Method and system for formulating energy storage charging and discharging strategies under two time-varying prices

Abstract

The invention provides an energy storage charging and discharging strategy formulation method and system under two time-varying prices. The method comprises the following steps: acquiring an original load curve of a power consumer, a time-of-use electricity price curve, a carbon emission factor time-varying curve, a carbon dioxide emission price and an energy storage system capacity; inputting the power consumer original load curve, the time-of-use electricity price curve, the carbon emission factor time-varying curve, the carbon dioxide emission price and the energy storage system capacity into a pre-constructed energy storage system charging and discharging strategy optimization model to obtain charging or discharging power of the energy storage system in each time period; generating an energy storage charging and discharging strategy based on the charging or discharging power of the energy storage system in each time period; according to the method, the energy storage system charging and discharging strategy optimization model is established according to the time-of-use electricity price and the carbon emission time-varying factor, and the charging and discharging strategy is formulated based on the charging or discharging power, output by the model, of the energy storage system in each time period, so that the problem that an existing energy storage system only considers a single-basis operation strategy such as the time-of-use electricity price is solved; and a reasonable energy storage charging and discharging strategy is formulated for a user.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for formulating energy storage charging and discharging strategies under two time-varying prices. Background Art

[0002] In the regional energy internet, due to the high generation of photovoltaic power during the day, high generation of wind power at night, and the operation of combined heat and power units based on heat, the phenomenon of abandoning photovoltaic and wind power is often more serious during the heating period. Energy storage equipment is the main means to increase the absorption rate of new energy power generation. Energy storage increases cost investment, and also brings economic benefits while absorbing new energy power generation. Therefore, formulating the charging and discharging strategy of the energy storage system is the key factor in solving the problem of new energy absorption.

[0003] In recent years, many scholars have studied the configuration of energy storage on the user side and the benefits of installing energy storage systems. For example, a two-layer optimization configuration model is established to determine the configuration plan, taking the full life cycle and daily dispatch cycle benefits of the energy storage system as the goals; the energy storage charging and discharging strategy is optimized by considering the economic benefits brought by arbitrage of user-side energy storage under the background of time-of-use electricity prices. The above research mainly considers the optimal configuration and operation control of user energy storage systems based on time-of-use electricity prices, and does not consider the user's own carbon emissions and the relationship between the two value curves of time-of-use electricity prices and carbon emission factors, as well as how the energy storage system optimizes the charging and discharging power under two different value curves. Summary of the invention

[0004] In order to solve the problem that the existing energy storage system only considers the operation strategy of a single basis such as time-of-use electricity price, the present invention provides two energy storage charging and discharging strategy formulation methods under time-varying prices, including:

[0005] Obtain the original load curve of power users, time-of-use electricity price curve, time-varying curve of carbon emission factor, carbon dioxide emission price and energy storage system capacity;

[0006] Input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the energy storage system capacity into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period;

[0007] Generate an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period;

[0008] The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, and constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

[0009] Preferably, the construction of the energy storage system charging and discharging strategy optimization model includes:

[0010] The equivalent kilowatt-hour carbon price curve is formed by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price;

[0011] The equivalent value curve is obtained by superimposing the equivalent value kWh carbon price curve and the time-of-use electricity price curve;

[0012] Based on the equivalent value curve, an objective function is constructed with the goal of minimizing the user's daily comprehensive cost;

[0013] The constraints are power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint;

[0014] An energy storage system charging and discharging strategy optimization model is constructed based on the objective function and the constraint conditions.

[0015] Preferably, constructing an objective function based on the equivalent value curve with the goal of minimizing the user's daily comprehensive cost includes:

[0016] Calculate the total of the user's electricity fee and carbon emission fee based on the equivalent value curve, the power user's original load curve and the energy storage system capacity;

[0017] Calculate the user's basic electricity fee converted to a single-day cost based on the energy storage system capacity and the power user's original load curve;

[0018] Calculate the annual investment cost of energy storage and the operation and maintenance costs based on the capacity of the energy storage system and convert them into daily costs;

[0019] The objective function is determined based on the sum of the user's electricity charges and carbon emission costs, the user's basic electricity charges converted to a single day's cost, the annual investment cost of energy storage, and the cost of operating and maintenance costs converted to a single day.

[0020] Preferably, the objective function is as follows:

[0021] min F1=C1+C2+C3;

[0022] In the formula, F1 is the total daily cost; C1 is the sum of electricity charges and carbon emission costs; C2 is the user's basic electricity charge converted to a single day's cost; C3 is the annual investment cost of energy storage and operation and maintenance costs converted to a single day's cost;

[0023] Preferably, the sum of the user's electricity fee and carbon emission fee is calculated as follows:

[0024]

[0025] Where C1 is the sum of electricity charges and carbon emission charges; Jt It is the equivalent value formed by the superposition of carbon emission price and time-of-use electricity price; P L,t is the user's load at time t; t is time; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; Δt is the charging and discharging time.

