Optimal configuration and device of electric heating hybrid energy storage system and storage medium

Abstract

The invention relates to an optimal configuration method and device for an electric-heat hybrid energy storage system and a storage medium. The method comprises the steps that an electric-heat hybrid energy storage system overall structure for improving the wind and light absorption capacity is established; on the basis of different characteristics of electric energy storage and heat energy storage, a joint optimization control strategy of the electric-heat hybrid energy storage system is determined; energy cycle index constraints of an energy storage device are introduced into constraint conditions, a double-layer optimization configuration model of the electric heating hybrid energy storage system is constructed, and the double-layer optimization configuration model comprises an outer-layer optimization model and an inner-layer optimization model; and solving the double-layer optimal configuration model by combining a solver and a heuristic algorithm to obtain an optimal scheme for optimal configuration of the electric heating hybrid energy storage system. According to the method, the system can operate more economically, reasonably and efficiently, the service life of each device of the system is prolonged, and optimal distribution of energy is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of hybrid energy storage system optimization configuration, and in particular to an electric-thermal hybrid energy storage system optimization configuration, device and storage medium. Background Art

[0002] As humans have a deeper understanding of the concept of sustainable development of the environment, the safety and environmental pollution problems caused by the combustion of traditional fossil energy sources have become increasingly prominent. Seeking new clean energy and reducing the combustion of primary energy sources such as coal and oil has become a top priority. Wind power and photovoltaics, as environmentally friendly and mature renewable energy sources, can not only cope with today's climate change problems, but also reduce power generation costs and achieve economic operation of the system. However, for wind and solar power themselves, they have anti-peaking, randomness and volatility. When a large amount of new energy is connected to the power grid, the system's insufficient regulation capacity of the units has caused a large amount of wind and solar power to be abandoned in order to maintain frequency and voltage stability. Energy storage devices have the ability to separate energy generation and consumption in time and space and can quickly respond to fluctuations in wind and solar power output. They are becoming an important method to solve the problem of large amounts of wind and solar power abandonment.

[0003] However, in previous studies on energy storage optimization configuration, only single electric energy storage and thermal energy storage control strategies are often considered, and the characteristics of the two energy storages are rarely taken into account, and a joint optimization control strategy for electric and thermal energy storage is proposed; in the energy storage optimization configuration model, it is difficult to achieve the optimal allocation of system energy; in terms of solving the constructed model, most models adopt a single-layer structure and use traditional heuristic algorithms for solving, which is difficult to meet the speed and accuracy requirements of the solution. Therefore, it is necessary to make improvements. Summary of the invention

[0004] In order to solve the technical problems mentioned in the above background technology or at least partially solve the above technical problems, the present application provides an optimized configuration, device and storage medium of an electric-thermal hybrid energy storage system to achieve an improvement in the wind and solar power absorption capacity.

[0005] In a first aspect, the present application provides a method for optimizing configuration of an electric-thermal hybrid energy storage system, comprising:

[0006] Establish the overall structure of the electric-thermal hybrid energy storage system to improve the wind and solar power absorption capacity;

[0007] Based on the different characteristics of electric and thermal energy storage, determine the joint optimization control strategy of the electric-thermal hybrid energy storage system;

[0008] Introducing the energy cycle number constraint of the energy storage device into the constraint condition, constructing a double-layer optimization configuration model of the electric-thermal hybrid energy storage system, wherein the double-layer optimization configuration model includes an outer optimization model and an inner optimization model;

[0009] The double-layer optimization configuration model is solved by combining a solver with a heuristic algorithm to obtain the best solution for the optimization configuration of the electric-thermal hybrid energy storage system.

[0010] Optionally, establish the overall structure of the electric-thermal hybrid energy storage system to improve the wind and solar power absorption capacity, including:

[0011] Construct a hybrid energy storage system that includes cogeneration units, thermal power units, fans, photovoltaic units, heat storage tanks, and electric energy storage devices.

[0012] Optionally, constructing a two-layer optimization configuration model of the electric-thermal hybrid energy storage system specifically includes:

[0013] The outer optimization model aims to minimize the total operating cost and maximize the consumption of new energy, and the decision variables are the output electric and thermal power of the unit, and the charging and discharging power of the electric and thermal energy storage devices;

[0014] The inner optimization model aims to minimize the sum of the investment cost of the hybrid energy storage device and the outer optimization operating cost, and the decision variables are the capacity and power of the electric and thermal energy storage devices.

[0015] Optionally, a solver is combined with a heuristic algorithm to solve the two-layer optimization configuration model, specifically including: using a genetic algorithm to solve the inner layer objective function to generate the capacity and power of the hybrid energy storage device; using a gurobi solver to solve the outer layer objective function, returning the solution result to the inner layer, and repeatedly iterating to obtain the optimal electrical and thermal energy storage capacity and power, location and system cost.

[0016] Optionally, based on the different characteristics of electric and thermal energy storage, a joint optimization control strategy of the electric-thermal hybrid energy storage system is determined, specifically including:

[0017] The control strategies for absorbing wind power and photovoltaic power using single electric and thermal energy storage devices are analyzed. According to the different characteristics of electric and thermal energy storage devices, the joint optimization control strategy of electric and thermal hybrid energy storage system is summarized and proposed.

