Urban rail energy network group collaborative optimization method and system based on hybrid energy storage

Abstract

The invention relates to the technical field of urban rails, in particular to an urban rail energy network group collaborative optimization method and system based on hybrid energy storage, and the method comprises the steps: constructing an energy operator benefit maximization model; constructing a hybrid energy storage power station operation cost minimization model; constructing an optimal operation cost model of the energy network; converting the optimal operation cost model of the energy network and the operation cost minimization model of the hybrid energy storage power station into a single model by adopting a KKT condition, adding an energy network group constraint into the model of the hybrid energy storage power station, and determining an optimal scheduling value of the hybrid energy storage power station; linearization operation is carried out on a transaction price and transaction power product term in the optimal operation cost model of the energy network and the hybrid energy storage power station operation cost minimization model; and constructing a cooperative game model of the energy network, and performing distributed optimization. The problems that an existing method is high in operation cost, and the utilization rate of the hybrid energy storage power station is not high are solved.

Description

Technical Field

[0001] The present invention relates to the field of urban rail technology, and in particular to a method and system for collaborative optimization of an urban rail energy grid based on hybrid energy storage. Background Art

[0002] The "new energy power station + urban rail transit" model is better adapted to the development of renewable energy demand and reduces dependence on traditional fossil fuels; however, the dynamic lighting load of the subway is uncertain, and energy storage equipment is usually required to smooth out the peaks and fill the valleys. However, self-built energy storage devices are costly and difficult to maintain, and it is difficult to achieve large-scale application in the short term.

[0003] In the operation of hybrid energy storage systems in urban rail transit, three different hybrid energy storage operation strategies are often adopted, namely, rule-based operation strategy, optimization-based operation strategy and artificial intelligence-based operation strategy; for the problem of regenerative braking energy recovery and utilization rate of urban rail trains, a train power supply-traction-hybrid energy storage model is generally established to improve the stability and flexibility of power supply; in order to improve the service life of the hybrid energy storage system, some scholars have proposed an operation optimization strategy for hybrid energy storage based on single threshold and multi-threshold, and used a contingency particle swarm algorithm to solve it; in order to solve the problem of traction network voltage fluctuation caused by the start and stop of urban rail transit trains, supercapacitors and batteries are used to form a hybrid energy storage system, and a passive control method is used to solve it. Summary of the Invention

[0004] In response to the shortcomings of existing methods, the present invention solves the existing technology by providing an adaptation technology for the integration of rail transit energy demand and renewable energy in multiple scenarios. From the perspective of the uncertainty of renewable energy and traction load, a two-stage optimization model is established to reduce the system operating cost. However, the existing methods still have the problems of high operating costs and low utilization rate of hybrid energy storage power stations. In addition, the tight power supply and large fluctuations in rail transit power demand cause power supply pressure on the existing rail system.

[0005] The technical solution adopted by the present invention is: a method for collaborative optimization of urban rail energy grid clusters based on hybrid energy storage, comprising the following steps:

[0006] Step 1: Collect the electricity purchase and sales prices and electricity consumption between energy operators and different energy suppliers, and build a profit maximization model for energy operators;

[0007] As a preferred embodiment of the present invention, the energy supplier includes: a distribution network, an energy network and a hybrid energy storage power station.

[0008] As a preferred embodiment of the present invention, upper and lower limits and mean constraints are imposed on the purchase and sale prices of electricity for the energy grid and the hybrid energy storage power station.

[0009] Step 2: Build a hybrid energy storage power station operation cost minimization model using the investment and maintenance costs of the hybrid energy storage power station, the cost of purchasing electricity from energy operators, the service fees charged by the energy grid for using the hybrid energy storage power station, and the cost of charging and discharging the hybrid energy storage power station by the energy grid.

[0010] As a preferred embodiment of the present invention, the constraints on the hybrid energy storage power station operation cost minimization model include: hybrid energy storage power station charge and discharge balance power constraint, hybrid energy storage power station charge and discharge power non-negativity constraint, battery charge and discharge power constraint, and supercapacitor charge and discharge power non-negativity constraint.

[0011] Step 3: Build an optimal operating cost model for the energy grid based on the transaction costs with energy operators, the transaction costs with hybrid energy storage power plants, and the service costs provided to hybrid energy storage power plants.

[0012] As a preferred embodiment of the present invention, the constraints on the optimal operating cost model of the energy grid include: power balance constraints, demand response constraints, transaction imbalance constraints with hybrid energy storage power stations, upper and lower limit constraints on electric power transactions with energy operators, and transaction balance constraints between grid groups.

[0013] Step 4: Use the KKT condition to transform the optimal operating cost model of the energy grid and the hybrid energy storage power station operating cost minimization model into a single model, add the energy grid group constraint to the hybrid energy storage power station model, and determine the optimal dispatch value of the hybrid energy storage power station;

[0014] As a preferred embodiment of the present invention, the optimal dispatch value of the hybrid energy storage power station includes:

[0015] Constructing a Lagrangian model of energy grid clusters;

[0016] Solve KKT conditions for energy grid groups;

[0017] Boolean variables and positive numbers M are introduced to perform complementary relaxation on the Boolean variables of the Lagrangian model of the energy grid group.

[0018] As a preferred embodiment of the present invention, the formula of the Lagrangian model of the energy grid group is:

[0019]

[0020] in, is a Boolean variable, is the renewable energy output value, To respond to the demand of dynamic load, is the predicted load value of the dynamic image, P ij,t is the trading volume between energy sources, The upper limit of the amount of electricity that the energy grid can purchase from energy operators. Selling electricity to hybrid energy storage power stations for energy grids, Selling electricity to hybrid energy storage power stations for energy grids, The upper limit of the amount of electricity that the energy grid can sell to energy operators. The upper limit of the amount of electricity that the energy grid can purchase from the hybrid energy storage power station, The upper limit of the amount of electricity that the energy grid can sell to the hybrid energy storage power station, is the lower limit of dynamic load demand response, is the upper limit of dynamic load demand response, P SE,c,max is the upper limit of the discharge power of the hybrid energy storage power station, P SE,d,max is the upper limit of the charging power of the hybrid energy storage station, P ij,t The amount of electricity traded between energy grids, is the upper limit of the amount of electricity that can be traded between energy grids. It is the lower limit of electricity volume traded between energy grids.