[0026] Preferably, the power balance constraint is as follows:

[0027] P grid =P load +P bess ;

[0028] Where P grid Purchase power from the power grid for users; P load is the user's load power; P bess is the charging and discharging power of the energy storage.

[0029] Preferably, the energy storage charge state continuity constraint is as follows:

[0030]

[0031] In the formula, S t is the remaining energy storage capacity at time t; S t+1 is the remaining energy storage capacity at time t+1; Δt is the charging and discharging time; is the charging and discharging efficiency of energy storage; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; E is the rated capacity of the energy storage.

[0032] Based on the same inventive concept, the present invention also provides two energy storage charging and discharging strategy formulation systems under time-varying prices, including:

[0033] An acquisition module is used to obtain the original load curve of power users, the time-of-use electricity price curve, the time-varying curve of carbon emission factors, the carbon dioxide emission price and the capacity of the energy storage system;

[0034] A calculation module is used to input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the capacity of the energy storage system into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period;

[0035] A strategy generation module, used to generate an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period;

[0036] The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

[0037] Preferably, the system further includes an energy storage system charging and discharging strategy optimization model building module:

[0038] The energy storage system charging and discharging strategy optimization model building module includes:

[0039] The curve generation submodule is used to form an equivalent carbon price curve per kilowatt-hour by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price;

[0040] A superposition submodule, used for superimposing the equivalent value kWh carbon price curve with the time-of-use electricity price curve to obtain an equivalent value curve;

[0041] An objective function construction submodule, used to construct an objective function based on the equivalent value curve with the goal of minimizing the daily comprehensive cost of the user;

[0042] A constraint submodule, used to use power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint as constraint conditions;

[0043] A construction submodule is used to construct an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

[0044] Preferably, the objective function construction submodule includes:

[0045] Calculate the equivalent value curve based on the time-of-use electricity price curve, the carbon emission factor time-varying curve, and the carbon dioxide emission price;

[0046] Calculate the total of the user's electricity fee and carbon emission fee based on the equivalent value curve, the power user's original load curve and the energy storage system capacity;

[0047] Calculate the user's basic electricity fee converted to a single-day cost based on the energy storage system capacity and the power user's original load curve;

[0048] Calculate the annual investment cost of energy storage and the operation and maintenance costs based on the capacity of the energy storage system and convert them into daily costs;

[0049] The objective function is determined based on the sum of the user's electricity charges and carbon emission costs, the user's basic electricity charges converted to a single day's cost, the annual investment cost of energy storage, and the cost of operating and maintenance costs converted to a single day.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] The present invention provides a method for formulating energy storage charging and discharging strategies under two time-varying prices, including: obtaining an original load curve of an electricity user, a time-of-use electricity price curve, a time-varying curve of a carbon emission factor, a carbon dioxide emission price, and an energy storage system capacity; inputting the original load curve of the electricity user, the time-of-use electricity price curve, the time-varying curve of a carbon emission factor, a carbon dioxide emission price, and the capacity of an energy storage system into a pre-constructed energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period; generating an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period; wherein the energy storage system charging and discharging strategy optimization model considers two different price curves, namely, a time-of-use electricity price curve and a carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function. The present invention establishes an energy storage system charging and discharging strategy optimization model based on two different price curves of time-of-use electricity price and carbon emission factor, and formulates a charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period output by the energy storage system charging and discharging strategy optimization model. It takes into account the relationship between the user himself and the two curves of time-of-use electricity price and carbon emission factor, as well as the optimal control of the charging and discharging power of the energy storage system under the two curves, which solves the problem that current research mainly considers the optimal configuration and operation control of the user's energy storage system based on time-of-use electricity price, and can provide suggestions for power users who configure energy storage systems to reasonably formulate energy storage charging and discharging strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a flow chart of a method for formulating energy storage charging and discharging strategies under two time-varying prices provided by the present invention;

[0053] Figure 2 It is the overall flow chart of the present invention;

[0054] Figure 3 It is the IEEE33 node improvement system diagram of the present invention;

[0055] Figure 4 It is the photovoltaic output and typical load curve of industrial users of the present invention;

[0056] Figure 5 is a system equivalent load curve diagram of the present invention;

[0057] Figure 6 It is the "equivalent value" curve diagram of scenario 1 of the present invention;

[0058] Figure 7 It is the “equivalent value” curve diagram of scenario 2 of the present invention;

[0059] Figure 8 is a charge and discharge strategy diagram of the energy storage system in scenario 1 of the present invention;

[0060] Fig. 9is a scene 1 equivalent load curve diagram of the present invention;

[0061] Fig.10 is a charge and discharge strategy diagram of the energy storage system in scenario 2 of the present invention;

[0062] Fig.11 This is the equivalent load curve diagram of scenario 2 of the present invention. DETAILED DESCRIPTION

[0063] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and examples.