[0018] Optionally, a solver is combined with a heuristic algorithm to solve the two-layer optimization configuration model, specifically comprising the following steps:

[0019] Step 1: Initialize the system and input basic parameters of the unit, line, and energy storage;

[0020] Step 2: Genetic algorithm is used to randomly obtain the capacity and power of the electric and thermal energy storage devices to generate an initial population;

[0021] Step 3: Input the wind, solar, electricity and heat load forecast curves into the outer optimization model, and use the solver to solve the outer optimization scheduling results;

[0022] Step 4, returning the optimal result obtained by the optimization operation to the inner optimization model;

[0023] Step 5: Find the optimal solution of the population at the current moment;

[0024] Step six, determine if the convergence conditions are met and output the optimal solution for the capacity, power, location and cost of the electrical and thermal energy storage devices.

[0025] Optionally, a solver is combined with a heuristic algorithm to solve the two-layer optimization configuration model, which specifically includes:

[0026] When it is determined that the convergence condition is not met, the population is updated and the process returns to step 2 until the optimal solution is obtained.

[0027] Optionally, the electric-thermal hybrid energy storage system includes a cogeneration unit, a thermal power unit, a photovoltaic unit, a wind power unit, an electric energy storage device and a heat storage tank;

[0028] The cogeneration unit, the thermal power unit, the wind power unit, the photovoltaic unit and the electric energy storage device are used to supply electrical load; the heat storage tank and the cogeneration unit are used to supply thermal load.

[0029] In a second aspect, the present application also provides an optimized configuration device for an electric-thermal hybrid energy storage system, comprising:

[0030] A memory for storing program instructions;

[0031] A processor is used to call the program instructions stored in the memory to implement the method for optimizing the configuration of the electric-thermal hybrid energy storage system as described in any technical solution in the first aspect.

[0032] In a third aspect, the present application further provides a computer-readable storage medium storing a program code, wherein the program code is used to implement the method for optimizing configuration of an electric-thermal hybrid energy storage system as described in any technical solution in the first aspect.

[0033] The above technical solution provided by the embodiment of the present application has the following advantages over the prior art: first, the present invention establishes the overall structure of an electric-thermal hybrid energy storage system that improves the wind-solar absorption capacity; secondly, in view of the different characteristics of electric and thermal energy storage, the wind power and photovoltaic control strategies of a single electric and thermal energy storage device are explored, and a joint optimization control strategy for an electric-thermal hybrid energy storage system is proposed; thirdly, a two-layer optimization configuration model for a hybrid energy storage system is constructed, and in order to ensure the economic optimality of the system while improving the service life of each device in the system and achieving the optimal distribution of energy, the energy cycle number constraint of the energy storage device is introduced into the constraint conditions; finally, in terms of model solving, a solver combined with a heuristic algorithm is used to solve the model, which can ensure the solution accuracy while accelerating the solution speed, and obtain the best solution for the optimized configuration of the electric-thermal hybrid energy storage system.

[0034] 1. The combined optimization scheduling strategy of the electric and thermal hybrid energy storage devices proposed in the present invention fully considers the respective characteristics of the electric and thermal energy storage devices, and compared with the control strategy of a single energy storage device, can make the system operation more economical, reasonable and efficient;

[0035] 2. The hybrid energy storage system optimization configuration of the present invention is a two-layer decision, where the inner and outer layers correspond to different objectives and constraints. In addition to the constraints imposed on the inner and outer layers themselves, the inner and outer layers also influence each other, which can improve the speed and accuracy of the solution.

[0036] 3. In addition to fully considering the system's electrical and thermal balance constraints, energy storage device constraints, etc., the present invention introduces electrical and thermal energy storage device capacity cycle constraints into the outer model to reduce the number of charge and discharge uses of the energy storage device and increase the service life of the system, making the model more practical;

[0037] 4. In terms of solution method, the gurobi solver and heuristic algorithm are used for joint solution. Compared with a single heuristic algorithm, it can improve the solution speed while ensuring the solution accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0039] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0040] FIG. 1( a ) is a structural diagram of an optimized configuration system of an electric-thermal hybrid energy storage system provided in an embodiment of the present application;

[0041] FIG1( b ) is a flow chart of a method for optimizing configuration of an electric-thermal hybrid energy storage system provided in an embodiment of the present application;

[0042] Figure 2 A control strategy diagram for absorbing wind power and photovoltaic power for electric energy storage devices;

[0043] Figure 3 A control strategy diagram for the joint consumption of wind power and photovoltaic power by electric and thermal energy storage devices;

[0044] Figure 4 The structural diagram of the double-layer optimization model for the electric-thermal hybrid energy storage system;

[0045] Figure 5 Solve the flow chart for the double-layer optimization configuration model of the electric and thermal hybrid energy storage system;

[0046] Figure 6 This is the network topology diagram of the electric and thermal hybrid energy storage system;

[0047] Figure 7 Provide output curves for wind and solar forecasts;

