configuration model is:

[0011]

[0012] Where: C seso is the net profit of the shared energy storage operator in one year; k represents the scenario; K represents the number of selected scenarios; π k is the probability of each scenario; D is the number of operating days per year; Annual fees collected by shared energy storage operators for charging and discharging new energy stations; To utilize the peak-valley electricity price difference to obtain the revenue from electricity purchase and sale in scenario k; is the lifetime loss cost of shared energy storage in the kth scenario; is the penalty cost for the shared energy storage failing to meet the shared energy storage operator’s needs in scenario k; The annualized investment and construction cost for shared energy storage;

[0013] The objective function of the new energy station operation model is:

[0014]

[0015] Where: C repp,i is the total net income of new energy station i in one year; The revenue from online electricity sales of new energy station i in scenario k; Penalty for deviation in access to the Internet for new energy station i; One year's service fee for purchasing shared energy storage services for new energy station i.

[0016] The constraints of the shared energy storage configuration model and the new energy station operation model include master-slave game pricing operation constraints, shared energy storage service usage power balance constraints, shared energy storage charging and discharging power constraints, and shared energy storage capacity constraints.

[0017] Through the above-mentioned design scheme, the present invention can bring the following beneficial effects: a multi-mode optimization configuration method for shared energy storage based on master-slave game pricing. First, a regional power grid system containing shared energy storage is constructed, and two sharing modes of power sharing and capacity sharing are designed. Secondly, in order to reduce the impact of the uncertainty of wind and solar power output on the configuration model, a random optimization method is used to generate a large number of scenarios and reduce them to obtain the required scenarios. Then, based on the master-slave game pricing strategy, a random optimization configuration model is established with the shared energy storage operator as the leader and the new energy station as the follower, and the net profit of each subject is maximized. The optimal energy storage service price and the optimal configuration are solved through the game between the two parties. The configuration method proposed in the present invention can increase the benefits of both parties, reduce the amount of energy storage configuration, and deal with the uncertainty of new energy output well; in the two sharing modes, power sharing is more economical, and the investment payback period of capacity sharing is shorter. It has the advantages of scientific rationality, strong applicability, and good effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The present invention is further described below with reference to the accompanying drawings and specific embodiments:

[0019] Figure 1 Schematic diagram of a regional power grid with shared energy storage constructed for the present invention.

[0020] Figure 2 Schematic diagram of the master-slave game pricing optimization model for shared energy storage in new energy stations of the present invention.

[0021] Figure 3 This is a schematic diagram of the total power demand of new energy stations on a daily basis, pricing of shared energy storage operators, and energy storage operation status in the same scenario of a specific implementation method of the present invention.

[0022] Figure 4 This is a comparison chart of the actual grid-connected status of new energy stations according to a specific implementation method of the present invention. DETAILED DESCRIPTION

[0023] The following is a further explanation of the method for optimizing configuration of shared energy storage at new energy stations of the present invention using drawings and embodiments.

[0024] Reference Figure 1 and Figure 2 The present invention proposes a shared energy storage multi-mode optimization configuration method based on master-slave game pricing, including the following contents:

[0025] 1) Construct a multi-mode shared energy storage master-slave game pricing operation mode

[0026] ① Formulate a pricing and operation framework for shared energy storage master-slave game;

[0027] ② Construct a master-slave game pricing operation strategy for shared energy storage at new energy stations. Shared energy storage operators formulate sharing modes and service prices. New energy stations use the modes and prices formulated by shared energy storage operators to determine their own energy storage service fees and energy storage demand. Shared energy storage operators have formulated two shared energy storage service sharing modes:

[0028] (a) Power Sharing Pricing Model

[0029] Shared energy storage operators provide charging and discharging services based on the real-time power demand of new energy stations, and charge fees in return, implementing a "pay as much as you use, get as much as you charge" model.

[0030]

[0031] Where: The fee paid by the shared energy storage operator to the new energy station i in the power sharing mode; T is the number of time periods in each scenario; Δt is the time interval; i represents the new energy station; The charging and discharging service electricity price specified by the shared energy storage operator at time t in scenario k; are the power requirements of new energy station i at time t in the kth scenario.