[0064] Embodiment 1:

[0065] The present invention provides two energy storage charging and discharging strategy formulation methods under time-varying prices, such as Figure 1 As shown, including:

[0066] Step 1: Obtain the original load curve of power users, time-of-use electricity price curve, carbon emission factor time-varying curve, carbon dioxide emission price and energy storage system capacity;

[0067] Step 2: Input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the energy storage system capacity into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period;

[0068] Step 3: generating an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period;

[0069] Among them, the energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the time-varying carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

[0070] The energy storage system charging and discharging strategy optimization model mentioned in this embodiment considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, and constructs an objective function with the goal of minimizing the user's daily comprehensive cost. Constraints are set for the objective function. Here, the construction of the energy storage system charging and discharging strategy optimization model is first introduced:

[0071] The construction process of the energy storage system charging and discharging strategy optimization model is as follows:

[0072] Step A1: multiply the carbon emission factor time-varying curve by the carbon dioxide emission price to form an equivalent kilowatt-hour carbon price curve;

[0073] Step A2: superimposing the equivalent value kWh carbon price curve with the time-of-use electricity price curve to obtain an equivalent value curve;

[0074] Step A3 constructs an objective function based on the equivalent value curve with the goal of minimizing the user's daily comprehensive cost;

[0075] Step A4 uses power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint as constraint conditions;

[0076] Step A5 constructs an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

[0077] Furthermore, in step A1, the carbon emission factor time-varying curve is formed by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price to form an equivalent kilowatt-hour carbon price curve, which specifically includes:

[0078] According to the concept of "equivalent value" proposed in the present invention, the carbon emission factor time-varying curve can be multiplied by the unit carbon emission price to form a carbon price curve. The carbon emission factor mentioned in this embodiment is in units of tCO2 / k, the carbon dioxide emission price is in units of ￥ / tCO2, and the equivalent carbon price per kilowatt-hour is in units of ￥ / kWh.

[0079] Furthermore, in step A2, the equivalent value curve is obtained by superimposing the equivalent value per kWh carbon price curve and the time-of-use electricity price curve, which specifically includes:

[0080] The carbon price curve is then superimposed on the time-of-use electricity price curve to form a new equivalent value curve. Finally, the charging and discharging strategy of the energy storage system is optimized based on the equivalent value curve.

[0081] Furthermore, step A3 constructs an objective function based on the equivalent value curve with the goal of minimizing the user's daily comprehensive cost, specifically including:

[0082] An optimization model for the charging and discharging strategy of the energy storage system is established. The objective function is to minimize the user's daily comprehensive cost. The energy storage capacity is known, and the charging and discharging strategy of the energy storage is optimized.

[0083] The user's daily comprehensive cost includes comprehensive costs (the sum of electricity charges and carbon emission costs), basic electricity charges, energy storage converted to daily investment costs and operation and maintenance costs:

[0084] minF1=C1+C2+C3;

[0085] In the formula: F1 is the total daily cost; C1 is the sum of electricity charges and carbon emission costs; C2 is the user's basic electricity charge converted to a single day's cost; C3 is the annual investment cost of energy storage and operation and maintenance costs converted to a single day's cost.

[0086] The equivalent value curve mentioned in this embodiment constructs an objective function with the goal of minimizing the user's daily comprehensive cost. The objective function is constructed as follows:

[0087] The total of the user's electricity fee and carbon emission fee is calculated based on the equivalent value curve, the original load curve of the power user and the capacity of the energy storage system, including:

[0088] The total of the user's electricity bill and carbon emission costs is as follows:

[0089]

[0090] Where: C1 is the sum of electricity charges and carbon emission charges; J t It is the equivalent value formed by the superposition of carbon emission price and time-of-use electricity price; P L,t is the user's load at time t; t is time; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; Δt is the charging and discharging time.

[0091] The cost of converting the user's basic electricity fee to a single day is calculated based on the energy storage system capacity and the user's original load curve, including:

[0092] The basic electricity fee of the user converted into the cost per day is shown below. The basic electricity fee of industrial and commercial users can be calculated based on the transformer capacity or the maximum demand. The present invention adopts the maximum demand for calculation.

[0093]

[0094] Where: C2 is the user's basic electricity fee converted to a single day's cost; n r P is the user unit demand electricity fee; max is the actual maximum demand value; P c It is 1.05 times the approved maximum demand value.

[0095] The annual investment cost of energy storage and the operation and maintenance costs are calculated based on the capacity of the energy storage system and converted to the cost per day, including:

[0096] The annual investment cost and operation and maintenance cost of energy storage converted to daily costs are shown below:

[0097]

[0098] Where: C3 is the annual investment cost of energy storage and the operation and maintenance cost converted to a single day; C'3 is the investment cost of the energy storage battery; C'3' is the energy storage operation and maintenance cost.

