CN111325423A - A regional multi-energy interconnection operation optimization method and computing device - Google Patents

Abstract

The invention discloses a regional multi-energy interconnection operation optimization method, which is executed in computing equipment and comprises the following steps: establishing a battery operation cost model, wherein the cost model comprises battery electricity quantity price and battery electricity quantity consumption in the charging and discharging process; constructing a random unit combination model of the microgrid based on the cost model, wherein the combination model comprises a constraint condition and an objective function, and the objective function is the minimum expected operation cost in a time period; and solving the combination model by adopting a preset method to obtain optimal unit combination parameters, and combining the units according to an optimal result to realize regional multi-energy interconnection operation optimization. The invention also discloses a computing device for executing the operation optimization method.

Description

A regional multi-energy interconnection operation optimization method and computing device

technical field

The invention relates to the field of power systems, in particular to a method and computing device for optimizing the operation of regional multi-energy interconnection.

Background technique

With the rapid growth of the domestic economy, the society's demand for electricity is growing, and the infrastructure investment in power grids will also increase. Among them, the application and research of energy interconnection microgrids will also increase. In the "Energy Interconnection Microgrid System Supply and Demand Bilateral Multi-Energy Collaborative Optimization Strategy and Its Solution Algorithm", considering the influence between the supply side, the demand side and energy conversion, the energy interconnection microgrid system is constructed. For the optimization strategy model, a combined algorithm combining ant colony algorithm with individual differences and particle swarm optimization algorithm for solving mixed integer nonlinear programming model is proposed. "Unified Planning Model and Benders Decoupling Method for Electric Power-Gas Integrated Energy System" studies the unified planning problem of electric-gas integrated energy system considering boundary conditions constraints of electric power system and natural gas system. , the location and constant volume of natural gas pipelines are optimized, and a mixed integer non-convex nonlinear programming model is constructed; then, the mixed integer non-convex nonlinear programming problem is simplified into a two-layer main and sub-problems by using Benders decoupling, and respectively The high-efficiency commercial solvers CPLEX and IPOPT are used to iteratively solve the problem; finally, the developed unified planning based on Benders decoupling is illustrated using the constructed electricity-gas integrated energy system including a 54-node power system and a 19-node natural gas network coupled with each other. the feasibility of the model.

However, most existing research is currently done on the basis of "scenario-based stochastic programming", an approach based on replicated deterministic models generated under Monte Carlo simulation scenarios. The computational burden in this method grows exponentially as the number of investigated scenarios increases. Using a different technique to reduce scenarios can alleviate the computational problem, but this approach may ignore low-probability but high-impact cases.

SUMMARY OF THE INVENTION

To this end, the present invention provides a new regional multi-energy interconnection operation optimization method and computing device, so as to try to solve or at least alleviate the above problems.

According to one aspect of the present invention, a method for optimizing regional multi-energy interconnection operation is provided, executed in a computing device, the method includes: establishing a battery operation cost model, the cost model including battery power price and battery power consumption during charging and discharging; A random unit combination model of the microgrid is constructed based on the cost model. The combination model includes constraints and an objective function, and the objective function is that the expected operating cost of a time period is minimized. The combination model is solved by a predetermined method, and the result is obtained The optimal unit combination parameters, and the unit combination is carried out according to the optimal results to realize the optimization of regional multi-energy interconnection operation.

Optionally, in the method according to the present invention, the calculation formula of the battery price c _bat is:

表示用于电池充电的能源价格，

表示电池容量的可用成本，其指拥有1千瓦时的存储容量的可用成本，C_∑是电池的全生命周期容量，c_rep是重置成本。in,

represents the price of energy used to charge the battery,

Denotes the available cost of battery capacity, which refers to the available cost of having 1 kWh of storage capacity, _C∑ is the full life cycle capacity of the battery, and _crep is the replacement cost.

Optionally, in the method according to the invention, for lead-acid and lithium-ion batteries:

C _∑ =C _r DOD _r [L _r -0.2*(1+2+...+L _r )/L _r ]=C _r DOD _r (0.9L _r -0.1)(kWh)

For vanadium redox batteries: C _∑ = C _r DOD _r L _r (kWh),

where DOD _r is the depth of discharge, C _r is the rated capacity of the battery, and L _r is the rated life.

Optionally, in the method according to the present invention, the power consumption of the battery during the discharging process is the energy usage H _bat supplied to the load per unit time, and the battery power consumption during the charging process is the power loss L _bat of the charging battery per unit time, Its calculation formulas are:

是电池输出功率，

是放电功率损耗，

是电池输入功率，

是充电功率损耗。in,

is the battery output power,

is the discharge power loss,

is the battery input power,

is the charging power loss.