[0048] Figure 8 is a graph showing the prediction of electrical and thermal loads;

[0049] Figure 9 is a comparison of solutions obtained using different solution methods;

[0050] Figure 10 shows the impact of changes in new energy penetration on the optimal configuration of energy storage;

[0051] Figure 11 shows the impact of changes in new energy penetration on energy storage configuration costs and total system costs. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0053] For ease of understanding, the following is a detailed introduction to an optimized configuration method, device and storage medium of an electric-thermal hybrid energy storage system provided in an embodiment of the present application. Referring to FIG. 1( b ), an optimized configuration method of an electric-thermal hybrid energy storage system includes the following steps:

[0054] Step S1: Establishing the overall structure of an electric-thermal hybrid energy storage system that improves the wind-solar energy absorption capacity;

[0055] The optimal configuration system structure of the electric heat hybrid energy storage system is shown in Figure 1 (a). The system structure basically includes a cogeneration unit, a thermal power unit, a photovoltaic unit, a wind turbine unit, an electric energy storage device and a heat storage tank. The cogeneration unit, the thermal power unit, the wind turbine, the photovoltaic unit and the electric energy storage device supply the electric load; the heat storage tank and the cogeneration unit supply the heat load.

[0056] Step S2: Determine the joint optimization control strategy of the hybrid energy storage system based on the different characteristics of electric and thermal energy storage;

[0057] The present invention first analyzes the wind power and photovoltaic control strategy using a single electric and thermal energy storage device, and on this basis, proposes a joint optimization control strategy for electric and thermal hybrid energy storage.

[0058] Step S2.1: Wind and solar energy storage system control strategy

[0059] The control strategy of the electric energy storage device system proposed in the present invention requires the following premises:

[0060] 1) In order to absorb as much new energy as possible, wind power and photovoltaic power supply should be considered first.

[0061] 2) Because the cogeneration unit operates in a thermal-electric coupling mode, the output electric power is supplied on the basis of satisfying the thermal load balance, so the output electric power of the unit is variable. For the independence of the power grid, the electric energy storage device has a higher priority.

[0062] 3) For the system, in order to meet the load balancing requirements under the constraints of the energy storage charge state, it is necessary to purchase or sell electricity from the power grid.

[0063] Based on the three premises mentioned above, the electric energy storage control strategy proposed by the present invention is as follows: Figure 2 As shown. First, the unit supplies the heat load, and the wind power, photovoltaic, and units work simultaneously to supply the electric load. The surplus power beyond the electric load is used to charge the electric energy storage device. When the capacity limit is exceeded, the surplus is sold to the grid. When the unit, photovoltaic, and wind power cannot meet the supply of electric load, the electric energy storage device discharges. If the electric power is unbalanced, the power deficit is purchased from the grid. When the unit is unable to supply the heat load, the heat network will supplement the heat supply.

[0064] Step S2.2: Wind and solar energy storage system control strategy

[0065] The optimization scheduling cycle of the present invention is 1 day, and one optimization period is 1 hour. Herein, a method for judging the heat storage device in the heat storage or heat release period is given, so that the output plan of the unit can be arranged economically and reasonably in the optimization scheduling stage:

[0066] The main reason for wind and solar power abandonment is that the cogeneration unit has limited ability to adjust the power output when supplying heat load. When the heat load constraint is not considered, the unit generally has a strong peak load regulation capability. Assuming that the minimum power output of the system is P min for:

[0067]

[0068] Where: M, N represent the number of cogeneration units and thermal power units respectively; P chp,min , P g,min Each represents the minimum output of the two units.

[0069] The equivalent electrical load value of the system during this period is:

[0070]

[0071] in: They respectively represent the electric load, wind power and photovoltaic output during this period.

[0072] when, When the coupling constraints between heat and electricity are considered, and the conflict between electric and thermal loads in time is considered, the thermal energy storage device in this period must be a heat release period. , further judgment is required.

[0073] The present invention adopts the method of "determining heat by electricity" to determine the charging and discharging time of the thermal energy storage device. When the electric power output of the cogeneration unit is a fixed value, the adjustment range of its thermal output is obtained. In order to make the thermal power unit have a larger peak-shaving capacity, it can achieve maximum heating under the premise of meeting the electric load demand. Therefore, the maximum thermal power that the unit can output in each time period within the optimization cycle is taken as the goal, and the balance of electric output is taken as a constraint condition, and the following model is obtained:

[0074]

[0075] in: Represents the thermal output of the unit at time t.

[0076] Electric power balance constraints:

[0077]

[0078] in: They each represent the electric power produced by the cogeneration unit and the thermal power unit at time t.

[0079] The upper and lower limits of the unit output are:

[0080]

[0081] in: They represent the minimum and maximum power output of the cogeneration unit and thermal power unit at time t respectively.

[0082] Hill climbing constraints:

[0083]

[0084] in: They represent the upper and lower ramp rates of the cogeneration unit and thermal power unit at time t respectively.

[0085] According to the above model, the maximum thermal output of the cogeneration unit in period t can be obtained, which is set as Compare the sum of the maximum thermal powers at this time with the heat load to determine whether the cogeneration unit has sufficient thermal peak regulation range to supply the heat load on the basis of meeting the heat load demand. At this time, it can be concluded whether the heat storage device absorbs or releases heat in time period t.