[0099]

[0100] Where: C'3 is the investment cost of energy storage battery; a e is the unit capacity cost of energy storage; b eis the unit power cost of energy storage; E is the rated capacity of energy storage; P is the rated power of energy storage; r and Y are the annual discount rate and the service life of energy storage, respectively.

[0101] C″3=b p P;

[0102] Where: C″3 is the energy storage operation and maintenance cost; b p is the annual operation and maintenance cost per unit power of energy storage; P is the rated power of energy storage.

[0103] The objective function is determined by the sum of the user's electricity bill and carbon emission costs, the user's basic electricity bill converted to a single day's cost, the annual investment cost of energy storage, and the cost of operating and maintenance costs converted to a single day.

[0104] Furthermore, in step A4, power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint are used as constraint conditions, specifically including:

[0105] The constraints are as follows:

[0106] a) Power balance constraints:

[0107] P grid =P load +P bess ;

[0108] Where: P grid Purchase power from the power grid for users; P load is the user's load power; P bess is the charging and discharging power of energy storage.

[0109] b) Energy storage charge state continuity constraints:

[0110]

[0111] Where: S t is the remaining energy storage capacity at time t; S t+1 is the remaining energy storage capacity at time t+1; Δt is the charging and discharging time; is the charging and discharging efficiency of energy storage; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; E is the rated capacity of the energy storage.

[0112] c) Energy storage state of charge constraints:

[0113] To avoid overcharging or over-discharging of the battery that affects its life, the charge state is limited to a certain range. The battery cannot be fully discharged or fully charged.

[0114] SOC(t) min≤SOC(t)≤SOC(t) max ;

[0115] Where: SOC(t) min 、SOC(t) max They represent the upper and lower limits of the energy storage system's state of charge respectively; SOC(t) is the energy storage system's state of charge at time t.

[0116] d) Energy storage power constraints:

[0117] The charging and discharging power of the energy storage system cannot exceed the upper and lower power limits allowed by the system:

[0118]

[0119] Where: P dis,t is the discharge power of energy storage at time t; P c,t is the charging power P of the energy storage at time t c,max , P d,max They are the maximum charging power and maximum discharging power of the energy storage system respectively.

[0120] e) Energy storage charging and discharging state constraints:

[0121] The energy storage system can only be charged or discharged at the same time.

[0122] B c,t,d +B dis,t,d ≤1;

[0123] Where: B c,t,d and B dis,t,d is a variable with a value of 0 or 1, which represents the charge and discharge status at time t on day d. c,t,d 1 means it is in charging state. dis,t,d A value of 1 indicates that the device is in the discharging state.

[0124] Step A5 constructs an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

[0125] The best application scenario of the "equivalent value" mentioned in this embodiment should be the situation where two or more value curves that change with time are basically the same in order of magnitude. Taking the user energy storage system in the present invention as an example, considering that users arbitrage through low storage and high discharge means, they formulate energy storage charging and discharging strategies based on time-of-use electricity prices, and users formulate energy storage charging and discharging strategies based on carbon emission factors in order to reduce carbon emissions. Assuming that after several years, due to the continuous increase in the power industry's demand for "carbon reduction", the carbon emission prices in different time periods are already at the same order of magnitude as the overall time-of-use electricity price. Therefore, users who configure energy storage systems should take into account energy storage peak-valley arbitrage and "carbon reduction" needs to formulate charging and discharging strategies. At this time, it is necessary to consider both the time-of-use electricity price curve and the carbon emission factor time-varying curve.

[0126] In step 1, the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the energy storage system capacity are obtained, including:

[0127] Obtain the user's load at a certain moment, the energy storage discharge power, the energy storage charging power, the user's unit demand electricity fee, the energy storage battery investment cost, the energy storage operation and maintenance cost, the energy storage unit capacity cost and the energy storage rated capacity, etc.

[0128] In step 2, the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the energy storage system capacity are input into the pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period, including:

[0129] According to the above data, the corresponding variables in the energy storage system charging and discharging strategy optimization model constructed by the present invention are assigned values, the charging and discharging power of the energy storage system in each time period under the arbitrage and carbon reduction goals is calculated, the energy storage system charging and discharging strategy optimization model is solved and the charging power or discharging power of the energy storage system in each time period is output.

[0130] In step 3, a storage charging and discharging strategy is generated for the charging power or discharging power of the energy storage system in each time period. Figure 2 shown.

[0131] Embodiment 2:

[0132] Based on the same inventive concept, the present invention also provides two energy storage charging and discharging strategy formulation systems under time-varying prices, including:

[0133] An acquisition module is used to obtain the original load curve of power users, the time-of-use electricity price curve, the time-varying curve of carbon emission factors, the carbon dioxide emission price and the capacity of the energy storage system;

[0134] A calculation module is used to input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the capacity of the energy storage system into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period;

[0135] A strategy generation module, used to generate an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period;

[0136] The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, and constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

[0137] The energy storage charging and discharging strategy formulation system based on two time-varying prices also includes an energy storage system charging and discharging strategy optimization model construction module, which is used to construct an energy storage system charging and discharging strategy optimization model.