[0021] Step 5: Linearize the product of transaction price and transaction power in the optimal operating cost model of the energy grid and the hybrid energy storage power station operating cost minimization model;

[0022] As a preferred embodiment of the present invention, the McCormick convex envelope is used to perform the linearization operation.

[0023] Step 6: Build a cooperative game model for the energy network and perform distributed optimization;

[0024] As a preferred embodiment of the present invention, distributed optimization includes:

[0025] Step 61: construct an augmented Lagrangian function model;

[0026] Step 62: Initialize the dual variables and penalty factors, set the first transaction price and the second transaction price between energy networks, and the number of iterations;

[0027] Step 63: Obtain a first transaction price based on the augmented Lagrangian function model, and send the first transaction price to the energy network control center that needs to trade;

[0028] Step 64: After receiving the first transaction price, the energy network control center that needs to trade obtains a second transaction price based on the augmented Lagrangian function model, and sends the second transaction price back to the energy network control center.

[0029] Step 65: Update the dual variables and penalty factors;

[0030] Step 66: Determine whether the loop has converged based on the first transaction price and the second transaction price; if so, output the result; otherwise, continue to execute step 63.

[0031] As a preferred embodiment of the present invention, a hybrid energy storage-based urban rail energy grid cluster collaborative optimization system includes: a plurality of energy grid power generation units, a hybrid energy storage power station coordination unit, an energy operator electricity price setting unit, an energy grid and hybrid energy storage power station price coordination unit, an energy grid cluster control unit, a power generation environment monitoring unit, and an operating cost acquisition unit;

[0032] The energy grid power generation unit is used to generate electricity;

[0033] The hybrid energy storage power station coordination unit is used to obtain the configuration capacity of the storage battery and lithium battery group in the hybrid energy storage power station;

[0034] The energy operator's electricity price setting unit obtains the transaction prices of energy operators, energy grids, and hybrid energy storage power stations;

[0035] The energy grid and hybrid energy storage power station price coordination unit is used for energy grid and hybrid energy storage power station prices;

[0036] The energy grid cluster control unit is used to obtain the optimal scheduling method of the energy grid based on the energy grid cluster control model;

[0037] The power generation environment monitoring unit is used to obtain rail transit dynamic lighting load data and energy consumption data of the energy grid;

[0038] The operation cost acquisition unit is used to obtain the optimal operation cost when the energy network is running.

[0039] Beneficial effects of the present invention:

[0040] 1. Based on the demand for energy and electricity, the present invention allows energy operators to formulate real-time electricity purchase and sale prices with hybrid energy storage power stations and energy grid groups, and timely adjust the energy utilization strategies of the energy grid groups and hybrid energy storage power stations, thus forming a leader-follower relationship and giving full play to the leading role of energy operators.

[0041] 2. This invention innovatively constructs a Lagrangian function model for energy grid clusters and hybrid energy storage power stations. It also introduces KKT conditions to address the difficulty of solving the double-layer model, converting the double-layer model into a single-layer optimization model.

[0042] 3. The hybrid energy storage power station of the present invention is composed of batteries and supercapacitors. According to the different operating characteristics of the two, a hybrid energy storage control strategy is formulated; the batteries are charged during low load periods and discharged during peak load periods; the supercapacitors quickly adjust the power generation capacity of the energy grid group when renewable energy fluctuates, thereby improving the peak-shaving and smoothing effects of the overall system.

[0043] 4. Since the transaction price and transaction power between the hybrid energy storage power station model and the energy grid cluster model are bidirectional nonlinear terms, the McCormick convex envelope technology is used for reconstruction. The reconstructed model greatly reduces the bilinear complexity of the classic model and improves the system's operating accuracy and speed.

[0044] 5. Through cooperative game theory, energy grid groups can operate cooperatively, and hybrid energy storage power stations can coordinate and control resources, which can increase the total revenue of the energy grid group. Individual energy grids can share power resources through cooperation and reduce cost risks, thereby bringing greater profits than operating alone, achieving a win-win situation in cooperative games. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the collaborative optimization method for urban rail energy grid based on hybrid energy storage of the present invention;

[0046] Figure 2 This is a schematic diagram of the hybrid energy storage urban rail energy grid collaborative optimization system of the present invention;

[0047] Figure 3 It is the price at which energy operators and energy grid groups purchase and sell electricity;

[0048] Figure 4 It is the price at which energy operators and hybrid energy storage power plants purchase and sell electricity;

[0049] Figure 5 is the dispatch result of energy network 1;

[0050] Figure 6 is the dispatch result of energy network 2;

[0051] Figure 7 is the dispatch result of energy network 3;

[0052] Figure 8 It is the charging state of the hybrid energy storage power station;

[0053] Figure 9 It is the hybrid energy storage discharge state;

[0054] Figure 10 The electricity price set for the hybrid energy storage power station to the energy network 1;

[0055] Figure 11 The electricity price set for the hybrid energy storage power station to Energy Network 2;

[0056] Figure 12 The electricity price set for the hybrid energy storage power station to Energy Network 3;

[0057] Figure 13 It is the transaction price of electricity between energy grids. DETAILED DESCRIPTION

[0058] The present invention will be further described below in conjunction with the accompanying drawings and embodiments. This figure is a simplified schematic diagram, which only illustrates the basic structure of the present invention in a schematic manner, and therefore only shows the components related to the present invention.