[0032] (b) Capacity Sharing Pricing Model

[0033] Shared energy storage operators set the annual power and capacity rental prices for their own energy storage. New energy stations can rent a certain amount of power capacity each year based on their own needs, without being restricted by the number of charging and discharging times.

[0034]

[0035] Where: The fees paid by the new energy station i under the capacity leasing sharing model provided to the shared energy storage operator; are the power and capacity rental unit prices set by shared energy storage operators; P cap,i 、E cap,i They are respectively the power value and capacity value leased by new energy station i.

[0036] 2) Constructing a shared energy storage optimization configuration model for new energy stations

[0037] ① Formulate output uncertainty methods for new energy stations; adopt scenario analysis methods, firstly, based on probability distribution constraints, use Latin hypercube sampling to generate a large number of wind and photovoltaic output scenarios that meet the requirements. Secondly, use the synchronous back-substitution elimination algorithm based on Kantorovich distance to reduce the generated large-scale scenarios, and extract a small number of typical scenarios that can restore the random variable characteristics of the original scenarios to participate in model optimization calculations.

[0038] ② Construct a shared energy storage configuration model; the decision variables include the service price and charging and discharging power strategy set by the shared energy storage operator, configure the power / capacity of the energy storage, and aim to maximize the net profit over its entire life cycle.

[0039] ③ Construct a new energy station operation model; this patent considers multiple new energy stations (including several wind farms and photovoltaic power stations) purchasing shared energy storage services, each of which is an independent entity. By using shared energy storage services instead of self-built energy storage at the station, method ① is used to reduce the impact of output uncertainty, with the goal of maximizing the annual net profit of the new energy station.

[0040] 3) Optimize the configuration model objective function

[0041] (a) Shared energy storage objective function

[0042]

[0043] Where: C seso is the net profit of the shared energy storage operator in one year; k represents the scenario; K represents the number of scenarios selected in this paper; π k is the probability of each scenario; D is the number of operating days per year; Annual fees collected by shared energy storage operators for charging and discharging new energy stations; To utilize the peak-valley electricity price difference to obtain the revenue from electricity purchase and sale in scenario k; is the lifetime loss cost of shared energy storage in the kth scenario; is the penalty cost for the shared energy storage failing to meet the shared energy storage operator’s needs in scenario k; The annualized investment and construction cost of shared energy storage.

[0044] (b) New energy station objective function

[0045]

[0046] Where: C repp,i is the total net income of new energy station i in one year; The revenue from online electricity sales of new energy station i in scenario k; Penalty for deviation in access to the Internet for new energy station i; One year's service fee for purchasing shared energy storage services for new energy station i.

[0047] 4) Constraints

[0048] (a) Master-slave game pricing operation constraints

[0049] In order to ensure that both parties maximize their profits in the power sharing mode and capacity sharing mode and avoid malicious price increases, upper and lower limits of the service unit price are set.

[0050]

[0051] Where: and They are the upper and lower limits of the charging and discharging service prices in the power sharing mode respectively; They are the upper limits of rental power and capacity price set in the capacity sharing mode.

[0052] (b) Power balance constraints for shared energy storage service usage

[0053] In scenario k, the shared energy storage operator needs to meet the total charging and discharging power requirements reported by all new energy sites, that is, the power balance constraint.

[0054]

[0055] Where: They are the charging and discharging powers that the shared energy storage needs to meet the total power demand of the new energy station in the kth scenario.

[0056] (c) Shared energy storage charging and discharging power constraints

[0057]

[0058] Where: are the total charging and discharging power of the shared energy storage at time t in the kth scenario; They are respectively the charging and discharging power at time t in the kth scenario when the shared energy storage power station uses the remaining capacity to participate in peak-valley arbitrage profits after responding to the demand of the new energy station. It indicates that the energy storage is constrained by its own power capacity and cannot meet the power demand of REPP. The optimization result must be 0. If it is not 0, SESO will meet the REPP demand by increasing the configuration or increasing the unit price of energy storage service.