[0138] The energy storage system charging and discharging strategy optimization model construction module is specifically used for:

[0139] The curve generation submodule is used to form an equivalent carbon price curve per kilowatt-hour by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price;

[0140] A superposition submodule, used for superimposing the equivalent value kWh carbon price curve with the time-of-use electricity price curve to obtain an equivalent value curve;

[0141] An objective function construction submodule, used to construct an objective function based on the equivalent value curve with the goal of minimizing the daily comprehensive cost of the user;

[0142] A constraint submodule, used to use power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint as constraint conditions;

[0143] A construction submodule is used to construct an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

[0144] Furthermore, the curve generation submodule is specifically used for:

[0145] According to the concept of "equivalent value" proposed in the present invention, the carbon emission factor time-varying curve can be multiplied by the unit carbon emission price to form a carbon price curve.

[0146] Furthermore, the superposition submodule is specifically used for:

[0147] Based on the formed carbon price curve, it is superimposed with the time-of-use electricity price curve to form a new equivalent value curve.

[0148] Furthermore, the objective function construction submodule is specifically used for:

[0149] An optimization model for the charging and discharging strategy of the energy storage system is established. The objective function is to minimize the user's daily comprehensive cost. The energy storage capacity is known, and the charging and discharging strategy of the energy storage is optimized.

[0150] The user's daily comprehensive cost includes comprehensive costs (the sum of electricity charges and carbon emission costs), basic electricity charges, energy storage converted to daily investment costs and operation and maintenance costs, as shown below.

[0151] minF1=C1+C2+C3;

[0152] Where: F1 is the total daily cost; C1 is the sum of electricity charges and carbon emission costs; C2 is the user's basic electricity charge converted to a single day's cost; C3 is the annual investment cost of energy storage and operation and maintenance costs converted to a single day's cost;

[0153] The total electricity and carbon emissions charges for the user are shown below.

[0154]

[0155] Where: C1 is the sum of electricity charges and carbon emission charges; J t It is the equivalent value formed by the superposition of carbon emission price and time-of-use electricity price; P L,t is the user's load at time t; t is time; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; Δt is the charging and discharging time.

[0156] The basic electricity fee of the user converted into the cost per day is shown below. The basic electricity fee of industrial and commercial users can be calculated based on the transformer capacity or the maximum demand. The present invention adopts the maximum demand for calculation.

[0157]

[0158] Where: C2 is the user's basic electricity fee converted to a single day's cost; n r P is the user unit demand electricity fee; max is the actual maximum demand value; P c It is 1.05 times the approved maximum demand value.

[0159] The annual investment cost of energy storage and operation and maintenance costs converted to daily costs are shown below:

[0160]

[0161] Where: C3 is the annual investment cost of energy storage and the operation and maintenance cost converted to a single day; C'3 is the investment cost of the energy storage battery; C″3 is the energy storage operation and maintenance cost.

[0162]

[0163] Where: C'3 is the investment cost of energy storage battery; a e is the unit capacity cost of energy storage; b e is the unit power cost of energy storage; E is the rated capacity of energy storage; P is the rated power of energy storage; r and Y are the annual discount rate and the service life of energy storage, respectively.

[0164] C″3=b p P;

[0165] Where: C″3 is the energy storage operation and maintenance cost; b p is the annual operation and maintenance cost per unit power of energy storage; P is the rated power of energy storage.

[0166] Furthermore, the constraint submodule is specifically used for:

[0167] Constraints:

[0168] a) Power balance constraints

[0169] P grid =P load +P bess ;

[0170] Where: P grid Purchase power from the power grid for users; P load is the user's load power; P bess is the charging and discharging power of energy storage.

[0171] b) Energy storage charge state continuity constraints

[0172]

[0173] Where: S t is the remaining energy storage capacity at time t; S t+1 is the remaining energy storage capacity at time t+1; Δt is the charging and discharging time; is the charging and discharging efficiency of energy storage; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; E is the rated capacity of the energy storage.

[0174] c) Energy storage state of charge constraints

[0175] To avoid overcharging or over-discharging of the battery that affects its life, the charge state is limited to a certain range. The battery cannot be fully discharged or fully charged.

[0176] SOC(t) min ≤SOC(t)≤SOC(t) max ;

[0177] Where: SOC(t) min 、SOC(t) max They represent the upper and lower limits of the energy storage system's state of charge respectively; SOC(t) is the energy storage system's state of charge at time t.

[0178] d) Energy storage power constraints

[0179] The charging and discharging power of the energy storage system cannot exceed the upper and lower power limits allowed by the system.