[0059] The urban rail transit energy network is a power supply system that provides the necessary power supply for urban rail transit operations, providing dynamic and lighting loads for electric trains. The dynamic load includes traction power, and lighting loads such as lighting, ventilation, air conditioning, water supply and drainage, communications, signals, disaster prevention and alarm, escalators, etc. provide electricity. The power supply system should be safe and reliable, technologically advanced, fully functional, easy to dispatch, and economically reasonable. Interruptions in the power supply system will lead to the paralysis of the urban rail transit system, endangering the lives and property of passengers. Therefore, power supply is an important guarantee for the normal operation of urban rail transit.

[0060] With the rapid development of urban rail transit, the power grid is facing the challenge of tight energy supply. The surge in passenger flow during peak hours has led to a continuous increase in demand for electricity, while the existing power supply capacity and the speed of infrastructure construction have failed to fully keep up with this growth trend. This not only affects the operational efficiency of rail transit, but also puts pressure on the overall traffic order in the city. Therefore, strengthening the planning and construction of the power grid and improving its energy supply capacity have become important issues that need to be urgently addressed.

[0061] In order to solve the above-mentioned problem of power shortage in urban rail transit, the present invention takes into account the dynamic lighting load of rail transit, combines the energy grid, hybrid energy storage power station and energy operators, considers the electricity prices set by energy operators, and guides the optimal operation control of the energy grid group.

[0062] like Figure 1 As shown, a method for collaborative optimization of urban rail energy grid clusters based on hybrid energy storage includes the following steps:

[0063] Step 1: Collect the electricity purchase and sales prices and electricity consumption between energy operators and different energy suppliers, and build a profit maximization model for energy operators;

[0064] Energy suppliers include: distribution network, energy grid and hybrid energy storage power station; distribution network, energy grid, energy operators and hybrid energy storage power station communicate with each other.

[0065] Obtain the transaction prices of energy operators, energy grids, and hybrid energy storage power stations based on the electricity purchase and sales prices of energy operators and distribution grids and the operating parameters of each device in the energy grid;

[0066] Rail transit dynamic load data is obtained from all equipment in the energy grid group on consecutive dates. Regional environmental parameters include the output value of each energy grid, inter-grid transaction status, and transaction prices between distribution networks and energy operators.

[0067] It is understandable that the premise for energy operators to set prices is based on the power generation and dynamic load demand of each energy grid; after obtaining the power trading volume between energy operators and energy grid groups, and considering the trading volume between hybrid energy storage power stations and energy operators, the trading price between hybrid energy storage power stations and energy operators is determined, thereby obtaining the specific trading method.

[0068] Prioritize establishing a profit maximization model for energy operators, the formula is:

[0069]

[0070] in, The price of electricity sold between energy operators and distribution networks; The electricity purchase price between energy operators and distribution networks; The amount of electricity sold between energy operators and distribution networks; The amount of electricity purchased between energy operators and distribution networks; The price of electricity purchased between energy operators and energy grids; The price of electricity sold between energy operators and energy grids; The amount of electricity purchased between energy operators and energy grids; The amount of electricity sold between energy operators and energy grids; The amount of electricity purchased between energy operators and hybrid energy storage power stations; is the electricity sold between energy operators and hybrid energy storage power stations; M is the number of energy networks; max U EO Benefits for the largest energy operators.

[0071] At this time, the energy transaction price generated by each energy grid in each time period is determined based on the transaction power between each energy grid and the energy operator. Similarly, under normal circumstances, it is necessary to control the transaction costs between energy operators and hybrid energy storage power stations during electricity trading, thereby reducing the operating costs generated by the entire power system and maximizing the allocation of resources.

[0072] In order to maximize the benefits for energy operators and reduce the waste of resource costs, the electricity trading price must meet the following constraints:

[0073] The upper limit constraint on the purchase and sale price of power between the energy grid and energy operators; the time-of-use purchase and sale price of power set by energy operators should not exceed the initial power sale price constraint; and the upper limit constraint on the purchase and sale price of power between hybrid energy storage power stations and energy operators;

[0074] The upper limit constraint on the price of electricity purchased and sold between the energy grid and energy operators is to limit the price of electricity purchased and sold between the energy grid and energy operators, prevent service providers from excessively raising or lowering prices, and ensure that energy transaction prices are within the acceptable range of the energy grid;

[0075] The time-of-use prices for purchasing and selling electricity set by energy operators should not exceed the initial electricity sales price. This allows energy operators to set reasonable prices for the energy grid and hybrid energy storage power plants respectively. This helps prevent large energy operators from exploiting their dominant market position to engage in unfair competition and ensures that the energy grid and hybrid energy storage power plants can survive and develop in the market.

[0076] The upper limit constraint on the price of electricity purchased and sold between the hybrid energy storage power station and the energy operator is to limit the price of electricity purchased and sold between the hybrid energy storage power station and the energy operator to ensure the resource return of the hybrid energy storage power station;

[0077] Among them, the formula for the power purchase price constraint between the energy grid and the energy operator is:

[0078]

[0079] in, It is the upper limit of the price of power purchased between the energy grid and energy operators. It is the lower limit of the power purchase price between the energy grid and energy operators. The price of electricity purchased between the energy grid and energy operators;

[0080] The formula for the power price constraint between the energy grid and energy operators is:

[0081]

[0082] in, It is the upper limit of the price of electricity sold between the energy grid and energy operators. It is the lower limit of the power price sold between the energy grid and energy operators. The price of electricity sold between the energy grid and energy operators.