[0059] (d) Shared energy storage capacity constraints

[0060] The capacity of shared energy storage at each moment in scenario k is subject to charging, discharging and configuration capacity constraints;

[0061]

[0062] Where: E seso,k,t+1 、E seso,k,t are the real-time capacities of the shared energy storage at time t+1 and time t in the kth scenario respectively; η c , η dare the charging and discharging efficiencies of the shared energy storage, respectively. The SOC of the energy storage and its constraints are obtained based on the capacity.

[0063]

[0064] Where: SOC k,t Represents the real-time SOC of shared energy storage; SOC min , SOC max are the upper and lower limits of the SOC of the shared energy storage; SOC k,0 , SOC k,T It means that the SOC of the shared energy storage at the initial time of the day scheduling in scenario k must be equal to the SOC at the final time.

[0065] 5) Solution method

[0066] The traditional particle swarm optimization algorithm (PSO) has the disadvantages of slow convergence speed and easy to fall into local optimal solution. This paper solves the shared energy storage optimization configuration model of renewable energy stations by using an improved particle swarm algorithm with adjusted learning factors and adaptive weights, embeds the renewable energy station model into the improved particle swarm algorithm, and uses a mature solver (CPLEX) to solve it.

[0067] 6) Examples

[0068] The example uses the output power of a regional power grid of a 220 kV new energy collection station in Hami, Xinjiang, as the data source, including a 100MW photovoltaic power station and two 200MW wind farms. This paper adopts electrochemical energy storage, built at a large-scale new energy station collection station, serving three new energy stations and participating in the peak-valley arbitrage market. The energy storage parameters are shown in Table 1.

[0069] Table 1 Centralized shared energy storage parameters

[0070]

[0071]

[0072] Table 2 shows the configuration results of this configuration method. Compared with capacity sharing, the net income of electricity sharing is increased by 10.60%, but the configuration volume is also increased by 19MW / 15MW·h. From the perspective of energy storage service fee income, the electricity sharing fee is 12.827 million yuan higher per year.

[0073] Table 2 SESO configuration results for examples 3 and 4

[0074]

[0075] Combination Figure 3 and Figure 4The operation of the present invention in the actual example can be obtained, from which the impact of the two sharing modes on the operation and benefits of both parties can be seen. The shared energy storage optimization configuration of the master-slave game pricing strategy has a significant improvement in the benefits and configuration volume of both parties compared to self-built energy storage. In the power sharing mode, both parties have higher benefits, but the configuration volume is also higher; in the capacity sharing mode, the configuration volume is low and the benefits are also reduced. For new energy sites such as photovoltaic power stations with fixed charging and discharging times and small energy storage requirements and dispersed demand, it is better to choose the power sharing mode; for wind farms with large demand and all-day demand, try to choose capacity sharing. For shared energy storage operators, the power sharing mode has high benefits, but the corresponding investment payback period is also longer, and they can only benefit from the needs of new energy sites, which has limitations; in the capacity sharing mode, although the energy storage benefits are reduced, capacity leasing often signs long-term annual contracts, and the investment payback period is short.

[0076] Through the above analysis of the present invention:

[0077] (1) The invention proposes to build a shared energy storage power station at multiple renewable energy stations, which solves the problems of high cost, low utilization rate and complicated operation and maintenance of self-built energy storage. It not only reduces the amount of energy storage configuration, but also improves the consumption of renewable energy and increases the income of renewable energy stations.

[0078] (2) The master-slave game pricing strategy proposed in the present invention allows shared energy storage operators to set reasonable energy storage service prices for new energy stations during configuration planning and operation optimization. This not only increases the revenue of shared energy storage operators and reduces energy storage configuration requirements, but also increases the economic benefits of new energy stations, thereby promoting the configuration planning of shared energy storage in the power grid.