Optionally, in the method according to the invention, for lead-acid and lithium-ion batteries,

where SOC is the state of charge, V _r is the battery's rated voltage, Q _r is the battery's rated capacity, R is the internal ohmic resistance, and K is a constant calculated from the manufacturer's data.

Optionally, in the method according to the invention, for vanadium redox batteries,

和

分别是电池在放电期间和充电期间的堆栈电流，

分别是电池损失模型系数，其是与额定电压V_r或额定电流I_r有关的参数。where V _OC is the open circuit voltage of the battery,

and

are the stack current of the battery during discharge and charge, respectively,

are battery loss model coefficients, which are parameters related to rated voltage V _r or rated current I _r , respectively.

Optionally, in the method according to the invention,

其中

其中，F_k是时间段k内的总期望运营成本，S_k是时间段k内包括发电机的启动和停机成本的总过渡成本，N是时间范围，F_g,k，

分别代表时间段k内发电机、放电电池和充电电池的总运行费用，F_m,k是由于功率不匹配造成的成本。Optionally, in the method according to the present invention, the objective function is

in

where F _k is the total expected operating cost in time period k, _Sk is the total transition cost including generator start-up and shutdown costs in time period k, N is the time horizon, F _g,k ,

are the total operating costs of the generator, discharged battery and rechargeable battery in time period k, respectively, and F _m,k is the cost due to power mismatch.

Optionally, in the method according to the invention,

分别代表时间段k内发电机i和电池i的二进制状态，c_gi是发电机i的燃料价格，c_bi是电池i的电量价格，F_gi是发电机i的燃料成本，H_gi是发电机i的燃料消耗，

和

分别为放电和充电期间电池i的电量消耗，

和

分别为放电和充电期间电池i的运行成本，P_gi,k、

分别代表了时间段k内发电机i、放电电池和充电电池的发送功率，P_m,k是时间段k内由于功率失配造成的成本，F_m指由于功率不匹配造成的单位时间成本。where T is the time step, n ₁ and n ₂ represent the number of generators and batteries, respectively, _gi and b _i represent generator i and battery i, respectively, s _gi,k ,

represent the binary states of generator i and battery i in time period k, respectively, c _gi is the fuel price of generator i, c _bi is the electricity price of battery i, F _gi is the fuel cost of generator i, and H _gi is the generator i the fuel consumption of i,

and

are the power consumption of battery i during discharging and charging, respectively,

and

are the operating costs of battery i during discharge and charge, respectively, P _gi,k ,

represent the transmit power of generator i, discharged battery and rechargeable battery in time period k, respectively, P _m,k is the cost caused by power mismatch in time period k, and F _m is the unit time cost caused by power mismatch.

Optionally, in the method according to the invention,

是P_net,k的期望值。Among them, P _net,k is the net load in the k period, p _k is the probability that the net load is less than 0, E(y|x) is the expectation of y under the condition of x, c _ex,k is the electricity price exported to the grid, c _im,k is the price of electricity imported to the grid, α _k and β _k are parameters, where α _k is the probability level controlling the import/export of electricity from the grid to the microgrid, P _gen,k is the total power generation, and P _chg,k is the total charging cost,

is the expected value of P _net,k .

Optionally, in the method according to the invention,

When P _net,k ≥ 0, P _m,k =P _gen,k -P _net,k ,

When P _net,k <0, P _m,k =P _chg,k -P _net,k ,

Optionally, in the method according to the present invention, the constraints include at least one of the following:

和

分别是电池i充电状态的最小值和最大值，

和

分别是放电电池i发送功率的最小值和最大值，

和

分别是充电电池i发送功率的最小值和最大值，

是k时段发电机i在线时的发送功率，

和

分别是发电机i发送功率的最小值和最大值，

和

分别是k时段发电机i的上线时间和离线时间，

和

分别是发电机i上线时间和离线时间的最小值。Among them, P(x) is the probability of satisfying the condition of x, AND(a,b)=0 means that a and b cannot be 1 at the same time, SOC _bi,k is the state of charge of battery i in the k period,

and

are the minimum and maximum values of the state of charge of battery i, respectively,

and

are the minimum and maximum transmit power of the discharged battery i, respectively,

and

are the minimum and maximum values of the transmit power of the rechargeable battery i, respectively,

is the transmit power of generator i when it is online in period k,

and

are the minimum and maximum values of the power sent by generator i, respectively,

and

are the on-line time and off-line time of generator i in period k, respectively,

and

are the minimum values of generator i on-line time and off-line time, respectively.