[0083] The formula for the power purchase price constraint between the hybrid energy storage power station and the energy operator is:

[0084]

[0085] in, It is the upper limit of the power purchase price between hybrid energy storage power stations and energy operators. It is the lower limit of the power purchase price between the hybrid energy storage power station and the energy operator. The price of electricity purchased between the hybrid energy storage power station and the energy operator;

[0086] The formula for the power price constraint between the hybrid energy storage power station and the energy operator is:

[0087]

[0088] in, The upper limit of the power price sold between the hybrid energy storage power station and the energy operator, It is the lower limit of the power price sold between the hybrid energy storage power station and the energy operator. The price of electricity sold between the hybrid energy storage power station and the energy operator;

[0089] The formula for the time-of-use price of electricity purchased and sold formulated by energy operators should not exceed the initial electricity selling price constraint is:

[0090]

[0091] in, is the average price of electricity purchased from the energy grid within one day, T = 24 hours;

[0092]

[0093] in, The average price of electricity sold by the energy grid in one day;

[0094]

[0095] in, The average price of electricity purchased from the energy grid within a day;

[0096]

[0097] in, It is the average price of electricity sold by the energy grid in one day.

[0098] Step 2: Build a hybrid energy storage power station operation cost minimization model using the investment and maintenance costs of the hybrid energy storage power station, the cost of purchasing electricity from energy operators, the service fees charged by the energy grid for using the hybrid energy storage power station, and the cost of charging and discharging the hybrid energy storage power station by the energy grid.

[0099] Obtain the configuration capacity of batteries and supercapacitors in the hybrid energy storage power station based on the configuration of the energy grid and actual dynamic load data;

[0100] The energy grid group reasonably rents the hybrid energy storage power station and puts it into use. The hybrid energy storage power station needs to provide the required electricity transactions according to the needs of each energy grid.

[0101] The formula for the hybrid energy storage power station operation cost minimization model is:

[0102]

[0103] Among them, ρ PST_max P is the unit investment cost of the hybrid energy storage power station; ST_max is the maximum charging and discharging power of the hybrid energy storage power station; ρ EST_max is the unit hybrid energy storage power station capacity investment cost; E ST_max is the maximum capacity of the hybrid energy storage power station; ρ MST_max is the maintenance cost per unit electric power of the hybrid energy storage power station; the cost of purchasing electricity from hybrid energy storage plants for the energy grid; Purchase power from hybrid energy storage power stations for the energy grid; the cost of selling electricity from hybrid energy storage plants to the energy grid; Selling power from hybrid energy storage plants to the energy grid; The operating cost of the minimum hybrid energy storage power station.

[0104] It should be noted that the above function is divided into four parts. The formulas on both sides of each plus sign represent one part, which respectively represents the investment and maintenance cost of the hybrid energy storage power station; the cost of purchasing electricity from the energy operator; the service fee for the energy grid to use the hybrid energy storage power station; and the cost of the energy grid using the hybrid energy storage power station for charging and discharging.

[0105] To maintain the coordinated operation of the hybrid energy storage power station and the energy grid, and to ensure that the hybrid energy storage power station has sufficient energy and power support capabilities, the hybrid energy storage power station must meet the following constraints:

[0106] Hybrid energy storage power station charge and discharge balance power constraints, hybrid energy storage power station charge and discharge power non-negativity constraints, battery charge and discharge power constraints, supercapacitor charge and discharge power non-negativity constraints.

[0107] The hybrid energy storage station charge and discharge balance power constraint requires that the charging and discharging energy of the hybrid energy storage station should be balanced at any time during the operation of the hybrid energy storage station to ensure that the system does not overcharge or discharge, which will damage the battery life;

[0108] The non-negativity constraint of the hybrid energy storage power station means that during the operation of the hybrid energy storage power station, the charging power and the discharging power must meet the non-negativity condition to prevent improper operation of the power station during the charging and discharging process;

[0109] Battery charge and discharge power constraints are a series of restrictions that the charge and discharge power must follow during battery operation to ensure that the battery can effectively store and release energy.

[0110] Supercapacitor charge and discharge power constraints are a series of restrictions that the charging and discharging power must follow during the operation of the supercapacitor, ensuring that the supercapacitor can operate safely and stably while providing rapid energy storage and release.

[0111] Among them, the formula for the charge and discharge balance power constraint of the hybrid energy storage power station is:

[0112]

[0113] in, The amount of electricity used to charge the battery, The amount of electricity discharged from the battery, The amount of electricity used to charge the supercapacitor, is the amount of electricity discharged from the supercapacitor, is the charging capacity of the hybrid energy storage power station, is the discharge capacity of the hybrid energy storage power station, The amount of electricity purchased from the hybrid energy storage power station by the energy grid group, The amount of electricity purchased from the hybrid energy storage power station for the energy grid group.

[0114] The power system corresponding to the hybrid energy storage power station can be optimized by constraining the power balance; this can be achieved through the use of battery control systems, supercapacitor control systems, and hybrid energy storage control systems.

[0115] The battery system is used to balance the power differences of the hybrid energy storage power station based on the charge and discharge values ​​of the lead-acid battery.

[0116] The application of supercapacitor control system is to balance the power difference of hybrid energy storage power station according to the charge and discharge value of supercapacitor.

[0117] The hybrid energy storage control system integrates batteries and supercapacitors to optimize the efficiency of energy storage and release, thereby meeting the needs of different load and renewable energy mutation scenarios.

[0118] Among them, the non-negativity constraint of the charging and discharging power of the hybrid energy storage power station is specifically expressed as follows:

[0119]

[0120] Among them, P SE,c,max is the upper limit of the discharge power of the hybrid energy storage power station, P SE,d,max is the upper limit of the charging power of the hybrid energy storage station, P SE,bat,c,max is the upper limit of battery discharge power, P SE,bat,d,max The upper limit of battery charging power, P SE ,sup,c,max is the upper limit of the supercapacitor discharge power, P SE,sup,d,maxThe upper limit of the supercapacitor charging power, is the regulation factor of the battery, is the adjustment factor of the supercapacitor.