[0086]

[0087] Wherein: R(t) represents the charging and discharging state flag of the thermal energy storage device, 1 represents heat dissipation, and 0 represents heat storage.

[0088] When R(t)=1, it means that the cogeneration unit cannot meet the heat load demand and the needs of wind and solar power consumption during this period, and a heat storage tank is needed to release heat. When R(t)=0, it means that the cogeneration unit still has a large margin to store heat for the heat storage device while meeting the needs of wind and solar power consumption.

[0089] Step S2.3: Control strategy for combining wind power and photovoltaic power generation with electric and thermal energy storage devices

[0090] Considering the characteristics of thermal energy storage devices that have a long response time to fluctuations and a limited adjustment level, and electric energy storage devices that have a small capacity and a high unit cost, the thermal and electric energy storage devices are complementary to each other to enhance their combined effect of absorbing wind power and photovoltaics. Based on the control strategy of the aforementioned single electric and thermal energy storage devices, the present invention proposes a control strategy for electric and thermal energy storage devices to jointly absorb wind power and photovoltaics, such as Figure 3 As shown:

[0091] First, determine whether there is wind or solar abandonment during period t. During this period, most of the wind power and photovoltaic output are at peak times, the electricity load is at low points, and the heat load is at peak times. During this period, the cogeneration unit reduces the heat output, and the lack of heat load is supplemented by the thermal energy storage device. If there is still new energy that has not been absorbed, charge the electric energy storage device. When the new energy can be fully absorbed, during this period, most of the electricity load is at peak times and the heat load is at low points, the cogeneration unit increases the output power, and the extra heat is stored by the thermal energy storage device. During this period, the electric energy storage device releases electricity to supply the electric load.

[0092] Step S3: Construct a two-layer optimization configuration model for the hybrid energy storage system. In order to ensure the economic optimization of the system, improve the service life of each device in the system, and achieve the optimal distribution of energy, the energy cycle number constraint of the energy storage device is introduced into the constraint condition.

[0093] Step S3.1: Hybrid energy storage system optimization configuration model block diagram

[0094] In order to improve the speed and accuracy of the solution, the optimal configuration of the hybrid energy storage system is a two-layer decision. The inner and outer layers correspond to different goals and constraints. In addition to the constraints on the inner and outer layers themselves, the inner and outer layers also affect each other. The specific structure is as follows: Figure 4 As shown:

[0095] from Figure 4 It can be seen that the outer model of the hybrid energy storage system optimization configuration aims to minimize the total operating cost and maximize the new energy consumption. The decision variables are the output electricity and thermal power of the unit, and the charging and discharging (thermal) power of the electric and thermal energy storage devices. The inner optimization model aims to minimize the sum of the investment cost of the hybrid energy storage device and the outer optimization operating cost. The decision variables are the capacity and power of the electric and thermal energy storage devices.

[0096] Step S3.2: Outer objective function and constraints

[0097] Step S3.2.1: Outer objective function

[0098] The objective function of the outer model consists of three parts, namely the unit operating cost, start-up and shutdown cost, and the penalty for non-consumption of new energy (all converted into annual operating costs here). The specific expression is as follows:

[0099] minG s =365×(C op +C sp +C awp ) (8)

[0100] Where: C op , C sp , C awp They respectively represent the unit operating cost, the start-up and shutdown cost of thermal power units, and the penalty cost for non-consumption of new energy.

[0101] (1) Unit operating cost:

[0102] The operating cost of the unit consists of two parts, the specific formula is as follows:

[0103] C op =C chp +C g (9)

[0104]

[0105]

[0106] Where: C chp , C g Respectively represent the operating costs of cogeneration units and thermal power units; chp , Π g Respectively represent the cogeneration unit and thermal power unit set; P g,i,t P represents the electric power output of the thermal power unit at node i at time t; c,i,t , H c,i,t They represent the electrical power and thermal power output of the cogeneration unit at node i at time t; T represents the optimal scheduling period; a chp.i 、b chp,i 、c chp,i Each represents the cost coefficient of the cogeneration unit at node i; a g.i 、b g,i 、c g,i Each represents the cost coefficient of the thermal power unit at node i; c v is a fixed value (constant).

[0107] (2) Unit start-up and shutdown costs:

[0108] Since the main function of large-capacity cogeneration units is to meet the heat load demand and start and stop are not allowed, only the start and stop costs of thermal power units are calculated:

[0109]

[0110] Where: n g,i,t represents the operating state variable of the thermal power unit at node i at time t (1 means running, 0 means stopped); W g,i Represents the start-up and shutdown cost of the thermal power unit at node i.

[0111] (3) Penalty costs for non-consumption of new energy

[0112] C awp =C aw +C ap (13)

[0113]

[0114]

[0115] Where: C aw , C ap The cost of abandoning wind and solar power respectively; w , Π pv Respectively represent wind power and photovoltaic units; P pw,i,t , P aw,i,tThey represent the predicted value and actual value of wind power output of wind turbine group at node i at time t; P pv,i,t , P av,i,t They each represent the predicted and actual values ​​of the power output of the photovoltaic unit at node i at time t; γ and σ represent the penalty factors for wind and solar abandonment, respectively.