Optionally, in the method according to the present invention, the constraint condition R1 is:

Among them, φ is the cumulative distribution function obeying the (0,1) standard normal distribution, σ _net,k is the standard deviation of the net load error ΔP _net,k , ΔP _net,k is the actual load error ΔP _load , the photovoltaic power generation error ΔP The sum of _pv and wind power error ΔP _WT , ΔP _load , ΔP _pv and ΔP _WT is the forecast error depending on the forecast method and forecast range.

其中，L_k是阶段k的可行状态集合，m_k是L_k集合中状态的数量，

是单元x_i的二进制状态，x_i代表发电机、放电电池或充电电池。Optionally, in the method according to the present invention, the predetermined method is a stochastic dynamic programming method, wherein the state space at stage k is

where L _k is the set of feasible states for stage k, m _k is the number of states in the L _k set,

is the binary state of cell _{xi ,} which represents generator, discharging battery or charging battery.

Optionally, in the method according to the present invention, the stochastic dynamic programming method is a forward stochastic dynamic programming method, wherein the minimum cost of reaching state A in phase B is:

是到达状态

的最小费用，

是对于状态

的运作费用，

是从状态

到状态

的过渡费用。in,

is the state of arrival

the minimum cost of

is for the state

operating costs,

is from the state

to state

transition costs.

According to one aspect of the present invention, there is provided a computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be adapted for execution by the at least one processor, the The program instructions include instructions for executing the method for optimizing the operation of a regional multi-energy interconnection as described above.

According to one aspect of the present invention, there is provided a readable storage medium storing program instructions, when the program instructions are read and executed by a computing device, the computing device can perform the above-mentioned regional multi-energy interconnection operation optimization method.

According to the technical solution of the present invention, an optimal operation method for regional multi-energy interconnection operation based on stochastic dynamic programming is proposed. The model takes into account the cycle life and charge-discharge efficiency of the battery. This model enables economic dispatch of multiple cells in a microgrid system without introducing additional objective functions to maximize efficiency and cycle life. Furthermore, a probabilistically constrained approach is proposed to account for the uncertainty in load and renewable energy forecast errors. The method employs stochastic dynamic programming to find optimal feedforward scheduling for typical microgrids of natural gas generators, photovoltaics, wind power, vanadium redox batteries, and lead-acid batteries. The results show that the proposed method can maintain the optimal operation of the system with high probability without investigating a large number of scenarios.

Description of drawings

To achieve the above and related objects, certain illustrative aspects are described herein in conjunction with the following description and drawings, which are indicative of the various ways in which the principles disclosed herein may be practiced, and all aspects and their equivalents are intended to be within the scope of the claimed subject matter. The above and other objects, features and advantages of the present disclosure will become more apparent by reading the following detailed description in conjunction with the accompanying drawings. Throughout this disclosure, the same reference numbers generally refer to the same parts or elements.

FIG. 1 shows a structural block diagram of a computing device 100 according to an embodiment of the present invention; and

FIG. 2 shows a schematic diagram of a method 200 for regional multi-energy interconnection operation optimization according to an embodiment of the present invention;

3 shows a flowchart of a forward stochastic dynamic programming method according to an embodiment of the present invention;

Figure 4 shows a schematic diagram of a typical microgrid according to an embodiment of the present invention;

FIG. 5 shows a schematic diagram of load and renewable energy forecasting in the microgrid of FIG. 4; and

FIG. 6 and FIG. 7 show schematic diagrams of the deterministic charging results and the random charging results in the microgrid of FIG. 4 , respectively.

Detailed ways

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure will be more thoroughly understood, and will fully convey the scope of the present disclosure to those skilled in the art.

FIG. 1 is a block diagram of an example computing device 100 . In a basic configuration 102 , computing device 100 typically includes system memory 106 and one or more processors 104 . The memory bus 108 may be used for communication between the processor 104 and the system memory 106 .

Depending on the desired configuration, the processor 104 may be any type of process including, but not limited to, a microprocessor (μP), a microcontroller (μC), a digital information processor (DSP), or any combination thereof. Processor 104 may include one or more levels of cache, such as L1 cache 110 and L2 cache 112 , processor core 114 , and registers 116 . Exemplary processor cores 114 may include arithmetic logic units (ALUs), floating point units (FPUs), digital signal processing cores (DSP cores), or any combination thereof. The exemplary memory controller 118 may be used with the processor 104 , or in some implementations, the memory controller 118 may be an internal part of the processor 104 .