[0121] The formula for battery charging and discharging power constraint is:

[0122]

[0123] in, is the capacity of the battery, η SE,bat,c is the discharge efficiency of the battery, η SE,bat,d is the charging efficiency of the battery, is the lower limit of battery capacity, The upper limit of battery capacity.

[0124] Among them, the formula for supercapacitor charging and discharging power constraint is:

[0125]

[0126] in, is the capacity of the supercapacitor, η SE,sup,c is the discharge efficiency of the supercapacitor, η SE,sup,d is the charging efficiency of the supercapacitor, is the lower limit of supercapacitor capacity, is the upper limit of supercapacitor capacity.

[0127] In a hybrid energy storage power station, the charge and discharge values ​​of the battery and supercapacitor are both within the upper and lower limits of the hybrid energy storage power station configuration; the adjustment factor is used to analyze the correlation between the output values ​​of the battery and supercapacitor, and to determine the status of the hybrid energy storage power station based on the output conditions at the current moment; the sum of the adjustment factors is less than 1, and the adjustment factor value range is between 0 and 1.

[0128] By obtaining a hybrid energy storage power station connection connected to the energy grid, batteries and supercapacitors each have different cost, life and performance characteristics. According to the charge and discharge characteristics of the two batteries, the capacity is reasonably configured to smooth out the fluctuations in sunlight load and suppress the volatility of renewable energy.

[0129] Step 3: Build an optimal operating cost model for the energy grid based on the transaction costs with energy operators, the transaction costs with hybrid energy storage power plants, and the service costs provided to hybrid energy storage power plants.

[0130] The hybrid energy storage power station model is used to consider the charging and discharging of batteries and supercapacitors, as well as the power trading volume between energy operators and energy grid groups; while the energy grid group is used to consider the power production and sales within the energy grid and the power trading volume between energy grid groups.

[0131] The optimal operating cost of obtaining an energy grid group includes: the operating cost of obtaining the energy grid group, the cost of trading with energy operators, the cost of trading with hybrid energy storage power stations, and the service cost of providing hybrid energy storage power stations.

[0132] In rail transit, the energy grid group mainly provides power to the dynamic and lighting loads in urban rail transit.

[0133] The cost of transactions with energy operators refers to the cost of purchasing electricity from energy operators, and also eliminates the benefits of selling electricity to energy operators.

[0134] The cost of trading with a hybrid energy storage plant refers to the cost of purchasing electricity from the hybrid energy storage plant, and eliminating the benefits of selling electricity to the hybrid energy storage plant.

[0135] The service cost of the hybrid energy storage power station refers to the cost of renting the hybrid energy storage power station;

[0136] First, we obtain the transaction costs with energy operators, the transaction costs with hybrid energy storage power plants, and the service costs provided to hybrid energy storage power plants, and establish an optimal operating cost model for the energy grid. The formula is:

[0137]

[0138] Among them, p SE It is the unit maintenance cost of the hybrid energy storage power station.

[0139] The function in formula (15) is divided into three parts. The equations on both sides of each plus sign represent one part, which respectively represent the cost of trading with energy operators, the cost of trading with hybrid energy storage power stations, and the service cost of providing hybrid energy storage power stations.

[0140] Among them, the cost of transactions with energy operators is determined based on the transaction volume between the energy grid and energy operators and the price set by the energy operator; the cost of transactions with hybrid energy storage power stations is determined based on the transaction volume between the energy grid and hybrid energy storage power stations and the transaction price; the service cost for hybrid energy storage power stations is determined based on the unit maintenance cost of the hybrid energy storage power stations and the transaction volume with the hybrid energy storage power stations.

[0141] In order to maintain the stability of energy grid operations and reduce waste of resource costs, energy grids need to meet the following constraints: power balance constraints, demand response constraints, transaction imbalance constraints with hybrid energy storage power stations, upper and lower limit constraints on electric power transactions with energy operators, and transaction balance constraints between grids.

[0142] The power balance constraint is that the electric power generated by each energy source and the corresponding dynamic load consumption must be kept stable to maintain the stable operation of the energy grid;

[0143] Demand response constraints are to adjust the power demand of rail transit loads to support the power system in order to balance supply relationships, improve energy efficiency and optimize the operation of the energy grid system;

[0144] The imbalance constraint for transactions with hybrid energy storage power stations is to adjust the demand for hybrid energy storage power stations within the energy grid cluster to ensure that the capacity demand of the hybrid energy storage power stations is not exceeded;

[0145] The upper and lower limits for power transactions with energy operators are to limit the power that can be traded with energy operators, ensuring that the flow of traded power is within an acceptable range.

[0146] The inter-cluster transaction balance constraint is to constrain the output of distributed power sources to ensure that the power of distributed power sources does not exceed their equipment capacity to avoid overload and equipment damage;

[0147] The formula for power balance constraint is:

[0148]

[0149] in, is the renewable energy output value, To respond to the demand of dynamic load, is the predicted load value of the dynamic image, P ij,t The volume of energy transactions.

[0150] In formula (16), the optimization of the power system corresponding to the energy grid is achieved by constraining the power balance; for example, load management, power regulation of energy operators, regulation of energy storage systems, and transactions between energy grids are used to achieve this.

[0151] Load management is a measure to determine the actual dynamic load of the energy network based on the demand response of the dynamic load of the energy network;

[0152] Energy operator power regulation is to adjust the power allocation of energy operators based on the traded electric power between energy operators and energy grid groups to achieve power balance;

[0153] The application of energy storage system regulation is to adjust the transaction power between the energy grid group and the hybrid energy storage power station according to the configuration capacity of the hybrid energy storage power station;

[0154] Transactions between energy grids are to adjust the output within the energy grid according to the demand for electricity between energy grids.