[0116] Step S3.2.2: Outer constraints

[0117] The energy storage system optimization model mainly includes equality constraints (energy exchange constraints) and inequality constraints (constraints on each device in the system), which are specifically described as follows:

[0118] (1) Electric power balance constraints;

[0119]

[0120] Where: P es,discha,i,t , P es,cha,i,t They represent the discharge and charging power of the energy storage device at node i at time t; P L,i,t represents the electrical load power of node i at time t; l Represents the line bus set; P ij,t Represents the active power transmitted between branches ij at time t.

[0121] The present invention uses DC power flow constraint as the network power flow constraint in electric power balance, and needs to make the following assumptions: first, ignore the influence of line conductance (it is assumed that the line susceptance is much larger than the conductance); second, the phase angle difference of the line is very small, and the parallel reactance to the ground is negligible; finally, the bus voltage remains unchanged.

[0122] According to the above description, the power flow P of the line ij,t The expression is:

[0123]

[0124] |P ij,t |≤P ij,max (18) Where: P ij,t represents the active power transmitted between branches ij at time t; b i,j represents the susceptance between branches ij; θ ij,t Represents the voltage phase angle difference between branches ij at time t.

[0125] (2) Thermal power balance constraints:

[0126]

[0127] Where: hs Represents a thermal energy storage device set; H hs,dis,i,t , Hhs,cha,i,t They represent the thermal power released and absorbed by the thermal energy storage device at node i at time t; H L,t represents the heat load at time t.

[0128] (3) Operation constraints of electric energy storage devices:

[0129] Operating power constraints of electric energy storage devices:

[0130] 0≤P es,dis,i,t ≤υ es,dis P es,i (20)

[0131] 0≤P es,cha,i,t ≤υ cha,es P es,i (21) Where: υ cha ,υ discha Each represents the charging and discharging decision variable of the energy storage device (1 means action, 0 means no action); P es,i Represents the electric power of the electric energy storage device configured at node i at t.

[0132] Capacity constraints of electric energy storage devices:

[0133] S es,i,0 =S es,i,T

[0134]

[0135] Where: S es,i,t represents the capacity of the energy storage device at node i at time t; η dis , η cha They represent the discharge and charge efficiency of the energy storage device respectively; Δt represents the time interval; S es,i,0 , S es,i,T Represents the starting and ending capacities of the optimization cycle of the electric energy storage device at node i.

[0136] Constraints on charging and discharging status of electric energy storage devices:

[0137] υ cha *υ discha =0 (23)

[0138] Where: A product of zero indicates that the energy storage device cannot be charged and discharged simultaneously in a time period.

[0139] (4) Thermal energy storage device operation constraints

[0140] Thermal energy storage device power constraints:

[0141] 0≤H hs,dis,i,t ≤υ hs,dis H hs,i (twenty four)

[0142] 0≤H hs,cha,i,t ≤υ cha,hs H hs,i (25) Among them: H hs,i Represents the thermal power of the thermal energy storage device configured at node i.

[0143] Thermal energy storage device state constraints:

[0144] H hs,cha,i,t H hs,dis,i,t =0 (26)

[0145]

[0146] Where: C hs,i,t represents the heat storage capacity of the thermal energy storage device at node i at time t; H hs,i,cha,t H hs,i,dis,t =0 means that the thermal energy storage device is not allowed to absorb and release heat in the same period; They represent the charging and discharging efficiency of the thermal energy storage device respectively;

[0147] (5) Capacity cycle constraints of electrical and thermal energy storage devices:

[0148] Electric and thermal energy storage devices consume electric and thermal energy during their charging and discharging, and storing and releasing heat. Reducing the number of charging and discharging of energy storage devices is of great significance for coordinating the power distribution between various devices, maintaining the healthy cycle state of various devices in the system, and improving the service life of the system and energy utilization. Therefore, the present invention introduces the capacity cycle constraints of electric and thermal energy storage devices into the model constraints:

[0149] Constraints on the number of cycles of electric energy storage devices:

[0150]

[0151] N es,i ≤N es,i,max (29)

[0152] Constraints on the number of cycles of thermal energy storage devices:

[0153]

[0154] N hs,i ≤N hs,i,max (31)

[0155] Where: N es,i,max 、N hs,i,max They respectively represent the maximum value allowed for the number of power cycles of the electrical and thermal energy storage devices at node i within the optimization period.