Depending on the desired configuration, system memory 106 may be any type of memory including, but not limited to, volatile memory (such as RAM), non-volatile memory (such as ROM, flash memory, etc.), or any combination thereof. System memory 106 may include operating system 120 , one or more applications 122 , and program data 124 . In some embodiments, application 122 may be arranged to operate with program data 124 on an operating system. The program data 124 includes instructions, and in the computing device 100 according to the present invention, the program data 124 includes instructions for executing the regional multi-energy interconnection operation optimization method 200 .

Computing device 100 may also include an interface bus 140 that facilitates communication from various interface devices (eg, output device 142 , peripheral interface 144 , and communication device 146 ) to base configuration 102 via bus/interface controller 130 . Example output devices 142 include graphics processing unit 148 and audio processing unit 150 . They may be configured to facilitate communication via one or more A/V ports 152 with various external devices such as displays or speakers. Example peripheral interfaces 144 may include serial interface controller 154 and parallel interface controller 156, which may be configured to facilitate communication via one or more I/O ports 158 and input devices such as keyboard, mouse, pen , voice input devices, touch input devices) or other peripherals (eg printers, scanners, etc.) The example communication device 146 may include a network controller 160 that may be arranged to facilitate communication via one or more communication ports 164 with one or more other computing devices 162 over a network communication link.

A network communication link may be one example of a communication medium. Communication media may typically embody computer readable instructions, data structures, program modules in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media. A "modulated data signal" can be a signal of which one or more of its data sets or whose alterations can be made in such a way as to encode information in the signal. By way of non-limiting example, communication media may include wired media, such as wired or leased line networks, and various wireless media, such as acoustic, radio frequency (RF), microwave, infrared (IR), or other wireless media. The term computer readable medium as used herein may include both storage media and communication media.

Computing device 100 can be implemented as a server, such as a file server, database server, application server, and WEB server, etc., or as part of a small-sized portable (or mobile) electronic device such as a cellular phone, a personal digital Assistants (PDAs), personal media player devices, wireless web browsing devices, personal headsets, application specific devices, or hybrid devices that may include any of the above. Computing device 100 may also be implemented as a personal computer including desktop computer and notebook computer configurations. In some embodiments, the computing device 100 is configured to perform the regional multi-energy interconnection operation optimization method 200 according to the present invention.

FIG. 2 shows a schematic diagram of a post-evaluation method 200 for a power construction project according to an embodiment of the present invention. The method resides in the computing device 100 for execution.

As shown in FIG. 2, the method is suitable for step S220. In step S220, a battery operating cost model is established, and the cost model includes battery power price and battery power consumption during charging and discharging.

The battery operating cost model refers to the operating cost of small-scale natural gas generators in the microgrid. The operating cost is mainly reflected in the fuel cost. The cost F _gen can be regarded as a function of the output power:

Among them, c _gen (dollars/gallon) is the fuel price, H _gen (P _gen ) (gallons/hour) is the fuel consumption, and P _gen is the output power of the generator i.

In contrast to generators, batteries operate without fuel, making it challenging to assess the operating costs of batteries. However, in energy conversion, batteries and generators are similar. In a generator, energy is stored in the form of fuel and electricity is produced through a combustion process. Likewise, electricity in a battery is charged and discharged through electrochemical processes. In general, charging a battery is similar to refueling a generator; therefore, the input power (kWh) can be considered battery "fuel." The input electricity cost is expressed as "kWh _f " to emphasize the analogy. Therefore, the operating cost of the battery has the same deterministic form as the generator cost function, obtained from the battery power price (kWh _f price) and the battery power consumption (kWh _f consumption). The battery types studied in the present invention are lead-acid batteries, lithium-ion batteries and vanadium redox batteries.

For generators, the price of fuel c _gen consists of two parts:

代表燃料成本，

代表可用性成本，可用性成本包括燃料运输成本和其他服务成本，如现场存储设备的成本。考虑到发电机组的位置，运输成本和其他服务成本可导致c_gen比

大得多。in,

represents the fuel cost,

Represents the cost of availability, which includes the cost of fuel transportation and other services such as the cost of on-site storage equipment. Considering the location of the generator set, transportation costs and other service costs can result in a c _gen ratio

much bigger.