[0180]

[0181] Where: P dis,t is the discharge power of energy storage at time t; P c,t is the charging power P of the energy storage at time t c,max , P d,max They are the maximum charging power and maximum discharging power of the energy storage system respectively.

[0182] e) Energy storage charging and discharging state constraints

[0183] The energy storage system can only be charged or discharged at the same time.

[0184] B c,t,d +B dis,t,d ≤1;

[0185] Where: B c,t,d and B dis,t,d is a variable with a value of 0 or 1, which represents the charge and discharge status at time t on day d. c,t,d 1 means it is in charging state. dis,t,d A value of 1 indicates that the device is in the discharging state.

[0186] A construction submodule is used to construct an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

[0187] The acquisition module is specifically used for:

[0188] Obtain the user's load at a certain moment, the energy storage discharge power, the energy storage charging power, the user's unit demand electricity fee, the energy storage battery investment cost, the energy storage operation and maintenance cost, the energy storage unit capacity cost and the energy storage rated capacity, etc.

[0189] The computing module is specifically used for:

[0190] According to the above data, the corresponding variables in the energy storage system charging and discharging strategy optimization model constructed by the present invention are assigned values, the charging and discharging power of the energy storage system in each time period under the arbitrage and carbon reduction goals is calculated, the energy storage system charging and discharging strategy optimization model is solved and the charging power or discharging power of the energy storage system in each time period is output.

[0191] The strategy generation module is specifically used for:

[0192] According to the charging power or discharging power of the energy storage system in each time period output by the energy storage system charging and discharging strategy optimization model, the optimal charging and discharging strategy of the energy storage system configured by the user under the guidance of two different prices is obtained.

[0193] The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

[0194] Embodiment 3:

[0195] The present invention uses the improved IEEE33 node system as a simulation example to further introduce the energy storage charging and discharging strategy formulation method under two time-varying prices. Figure 3 As shown. The carbon emission intensity of node 1 is 875g / kWh. The gas turbine outputs at nodes 5 and 14 are 600kW and 500kW, respectively, and their carbon emission intensities are 525g / kWh and 500g / kWh, respectively. The outputs of the photovoltaic units at nodes 20 and 24 are 200kW and 400kW, respectively, and the output of the wind turbine unit at node 28 is 300kW. Since both photovoltaic and wind power are green energy, their carbon emission intensity is 0. The three industrial users connected at nodes 30, 31, and 32 are willing to jointly build photovoltaic energy storage. In order to meet their own electricity needs, they cooperate to build a photovoltaic power station with an installed capacity of 1200kW and an energy storage system with an installed capacity of 3000kW. The power load curves of industrial users and photovoltaic output are shown in the figure. Figure 4 As shown, the total load curves of the three industrial users are as follows Figure 5 The time-of-use electricity price in a certain area is shown in Table 1, and the unit price of demand electricity fee is 38 yuan / kW.

[0196] Table 1 Time-of-use electricity prices in a certain area

[0197]

[0198] The energy storage system uses lithium iron phosphate batteries with an initial SOC state of 0.2. Its service life is 12 years at a discharge depth of 0.5. Its relevant parameters are shown in Table 2.

[0199] Table 2 Lithium iron phosphate battery parameters

[0200]

[0201] According to the carbon flow calculation method, the carbon potential of the distribution network nodes can be obtained. In order to compare and analyze the "equivalent value" generated by the superposition of two different trends of carbon emission factors and time-of-use electricity prices, two scenarios are set in this application example, namely scenario one and scenario two. The time-of-use electricity prices and carbon unit prices used in the two scenarios are the same, but the peak and valley change trends of the carbon emission factor time-varying curves of the two scenarios are completely opposite. The dynamic carbon emission factor of scenario one is shown in Table 3. Figure 6 This is the “equivalent value” curve after the dynamic carbon emission factor of scenario 1 is superimposed with the time-of-use electricity price.

[0202] Table 3 Dynamic carbon emission factors for scenario 1

[0203]

[0204] Depend on Figure 6 It can be seen that the dynamic carbon emission factor of scenario 1 is approximately inversely related to the peak and valley of the time-of-use electricity price. The carbon emission factor is at a high level during the valley electricity price, and at a low level during the peak electricity price. This is caused by the fact that photovoltaic power generation has sufficient output during the daytime period of 10-15 o'clock, and the carbon emission factor is low; photovoltaic output is weakened during the period of 15-20 o'clock, and the load increases, and the carbon emission factor increases; wind power generation is sufficient from 21:00 to 23:00 in the evening, and the carbon emission factor is low; and at night, in order to meet the electricity demand of users, it is necessary to partially rely on fossil energy for power generation, so the carbon emission intensity is higher at night. By analyzing the "equivalent value" curve, it can be seen that compared with the original time-of-use electricity price curve, the peak-to-valley difference of the "equivalent value" curve is significantly reduced. This situation compresses the "peak-to-valley arbitrage" interval of the energy storage system, which is not conducive to the profit of the energy storage system.