[0156] (6) Constraints on cogeneration units:

[0157] 0≤H c,i,t ≤H c,i,max (32)

[0158] P c,i,max -c v1 H c,i,t ≥P c,i,t ≥min{c m H c,i,t +D i ,P c,i,min -c v2 H c,i,t} (33)

[0159] P c,i,t -P c,i,t-1 ≤γ c,i,up (34)

[0160] P c,i,t-1 -P c,i,t ≤γ c,i,down (35)

[0161] Where: H c,i,max represents the maximum thermal power output of the unit at node i; P c,i,min , P c,i,max Respectively represent the minimum and maximum thermal power output of unit i; γ c,i,up , γ c,i,down Respectively represent the up and down climbing rates of the unit; c v1 、c v2 、c m , D i are all constants;

[0162] (7) Constraints on thermal power units:

[0163] P g,i,min ≤P g,i,t ≤P g,i,max (36)

[0164] P g,i,t -P g,i,t-1 ≤ρ g,up (37)

[0165] P g,i,t-1 -P g,i,t ≤ρ g,down (38)

[0166] (n g,i,t-1 -n g,i,t )(T g,on,i,t -T g,on,i,min )≥0 (39)

[0167] (n g,i,t -ng,i,t-1 )(T g,off,i,t -T g,off,i,min )≥0 (40)

[0168] Where: P g,i,min ,P g,i,max They represent the minimum and maximum values ​​of the electric power output by the generator set at node i respectively;

[0169] ρ g,up ,ρ g,down They represent the up and down climbing rates of unit i respectively. g,on,i,min , T g,off,i,min They represent the minimum startup and shutdown time of the unit at node i respectively.

[0170] (8) Spinning reserve constraints:

[0171]

[0172] Where: P by The system's spinning reserve quota.

[0173] Step S3.3: Inner objective function and constraints

[0174] Step S3.3.1: Inner objective function

[0175] The inner objective function is to minimize the sum of the configuration cost of energy storage and the outer optimized operation cost. The energy storage device includes two forms of energy storage: electricity and heat. Considering the configuration cost, the capital recovery coefficient is introduced to convert it into annual investment cost. The specific expression is as follows:

[0176] minC total =C ess +G s (42)

[0177] Where: C ess represents the cost of optimal energy storage configuration; G s Represents the total daily operating cost of the outer optimization model.

[0178]

[0179] Where: es ,Π hs Represents a collection of electrical and thermal energy storage devices; ε represents the investment recovery rate of the electric and thermal energy storage devices; β and ω represent the operation life cycle of the electric and thermal energy storage devices; χ e , δ e Respectively represent the unit power and capacity investment cost coefficients of the electric energy storage device; P es,i , H hs,i They represent the power of the electric and thermal energy storage devices at node i respectively; φ h They represent the unit power and capacity investment cost coefficient of thermal energy storage, S es,i , C hs,i They represent the capacity of the electric and thermal energy storage devices at node i respectively;

[0180] Step S3.3.2: Inner constraints

[0181] Constraints

[0182] C hs,i,min ≤C hs,i ≤C hs,i,max (44)

[0183] H hs,i,min ≤H hs,i ≤H hs,i,max (45)

[0184] S es,i,min ≤S es,i ≤S es,i,max (46)

[0185] P es,i,min ≤P es,i ≤P es,i,max (47)

[0186] Where: S es,i,min , S es,i,max represents the minimum and maximum values ​​of the energy storage capacity allowed for node i; C hs,i,min , C hs,i,max They represent the minimum and maximum capacities of the thermal energy storage device allowed to be configured at node i;

[0187] Step S4: Solving the model by combining a solver with a heuristic algorithm can ensure the accuracy of the solution and speed up the solution, and obtain the best solution for the optimal configuration of the electric thermal hybrid energy storage system, while ensuring the full absorption of wind power and photovoltaic power, minimizing the total system cost.

[0188] The inner layer of the hybrid energy storage two-level programming model established by the present invention is a nonlinear programming problem, and the outer layer is a mixed integer linear optimization problem. In view of the above situation, this section uses the combination of gurobi solver and genetic algorithm to carry out calculation and solution. The genetic algorithm is used to solve the inner layer function to generate the capacity and power of the hybrid energy storage device; the gurobi solver is used to solve the outer layer function, and the solution result is returned to the inner layer. The flowchart of the solution process is shown in Figure 5:

[0189] The specific steps are as follows:

[0190] (1) Initialize the system and input basic parameters of the unit, line, and energy storage;

[0191] (2) Using genetic algorithms to randomly obtain the capacity and power of electrical and thermal energy storage devices to generate an initial population;

[0192] (3) Input the wind, solar, electricity, and heat load forecast curves into the outer optimization model and use the solver to solve the outer optimization scheduling results.

[0193] (4) Return the optimal result obtained from the optimization operation to the inner model

[0194] (5) Find the optimal solution for the population at the current moment;

[0195] (6) Determine whether the convergence condition is met. If not, update the population and return to step (2) until the optimal solution is obtained.

[0196] (7) Optimal solution for the capacity, power, location and cost of output electrical and thermal energy storage devices.

[0197] Next, the effectiveness of the optimization configuration method of the electric-thermal hybrid energy storage system to improve the wind and solar power absorption capacity is closely verified.

[0198] The present invention applies the improved IEEE9 node system, and the network topology is shown in Figure 6 Nodes 1 and 2 are connected to cogeneration units respectively; nodes 3 and 5 are connected to thermal power units respectively, and node 3 is also connected to wind power and photovoltaic units with capacities of 400MW and 150MW respectively; nodes 5, 6, 7, and 9 are all load nodes.