[0079] (3) The present invention has developed two sharing modes for new energy stations, providing multiple ideas for the planning and operation of energy storage. Under the premise of only considering the maximization of benefits for both parties, the power sharing mode is better, and the net income of new energy stations is increased by 10.60% compared with capacity sharing, and the total net income of new energy stations is increased by 2.71%; under the premise of considering the shortest investment return period, the capacity sharing mode is more advantageous, and the reporting cycle is shortened by nearly two years. In addition, the capacity sharing mode often signs long-term contracts, which will pay back faster and has more advantages in energy storage configuration.

[0080] The calculation conditions, legends, tables, etc. in the embodiments of the present invention are only used to further illustrate the present invention, but are not exhaustive and do not constitute a limitation on the scope of protection of the claims. Based on the inspiration obtained from the embodiments of the present invention, those skilled in the art can think of other substantially equivalent alternatives without creative work, and all of them are within the scope of protection of the present invention.

Claims

1. A multi-mode optimization configuration method for shared energy storage based on master-slave game pricing, characterized by: The process includes the following steps, which are performed in sequence: Step 1: Construct a multi-mode shared energy storage master-slave game pricing operation mode. Under the shared energy storage master-slave game pricing operation framework, formulate a new energy site shared energy storage master-slave game pricing operation strategy, including two shared energy storage service sharing modes; Step 2: Construct a shared energy storage optimization configuration model for new energy stations, formulate an output method for new energy stations with uncertain output, determine the output scenario of new energy stations for model optimization calculation, and the model includes a shared energy storage configuration model and a new energy station operation model; Step 3: Establish the constraints of the shared energy storage configuration model and the new energy station operation model, use the improved particle swarm algorithm with adjusted learning factors and adaptive weights to solve the shared energy storage optimization configuration model of the new energy station, and embed the new energy station operation model into the improved particle swarm algorithm, and use the mature solver CPLEX to solve it.

2. According to claim 1, a shared energy storage multi-mode optimization configuration method based on master-slave game pricing is characterized by: The two shared energy storage service sharing modes include an electricity sharing pricing mode and a capacity sharing pricing mode.

3. The method for optimizing the configuration of shared energy storage multi-modes based on master-slave game pricing according to claim 1 is characterized by: The method for formulating the uncertainty output of new energy stations is as follows: using a scenario analysis method, firstly based on probability distribution constraints, Latin hypercube sampling method is used to generate wind and photovoltaic output scenarios that meet the requirements; secondly, the generated large-scale scenarios are reduced by using a synchronous back-substitution elimination algorithm based on Kantorovich distance, and a small number of typical scenarios that can restore the random variable characteristics of the original scenarios are extracted to participate in model optimization calculations.

4. The method for optimizing the configuration of shared energy storage multi-modes based on master-slave game pricing according to claim 1 is characterized by: The objective function of the shared energy storage configuration model is: Where: C seso is the net profit of the shared energy storage operator in one year; k represents the scenario; K represents the number of selected scenarios; π k is the probability of each scenario; D is the number of operating days per year; Annual fees collected by shared energy storage operators for charging and discharging new energy stations; To utilize the peak-valley electricity price difference to obtain the revenue from electricity purchase and sale in scenario k; is the lifetime loss cost of shared energy storage in the kth scenario; is the penalty cost for the shared energy storage failing to meet the shared energy storage operator’s needs in scenario k; The annualized investment and construction cost for shared energy storage; The objective function of the new energy station operation model is: Where: C repp,i is the total net income of new energy station i in one year; The revenue from online electricity sales of new energy station i in scenario k; Penalty for deviation in access to the Internet for new energy station i; One year's service fee for purchasing shared energy storage services for new energy station i.

5. The method for optimizing the configuration of shared energy storage multi-modes based on master-slave game pricing according to claim 1 is characterized by: The constraints of the shared energy storage configuration model and the new energy station operation model include master-slave game pricing operation constraints, shared energy storage service usage power balance constraints, shared energy storage charging and discharging power constraints, and shared energy storage capacity constraints.

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