[0205] The dynamic carbon emission factors for scenario 2 are shown in Table 4. Figure 7 This is the “equivalent value” curve after the dynamic carbon emission factor and time-of-use electricity price are superimposed in scenario two.

[0206] Table 4 Dynamic carbon emission factors for scenario 2

[0207]

[0208]

[0209] Depend on Figure 7It can be seen that the dynamic carbon emission factor of scenario 2 is approximately in phase with the peak and valley of the time-of-use electricity price. The carbon emission factor is also at a low level during the valley electricity price, and at a high level during the peak electricity price. This is because it is assumed that the output of photovoltaic power generation is insufficient during the day due to weather or other reasons, resulting in a higher carbon emission factor; the output of other distributed power sources increases during 15-20 o'clock, and the carbon emission factor decreases; in the evening period, in order to meet the electricity demand of users, it is still necessary to rely on fossil energy for power generation, so the carbon emission intensity is higher at night; the electricity consumption of users decreases during the night period, and the carbon emission factor is lower. By analyzing the "equivalent value" curve, it can be seen that compared with the original time-of-use electricity price curve, the peak-to-valley difference of the "equivalent value" curve has increased significantly. This situation increases the "peak-to-valley arbitrage" interval of the energy storage system, which is beneficial to the profit of the energy storage system.

[0210] Since the present invention is intended to reflect a method for formulating a storage charging and discharging strategy for an energy storage system under two different price guidances, namely, the "equivalent value" guidance, the energy storage capacity is a known condition, and the energy storage charging and discharging strategy is formulated under the "equivalent value" of scenario one and scenario two, respectively, and the Yalmip solver is used to calculate the above embodiment.

[0211] Scenario 1: Energy storage system charging and discharging strategy and equivalent load curve Figure 8 , Fig. 9 As shown;

[0212] Figure 8 , Fig.10 These are the energy storage charging and discharging power and equivalent value relationships for scenario one and scenario two, respectively. It can be seen that the energy storage system performs two charging and discharging cycles in a day, and charges during valley and normal periods, and discharges during peak and peak periods. Fig. 9 , Fig.11 The equivalent load curve after optimizing the energy storage charging and discharging strategy for industrial users shows that the load curve after formulating the energy storage charging and discharging strategy in Scenario 2 changes more significantly than that in Scenario 1. This is because the peak-to-valley difference of the equivalent value curve in Scenario 2 is larger, which is conducive to energy storage arbitrage, so the demand for energy storage charging and discharging power is larger, and the load curve changes more during valley electricity price periods and peak electricity price periods. In Scenario 1, due to the smaller peak-to-valley difference of the equivalent value, the space for energy storage to play is limited, so the user load changes less.

[0213] The optimal charging and discharging power of the energy storage system for scenario 1 and scenario 2, the user's annual carbon emission cost, annual electricity cost, annual demand electricity cost and annual total cost are shown in Table 5.

[0214] Table 5 Results of energy storage operation optimization configuration in different regions

[0215]

[0216]

[0217] Through analysis, it can be found that the total annual cost of scenario 2 is lower than that of scenario 1, and the carbon emission cost of scenario 1 is significantly higher than that of scenario 2. This is because the carbon emission factor time-varying curve of scenario 1 has the opposite trend to the time-of-use electricity price curve, so the peak-to-valley difference of the "equivalent value" formed after superposition is reduced, resulting in the "arbitrage" effect of the energy storage system being limited, the optimal power of energy storage being low, and the energy storage utilization rate being low; while the carbon emission factor time-varying curve of scenario 2 has the same trend as the time-of-use electricity price curve, so the peak-to-valley difference of the "equivalent value" formed after superposition is increased, the energy storage system can easily make a profit through arbitrage, the optimal power of energy storage is high, and the energy storage utilization rate is high. Therefore, when formulating energy storage operation strategies by comprehensively considering the time-of-use electricity price and the carbon emission factor time-varying curve, the divergence and convergence between the two should be fully considered.

[0218] Obviously, the described embodiments are only some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0219] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0220] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0221] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0222] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0223] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. Two energy storage charging and discharging strategy formulation methods under time-varying prices, characterized by ,include: Obtain the original load curve of power users, time-of-use electricity price curve, time-varying curve of carbon emission factor, carbon dioxide emission price and energy storage system capacity; Input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the energy storage system capacity into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period; Generate an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period; The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

2. The method according to claim 1, characterized in that The construction of the energy storage system charging and discharging strategy optimization model includes: The equivalent kilowatt-hour carbon price curve is formed by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price; The equivalent value curve is obtained by superimposing the equivalent value kWh carbon price curve and the time-of-use electricity price curve; Based on the equivalent value curve, an objective function is constructed with the goal of minimizing the user's daily comprehensive cost; The constraints are power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint; An energy storage system charging and discharging strategy optimization model is constructed based on the objective function and the constraint conditions.