[0199] The unit parameters, electrical and thermal energy storage device parameters, typical day wind and solar output curve, and electrical and thermal load prediction curves of the present invention are shown in Table 1, Table 2, Table 3, Figure 7 and Figure 8.

[0200] Table 1 Basic parameters of cogeneration unit

[0201]

[0202] Table 2 Basic parameters of thermal power units

[0203]

[0204]

[0205] Table 3 Parameters of electrical / thermal energy storage device

[0206]

[0207] Other parameters of the simulation model used in this embodiment are also taken from actual data of a certain area in Liaoning.

[0208] In order to verify the effectiveness of the model constructed in this section, three scenarios were established:

[0209] Scenario 1: There is no energy storage device in the regional power grid, and only the unit is used to meet the electric and thermal balance relationship. Scenario 2: Energy storage optimization configuration is performed without considering the capacity cycle number of electric and thermal energy storage devices. Scenario 3: Energy storage optimization configuration is performed with the capacity cycle number of electric and thermal energy storage devices considered;

[0210] Figure 9(a) and Figure 9(b) are iterative curves of the total cost of the optimal configuration of the electric and thermal hybrid energy storage systems respectively obtained by using the genetic algorithm and the solver combined with the genetic algorithm. The total cost of the optimal configuration of the electric and thermal hybrid energy storage system solved by the genetic algorithm is:

[0211] 6.9379759×10 7 Yuan, the total cost of the optimal configuration of the electric-thermal hybrid energy storage system solved by the solver and genetic algorithm is 6.9378652×10 7 , it can be seen that the solution accuracy of the two solution methods is very small. As shown in Figure 9, the total cost iteration curve using the solver and genetic algorithm to solve the problem converges faster and has fewer iterations, indicating that the joint solution method can improve the solution speed while ensuring the accuracy of the solution.

[0212] Table 4 compares the results of hybrid energy storage optimization configuration of regional power grids under scenarios 1 and 2. It can be seen from scenario 1 that when only cogeneration units and thermal power units meet the system's electrical and thermal load requirements, the system's unabsorbed wind and solar power capacity is 1.048×10 5 MWh. In scenario 2, by configuring electric and thermal energy storage devices, the regional power grid can fully absorb the wind and solar power. In addition, due to the charging and discharging characteristics of the electric and thermal energy storage devices, the forced power output of the cogeneration unit can be reduced, which reduces the total operating cost of the unit by 6.37×10 6 Yuan, although the energy storage configuration cost increased by 7.154×10 6 Yuan, but the total cost of the system decreased by 9.69×10 6 The economic efficiency of the system is further improved.

[0213] Table 4 Wind power consumption and cost

[0214]

[0215] Table 5 shows the impact of the change in the number of cycles of the electric and thermal energy storage device capacity on the results of the energy storage optimization configuration. It can be seen from the table that in the above four cases, wind and solar power can be absorbed. When the number of cycles of the electric and thermal energy storage devices changes from (4.5-3.5 / 3.5-2), the capacity and power of the electric energy storage device increase, and the capacity and power of the thermal energy storage device decrease.

[0216] Table 5 Wind and solar power consumption and cost under scenario 3

[0217]

[0218]

[0219] In addition, as the number of cycles of the electric and thermal energy storage devices continues to decrease, the system's electric and thermal energy storage configuration costs continue to rise, with a maximum value of 8.387712×10 6 Yuan. Due to the limitation of charging and discharging times of energy storage device, the operating cost of the unit is gradually increasing, and the total cost of the system is also increasing.

[0220] The impact of changes in clean energy penetration on the optimization configuration results is shown in Figure 10. With the continuous increase in clean energy penetration (22%-38%), in order to absorb wind power and photovoltaic power, the capacity and power of electric and thermal energy storage devices are gradually increased. Compared with thermal energy storage devices, the capacity and power of electric energy storage devices vary more than the capacity and power of thermal energy storage devices. At the same time, for a single electric and thermal energy storage device, the capacity is more sensitive to wind power penetration than the power. Therefore, in a clean energy system with a high penetration rate, the system is more inclined to increase the configuration capacity of electric and thermal energy storage devices.

[0221] The impact of changes in clean energy penetration on the total system cost is shown in Figure 11. As shown in Figure 11(a), as the wind power penetration rate of the system increases, the energy storage configuration cost of the system increases. As shown in Figure 11(b), although the configuration cost of the energy storage device continues to increase, as wind power and photovoltaic power are continuously integrated into the grid, the operation and start-up and shutdown costs of the units are continuously decreasing, making the total cost of the system increase from 7.06×10 7 Yuan dropped to 6.94×10 7 In addition, when the penetration rate reaches 52% or above, even if the capacity and power of the energy storage device are increased, the investment cost increases, the amount of new energy consumed remains unchanged, and the excess clean energy cannot be consumed. The penalty cost of new energy not consumed increases, resulting in an increase in the total cost of the system. This is due to the limitations of the network structure, which means that the expansion of the line needs to be considered in the energy storage optimization configuration model.