[0155] The specific formula of load demand response constraint is as follows:

[0156]

[0157] in, is the demand response of dynamic load, PDR,min is the lower limit of demand response for dynamic load, P DR,max It is the upper limit of dynamic load demand response.

[0158] The formula for the imbalance constraint on transactions with hybrid energy storage power stations is:

[0159]

[0160] in, Selling electricity to hybrid energy storage power stations for energy grids, To sell electricity to the hybrid energy storage power station for the energy grid, P SE,c,max is the upper limit of the discharge power of the hybrid energy storage power station, P SE,d,max is the upper limit of the charging power of the hybrid energy storage power station; in this formula, the electricity transaction between the energy grid and the hybrid energy storage power station cannot exceed the configured capacity of the hybrid energy storage power station.

[0161] The formula for upper and lower limits of power transactions with energy operators is:

[0162]

[0163] in, The amount of electricity purchased between energy operators and energy grids. The amount of electricity sold between energy operators and energy grids. and is a Boolean variable;

[0164] In the power trading constraints with energy operators, the trading power between the energy grid group and the energy operator cannot exceed the upper limit set by the energy operator, and the purchase and sale of electricity between the energy grid group and the energy operator cannot occur at the same time to ensure the stability of the power lines.

[0165] The transaction balance constraint formula between network groups is as follows:

[0166] P ij,t =P ji,t ,i≠j (23)

[0167] Among them, P ij,t It is the amount of electricity traded between energy grids; ensuring that the electricity sold by one energy grid is consistent with the electricity purchased by other energy grids.

[0168] Through the above multiple constraint methods, the restriction forms of multiple environmental parameters in the energy network are determined when the operating cost is minimized, so as to meet the implementation method of the energy network.

[0169] Step 4: Use KKT conditions to transform the two-layer model into a single-layer optimization model, add the constraints of the energy grid group to the model of the hybrid energy storage power station, and determine the optimal scheduling value of the hybrid energy storage power station;

[0170] In order to better solve the two-level game model of the energy grid group and the hybrid energy storage power station, it is necessary to transform the KKT conditions, add the constraints of the energy grid group to the model of the hybrid energy storage power station, and solve the objective function value of the hybrid energy storage power station;

[0171] First, establish the Lagrangian model of the energy grid cluster, the formula is:

[0172]

[0173] in, is a Boolean variable, is the renewable energy output value, To respond to the demand of dynamic load, is the predicted load value of the dynamic image, P ij,t is the trading volume between energy sources, The upper limit of the amount of electricity that the energy grid can purchase from energy operators. Selling electricity to hybrid energy storage power stations for energy grids, Selling electricity to hybrid energy storage power stations for energy grids, The upper limit of the amount of electricity that the energy grid can sell to energy operators. The upper limit of the amount of electricity that the energy grid can purchase from the hybrid energy storage power station, The upper limit of the amount of electricity that the energy grid can sell to the hybrid energy storage power station, is the lower limit of dynamic load demand response, is the upper limit of dynamic load demand response, P SE,c,max is the upper limit of the discharge power of the hybrid energy storage power station, P SE,d,max is the upper limit of the charging power of the hybrid energy storage station, P ij,t The amount of electricity traded between energy grids, is the upper limit of the amount of electricity that can be traded between energy grids. It is the lower limit of electricity volume traded between energy grids.

[0174] Energy grid clusters and hybrid energy storage power stations are two-layer optimization models, which are difficult to solve. Therefore, the present invention innovatively constructs a Lagrangian function model, introduces KKT conditions, and transforms it into a single-layer optimization model.

[0175] The KKT conditions of the energy grid group are as follows:

[0176]

[0177] By introducing a Boolean variable Complementary relaxation with a large positive number M is performed as follows:

[0178]

[0179] The KKT condition is used to transform the two-layer model of hybrid energy storage power station and energy grid group into a single-layer optimization model.

[0180] Step 5: Use McCormick convex envelope to transform the two-level optimization model to obtain the transaction electricity prices of the hybrid energy storage power station and the energy grid group;

[0181] In order to determine the transaction price of the hybrid energy storage power station and the energy grid group, the bilinear terms in the two-layer objective function of the hybrid energy storage power station and the energy grid group are linearized.

[0182] Specifically, the product of transaction price and transaction power in the two-layer objective function of hybrid energy storage power station and energy grid group is nonlinear, and the McCormick convex envelope technology is used to linearize it.

[0183] The purchase and sale prices of electricity between the energy grid and the hybrid energy storage power station are constrained by the formula:

[0184]

[0185] in, The upper limit of the price for the electricity sold by the energy grid to the hybrid energy storage power station, The lower limit of electricity that the energy grid can purchase from hybrid energy storage power stations.

[0186] The product of the electricity sales power and electricity sales price in the transaction between the energy grid and the hybrid energy storage power station is expressed as:

[0187]

[0188] in, is the product of the electricity sales power and electricity sales price in the transaction between the energy grid and the hybrid energy storage power station, The upper limit of the amount of electricity that the energy grid can sell to the hybrid energy storage power station, The lower limit for the amount of electricity that the energy grid can sell to hybrid energy storage power stations.

[0189] The product of the purchased power and the purchased power price in the transaction between the energy grid and the hybrid energy storage power station is expressed as:

[0190]

[0191] in, is the product of the purchased power and the purchased power price in the transaction between the energy grid and the hybrid energy storage power station. The upper limit of the amount of electricity that the energy grid can purchase from the hybrid energy storage power station, The lower limit of electricity that the energy grid can purchase from hybrid energy storage power stations.

[0192] The objective function of the energy grid and hybrid energy storage power station is transformed into:

[0193]

[0194] The bilinear terms in the objective functions of the energy grid and the hybrid energy storage power station are linearized in order to solve the transaction power and transaction price of the energy grid and the hybrid energy storage power station.