3. The method according to claim 2, characterized in that The objective function is constructed based on the equivalent value curve with the goal of minimizing the daily comprehensive cost of the user, including: Calculate the total of the user's electricity fee and carbon emission fee based on the equivalent value curve, the power user's original load curve and the energy storage system capacity; Calculate the user's basic electricity fee converted to a single-day cost based on the energy storage system capacity and the power user's original load curve; Calculate the annual investment cost of energy storage and the operation and maintenance costs based on the capacity of the energy storage system and convert them into daily costs; The objective function is determined based on the sum of the user's electricity charges and carbon emission costs, the user's basic electricity charges converted to a single day's cost, the annual investment cost of energy storage, and the cost of operating and maintenance costs converted to a single day.

4. The method according to claim 1, characterized in that The objective function is shown in the following formula: minF1=C1+C2+C3; In the formula, F1 is the total daily cost; C1 is the sum of electricity charges and carbon emission costs; C2 is the user's basic electricity charge converted to a single day's cost; C3 is the annual investment cost of energy storage and operation and maintenance costs converted to a single day's cost.

5. The method according to claim 2, characterized in that The sum of the user's electricity charges and carbon emission charges is calculated as follows: Where C1 is the sum of electricity charges and carbon emission costs; J t It is the equivalent value formed by the superposition of carbon emission price and time-of-use electricity price; P L,t is the user's load at time t; t is time; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; Δt is the charging and discharging time.

6. The method according to claim 5, characterized in that The power balance constraint is as follows: P grid =P load +P bess ; Where P grid Purchase power from the power grid for users; P load is the user's load power; P bess is the charging and discharging power of energy storage.

7. The method according to claim 6, characterized in that The energy storage charge state continuity constraint is as follows: In the formula, S t is the remaining energy storage capacity at time t; S t+1 is the remaining energy storage capacity at time t+1; Δt is the charging and discharging time; is the charging and discharging efficiency of energy storage; P dis,t is the discharge power of energy storage at time t; P c,t is the charging power of the energy storage at time t; E is the rated capacity of the energy storage.

8. Two energy storage charging and discharging strategy formulation systems under time-varying prices, characterized in that: include: An acquisition module is used to obtain the original load curve of power users, the time-of-use electricity price curve, the time-varying curve of carbon emission factors, the carbon dioxide emission price and the capacity of the energy storage system; A calculation module is used to input the original load curve of the power user, the time-of-use electricity price curve, the time-varying curve of the carbon emission factor, the carbon dioxide emission price and the capacity of the energy storage system into a pre-built energy storage system charging and discharging strategy optimization model to obtain the charging power or discharging power of the energy storage system in each time period; A strategy generation module, used to generate an energy storage charging and discharging strategy based on the charging power or discharging power of the energy storage system in each time period; The energy storage system charging and discharging strategy optimization model considers two different price curves, namely the time-of-use electricity price curve and the carbon emission factor, constructs an objective function with the goal of minimizing the user's daily comprehensive cost, and sets constraints for the objective function.

9. The system according to claim 8, characterized in that The system also includes a module for constructing an energy storage system charging and discharging strategy optimization model: The energy storage system charging and discharging strategy optimization model building module includes: The curve generation submodule is used to form an equivalent carbon price curve per kilowatt-hour by multiplying the carbon emission factor time-varying curve by the carbon dioxide emission price; A superposition submodule, used for superimposing the equivalent value kWh carbon price curve with the time-of-use electricity price curve to obtain an equivalent value curve; An objective function construction submodule, for constructing an objective function based on the equivalent value curve with the goal of minimizing the daily comprehensive cost of the user; A constraint submodule, used to use power balance constraint, energy storage charge state continuity constraint, energy storage charge state constraint, energy storage power constraint and energy storage charge and discharge state constraint as constraint conditions; A construction submodule is used to construct an energy storage system charging and discharging strategy optimization model based on the objective function and the constraint conditions.

10. The system according to claim 9, characterized in that The objective function construction submodule includes: Calculate the equivalent value curve based on the time-of-use electricity price curve, the carbon emission factor time-varying curve, and the carbon dioxide emission price; Calculate the total of the user's electricity fee and carbon emission fee based on the equivalent value curve, the power user's original load curve and the energy storage system capacity; Calculate the user's basic electricity fee converted to a single-day cost based on the energy storage system capacity and the power user's original load curve; Calculate the annual investment cost of energy storage and the operation and maintenance costs based on the capacity of the energy storage system and convert them into daily costs; The objective function is determined based on the sum of the user's electricity charges and carbon emission costs, the user's basic electricity charges converted to a single day's cost, the annual investment cost of energy storage, and the cost of operating and maintenance costs converted to a single day.

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