[0222] In some other specific embodiments of the present application, the present application further provides an optimization configuration device for an electric-thermal hybrid energy storage system, comprising:

[0223] A memory for storing program instructions;

[0224] A processor is used to call the program instructions stored in the memory to implement the electric-thermal hybrid energy storage system optimization configuration method in any of the above embodiments.

[0225] In some further specific embodiments of the present application, the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program code, and the program code is used to implement the method for optimizing configuration of the electric-thermal hybrid energy storage system as in any of the above embodiments.

[0226] It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0227] The foregoing is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for optimizing the configuration of an electric-thermal hybrid energy storage system, characterized in that: include: Establish the overall structure of the electric-thermal hybrid energy storage system to improve the wind and solar power absorption capacity; Based on the different characteristics of electric and thermal energy storage, determine the joint optimization control strategy of the electric-thermal hybrid energy storage system; Introducing the energy cycle number constraint of the energy storage device into the constraint condition, constructing a double-layer optimization configuration model of the electric-thermal hybrid energy storage system, wherein the double-layer optimization configuration model includes an outer optimization model and an inner optimization model; The double-layer optimization configuration model is solved by combining a solver with a heuristic algorithm to obtain the best solution for the optimization configuration of the electric-thermal hybrid energy storage system.

2. The method for optimizing the configuration of an electric-thermal hybrid energy storage system according to claim 1, characterized in that: Establish the overall structure of the electric-thermal hybrid energy storage system to improve the wind and solar power absorption capacity, including: Construct a hybrid energy storage system that includes cogeneration units, thermal power units, fans, photovoltaic units, heat storage tanks, and electric energy storage devices.

3. The method for optimizing the configuration of the electric-thermal hybrid energy storage system according to claim 2, characterized in that: Constructing a two-layer optimization configuration model of the electric-thermal hybrid energy storage system specifically includes: The outer optimization model aims to minimize the total operating cost and maximize the consumption of new energy, and the decision variables are the output electric and thermal power of the unit, and the charging and discharging power of the electric and thermal energy storage devices; The inner optimization model aims to minimize the sum of the investment cost of the hybrid energy storage device and the outer optimization operation cost, and the decision variables are the capacity and power of the electric and thermal energy storage devices.

4. The method for optimizing the configuration of the electric-thermal hybrid energy storage system according to claim 3, characterized in that: The two-layer optimization configuration model is solved by combining a solver with a heuristic algorithm, specifically including: using a genetic algorithm to solve the inner layer objective function to generate the capacity and power of the hybrid energy storage device; using a gurobi solver to solve the outer layer objective function, returning the solution result to the inner layer, and repeatedly iterating to obtain the optimal electrical and thermal energy storage capacity and power, location and system cost.

5. The method for optimizing configuration of an electric-thermal hybrid energy storage system according to claim 1, characterized in that: Based on the different characteristics of electric and thermal energy storage, the joint optimization control strategy of the electric-thermal hybrid energy storage system is determined, specifically including: The control strategies for absorbing wind power and photovoltaic power using single electric and thermal energy storage devices are analyzed. According to the different characteristics of electric and thermal energy storage devices, the joint optimization control strategy of electric and thermal hybrid energy storage system is summarized and proposed.

6. The method for optimizing configuration of an electric-thermal hybrid energy storage system according to claim 4, characterized in that: The two-layer optimization configuration model is solved by combining a solver with a heuristic algorithm, which specifically includes the following steps: Step 1: Initialize the system and input basic parameters of the unit, line, and energy storage; Step 2: Genetic algorithm is used to randomly obtain the capacity and power of the electric and thermal energy storage devices to generate an initial population; Step 3: Input the wind, solar, electricity and heat load forecast curves into the outer optimization model, and use the solver to solve the outer optimization scheduling results; Step 4, returning the optimal result obtained by the optimization operation to the inner optimization model; Step 5: Find the optimal solution of the population at the current moment; Step six, determine if the convergence conditions are met and output the optimal solution for the capacity, power, location and cost of the electrical and thermal energy storage devices.

7. The method for optimizing configuration of an electric-thermal hybrid energy storage system according to claim 6, characterized in that: The two-layer optimization configuration model is solved by combining a solver with a heuristic algorithm, which specifically includes: When it is determined that the convergence condition is not met, the population is updated and the process returns to step 2 until the optimal solution is obtained.

8. The method for optimizing configuration of an electric-thermal hybrid energy storage system according to claim 1, characterized in that: The electric-thermal hybrid energy storage system includes a cogeneration unit, a thermal power unit, a photovoltaic unit, a wind power unit, an electric energy storage device and a heat storage tank; The cogeneration unit, the thermal power unit, the wind power unit, the photovoltaic unit and the electric energy storage device are used to supply electrical load; the heat storage tank and the cogeneration unit are used to supply thermal load.

9. An optimized configuration device for an electric-thermal hybrid energy storage system, characterized in that: include: A memory for storing program instructions; A processor, configured to call the program instructions stored in the memory to implement the method for optimizing configuration of the electric-thermal hybrid energy storage system according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, and the program code is used to implement the electric-thermal hybrid energy storage system optimization configuration method according to any one of claims 1 to 8.

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