[0195] Step 6: Determine the most profitable cooperative transaction plan for the energy grid group based on the operating costs of each energy grid;

[0196] Use Nash negotiation to establish a cooperative transaction model for energy grid groups, thereby determining the solution that maximizes the benefits of energy grid cooperation;

[0197] Establishing a cooperative game model for the energy grid:

[0198]

[0199]

[0200] in, is the transaction price between energy networks, For trading electric power between energy grids, The breakdown point in negotiations over the cost of running the energy grid, is the energy network transaction balance coefficient.

[0201] The distributed optimization of cooperative transactions in energy grid clusters is given. The specific steps are as follows:

[0202] Step 61: Establish an augmented Lagrangian function model, the formula is:

[0203]

[0204] in, The optimal dispatch value of the energy grid group from step 3, λ i,t is the dual variable of the energy grid operation cost, ρ i for The penalty factor, is the optimal augmented Lagrangian function value;

[0205] Step 62: Initialize the dual variables and penalty factors, set the first transaction price and the second transaction price between energy networks to 0, and the number of iterations k = 0;

[0206] Step 63: After receiving the information from other energy networks, the energy network control solves the optimal scheduling problem according to the augmented Lagrangian function model (35), obtains a set of optimal energy network transaction prices (i.e., first transaction prices), and sends the first transaction prices back to the energy network that needs to trade;

[0207] Step 64: After receiving the first transaction price, the energy grid control center that needs to trade solves the optimal scheduling problem based on the augmented Lagrangian function model to obtain another set of optimal energy grid transaction prices (i.e., second transaction prices), and sends the second transaction prices back to the energy grid control center.

[0208] Step 65: Update the dual variable and penalty factor, as well as the number of iterations k=k+1;

[0209]

[0210] Step 66: Determine whether the loop has converged based on the first transaction price and the second transaction price; if so, output the result; otherwise, continue to step 63;

[0211]

[0212] Here, ε is a very small number.

[0213] like Figure 2 As shown, a hybrid energy storage-based urban rail energy grid cluster collaborative optimization system includes: two or more energy grid power generation units, a hybrid energy storage power station coordination unit, an energy operator electricity price setting unit, an energy grid and hybrid energy storage power station price coordination unit, an energy grid cluster control unit, a power generation environment monitoring unit, a communication control unit, and an operating cost acquisition unit;

[0214] The energy grid power generation unit is used to generate electricity;

[0215] The hybrid energy storage power station coordination unit is used to obtain the configuration capacity of batteries and supercapacitors in the hybrid energy storage power station;

[0216] The energy operator's electricity price setting unit obtains the transaction prices of energy operators, energy grids, and hybrid energy storage power stations;

[0217] The energy grid and hybrid energy storage power station price coordination unit is used for the transaction price between the energy grid and the hybrid energy storage power station;

[0218] The energy grid cluster control unit is used to obtain the optimal scheduling method of the energy grid based on the energy grid cluster control model;

[0219] The power generation environment monitoring unit is used to obtain rail transit dynamic lighting load data and energy consumption data of the energy grid;

[0220] The communication unit is used to send the acquired rail transit dynamic load data, operating parameters of each device and transaction delivery to the energy grid group control unit;

[0221] The operation cost acquisition unit is used to obtain the optimal operation cost when the energy network is running.

[0222] Specific experiments:

[0223] This embodiment uses an energy grid group consisting of three energy grids. Each energy grid consists of photovoltaic (PV) and (load). The hybrid energy storage station consists of batteries and supercapacitors. The relevant parameters of the batteries and supercapacitors are shown in Table 1:

[0224] Table 1 System parameters of energy storage device

[0225]

[0226] The experimental results are given according to the parameters in Table 1. Figure 3 The purchase and sale prices of electricity for energy operators and energy grid groups, Figure 4 The purchase and sale prices of electricity for energy operators and hybrid energy storage power plants; Figure 3 、 4 It is determined according to step 1 and obtained experimentally from formulas (1)-(9). The horizontal axis of the two graphs is in hours and the vertical axis is in yuan;

[0227] Figure 8 The charging status of the hybrid energy storage power station, Figure 9 It is the hybrid energy storage discharge state. Figure 8 、 9 It is determined according to step 2 and obtained experimentally from formulas (10)-(14). The horizontal axes of the three graphs are in hours and the vertical axes are in watts;

[0228] Figure 5 is the dispatch result of energy network 1, Figure 6 is the dispatch result of energy network 2, Figure 7 is the dispatch result of energy network 3; Figure 5 、 6 , 7 are determined according to steps 3 and 4, and are obtained experimentally from formulas (15)-(23) and (26). The horizontal axes of the three graphs are in hours, and the vertical axes are in watts;

[0229] Figure 10 The electricity price set for the hybrid energy storage power station to the energy network 1; Figure 10 、 11 , 12 are determined according to step 5 and obtained experimentally from formulas (27)-(32). The horizontal axes of the three graphs are in hours and the vertical axes are in yuan;

[0230] Figure 13 is the transaction price between energy grids, which is determined according to step 6 and obtained experimentally from formulas (33)-(37). The horizontal axis of the figure is in hours and the vertical axis is in yuan;

[0231] Table 2 Comparison of operating costs between energy grid clusters and non-cooperative energy grids

[0232]

[0233] Table 3 Comparison of operating costs considering hybrid energy storage power stations and energy grids with energy storage

[0234]

[0235] The comparison of the operating costs of the energy grid cluster and the non-cooperative energy grid is shown in Table 2. It can be seen from the table that the operating cost of the energy grid cluster is 38.3% lower than that of the non-cooperative energy grid.

[0236] Table 3 shows a comparison of the operating costs of a hybrid energy storage power station and the energy grid with energy storage added. The energy grid group that considers a hybrid energy storage power station has an operating cost 2,631.89 yuan lower than the energy grid that adds energy storage. This is because the addition of energy storage to the energy grid requires consideration of the daily operating costs of the energy storage system.

[0237] With the above-described preferred embodiments of the present invention as inspiration, and with reference to the above description, relevant personnel are fully capable of making various changes and modifications without departing from the technical scope of this invention. The technical scope of this invention is not limited to the contents of the specification and must be determined according to the scope of the claims.

Claims

1. A method for collaborative optimization of urban rail energy grids based on hybrid energy storage, characterized in that: The following steps are involved: Step 1: Collect the electricity purchase and sales prices and electricity consumption between energy operators and different energy suppliers, and build a profit maximization model for energy operators; Step 2: Build a hybrid energy storage power station operation cost minimization model using the investment and maintenance costs of the hybrid energy storage power station, the cost of purchasing electricity from energy operators, the service fees charged by the energy grid for using the hybrid energy storage power station, and the cost of charging and discharging the hybrid energy storage power station by the energy grid. Step 3: Build an optimal operating cost model for the energy grid based on the transaction costs with energy operators, the transaction costs with hybrid energy storage power plants, and the service costs provided to hybrid energy storage power plants. Step 4: Use the KKT condition to transform the optimal operating cost model of the energy grid and the hybrid energy storage power station operating cost minimization model into a single model, add the energy grid group constraint to the hybrid energy storage power station model, and determine the optimal dispatch value of the hybrid energy storage power station; Step 5: Linearize the product of transaction price and transaction power in the optimal operating cost model of the energy grid and the hybrid energy storage power station operating cost minimization model; Step 6: Build a cooperative game model for the energy network and perform distributed optimization.

2. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: The optimal dispatch values ​​of hybrid energy storage power stations include: Constructing a Lagrangian model of energy grid clusters; Solve KKT conditions for energy grid groups; Boolean variables and positive numbers M are introduced to perform complementary relaxation on the Boolean variables of the Lagrangian model of the energy grid group.

3. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 2 is characterized in that: The formula of the Lagrangian model of the energy grid group is: in, λ i,t 、 is a Boolean variable, is the renewable energy output value, To respond to the demand of dynamic load, is the predicted load value of the dynamic image, P ij,t is the trading volume between energy sources, The upper limit of the amount of electricity that the energy grid can purchase from energy operators. Selling electricity to hybrid energy storage power stations for energy grids, Selling electricity to hybrid energy storage power stations for energy grids, The upper limit of the amount of electricity that the energy grid can sell to energy operators. The upper limit of the amount of electricity that the energy grid can purchase from the hybrid energy storage power station, The upper limit of the amount of electricity that the energy grid can sell to the hybrid energy storage power station, is the lower limit of dynamic load demand response, is the upper limit of dynamic load demand response, P SE,c,max is the upper limit of the discharge power of the hybrid energy storage power station, P SE,d,max is the upper limit of the charging power of the hybrid energy storage station, P ij,t The amount of electricity traded between energy grids, is the upper limit of the amount of electricity that can be traded between energy grids. It is the lower limit of electricity volume traded between energy grids.

4. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: Distributed optimization includes: Step 61: construct an augmented Lagrangian function model; Step 62: Initialize the dual variables and penalty factors, set the first transaction price and the second transaction price between energy networks, and the number of iterations; Step 63: Obtain a first transaction price based on the augmented Lagrangian function model, and send the first transaction price to the energy network control center that needs to trade; Step 64: After receiving the first transaction price, the energy network control center that needs to trade obtains a second transaction price based on the augmented Lagrangian function model, and sends the second transaction price back to the energy network control center. Step 65: Update the dual variables and penalty factors; Step 66: Determine whether the loop has converged based on the first transaction price and the second transaction price; if so, output the result; otherwise, continue to execute step 63.

5. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: Energy suppliers include: distribution networks, energy grids and hybrid energy storage power stations.

6. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: Set upper and lower limits and average constraints on the purchase and sale electricity prices of energy networks and hybrid energy storage power stations.

7. The method for collaborative optimization of urban rail energy grids based on hybrid energy storage according to claim 1, characterized in that: The constraints on the hybrid energy storage power station operation cost minimization model include: hybrid energy storage power station charge and discharge balance power constraint, hybrid energy storage power station charge and discharge power non-negativity constraint, battery charge and discharge power constraint, and supercapacitor charge and discharge power non-negativity constraint.

8. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: The constraints on the optimal operating cost model of the energy grid include: power balance constraints, demand response constraints, transaction imbalance constraints with hybrid energy storage power stations, upper and lower limit constraints on electric power transactions with energy operators, and transaction balance constraints between grid clusters.

9. The urban rail energy grid collaborative optimization method based on hybrid energy storage according to claim 1 is characterized in that: The McCormick convex envelope is used for linearization.

10. A system using the urban rail energy grid collaborative optimization method based on hybrid energy storage according to any one of claims 1 to 10, characterized in that: include: Several energy grid power generation units, hybrid energy storage power station coordination units, energy operator electricity price setting units, energy grid and hybrid energy storage power station price coordination units, energy grid cluster control units, power generation environment monitoring units and operating cost acquisition units; The energy grid power generation unit is used to generate electricity; The hybrid energy storage power station coordination unit is used to obtain the configuration capacity of the storage battery and lithium battery group in the hybrid energy storage power station; The energy operator's electricity price setting unit obtains the transaction prices of energy operators, energy grids, and hybrid energy storage power stations; The energy grid and hybrid energy storage power station price coordination unit is used for energy grid and hybrid energy storage power station prices; The energy grid cluster control unit is used to obtain the optimal scheduling method of the energy grid based on the energy grid cluster control model; The power generation environment monitoring unit is used to obtain rail transit dynamic lighting load data and energy consumption data of the energy grid; The operation cost acquisition unit is used to obtain the optimal operation cost when the energy network is running.

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