According to one embodiment, taking the generator model as a reference, the battery power price c _bat can be:

表示用于电池充电的能源价格，

表示电池容量的可用成本，其为“拥有1千瓦时的存储容量的可用成本”，C_∑是电池的全生命周期容量，c_rep是重置成本。在微电网中，如果可再生能源用于充电电池，

可以为零，因此，

是价格的主要组成部分。in

represents the price of energy used to charge the battery,

Denotes the usable cost of battery capacity, which is "the usable cost of having 1 kWh of storage capacity", _C∑ is the full life cycle capacity of the battery, and _crep is the replacement cost. In a microgrid, if renewable energy is used to charge batteries,

can be zero, therefore,

is the main component of the price.

In general, lead-acid, lithium-ion electrochemical cells are generally considered to have degraded to 80% of their rated capacity at the end of their life cycle. Assuming that the battery is discharged to the rated depth of discharge every cycle, the average capacity degradation rate is (0.2/Lr)C _r , where C _r is the rated capacity of the battery and L _r is the rated life. Relative to lead-acid and lithium-ion batteries, vanadium redox batteries (VRBs) experience negligible capacity loss after repeated deep discharges and charges. The cycle life of a vanadium battery mainly depends on the life of its proton exchange membrane and pump. Vanadium batteries can be cycled more than 10,000 times until their membranes degrade or their pumps fail.

Therefore, for lead-acid and lithium-ion batteries, the total lifetime available capacity of the battery is:

C _∑ =C _r DOD _r [L _r -0.2*(1+2+...+L _r )/L _r ]=C _r DOD _r (0.9L _r -0.1)(kWh),

The total lifetime usable capacity of a vanadium redox battery is:

C _∑ =C _r DOD _r L _r (kWh)

where DOD _r is the depth of discharge.

The battery's operating cost model is based on similarities with the generator's fuel cost model, and therefore has little additional complexity compared to standard methods. The kWh _f price of the battery does not change as frequently as the fuel price. The price of battery power (kWh _f price) includes replacement cost, rated capacity and life cycle, and the above indicators are determined at the time of purchase and do not need to be upgraded.

The battery power consumption (kWh _f consumption) in the discharge process is defined as "the energy use H _bat supplied to the load per unit time, and the battery power consumption in the charging process is the power loss L _bat of the charging battery per unit time, and its calculation formulas are respectively for:

是电池输出功率，

是放电功率损耗，

是电池输入功率，

是充电功率损耗。根据电池类型，H_bat和L_bat分别是

和

的函数，本发明中，H_bat和L_bat分别代表铅酸、锂离子电池和钒氧化还原电池类型。in,

is the battery output power,

is the discharge power loss,

is the battery input power,

is the charging power loss. Depending on the battery type, H _bat and L _bat are

and

In the present invention, H _bat and L _bat represent lead-acid, lithium-ion and vanadium redox battery types, respectively.

Power loss in lead-acid or lithium-ion batteries is mainly caused by heat loss during charging or discharging. The heat is generated by the ohmic resistance of the electrodes and electrolyte, as well as by polarization effects. The power loss is proportional to the voltage drop (polarization) caused by the current P _joule = _ΔV ×I, so the voltage drop of lead-acid and lithium-ion batteries during discharge and charge can be determined as:

where SOC is the state of charge, R is the internal ohmic resistance, K is a constant that can be calculated from the manufacturer's data, and Q _r is the battery's rated capacity.

On this basis, according to an embodiment of the present invention, the battery power consumption of lead-acid and lithium-ion batteries during discharge is:

Battery power consumption during charging is:

where V _r is the rated voltage of the battery.

For vanadium redox batteries, the power loss during charging and discharging consists of two parts: pumping the electrolyte and stacking power loss due to internal resistance and electrochemical processes. Open circuit voltage and stack current can be characterized as a function of charge and discharge power:

和

分别是电池在放电期间和充电期间的堆栈电流，

分别是电池损失模型系数，其是与额定电压V_r和额定电流I_r有关的参数，所有的模型系数在表1中给出，计算时将其代入即可。where V _OC is the open circuit voltage of the battery,

and

are the stack current of the battery during discharge and charge, respectively,

are the battery loss model coefficients, which are parameters related to the rated voltage V _r and the rated current I _r . All the model coefficients are given in Table 1, which can be substituted into the calculation.

Table 1 Vanadium battery loss model coefficients

Based on this, the battery power consumption (kWh _f consumption) of the vanadium redox battery during discharge and charge can be determined as:

Subsequently, in step S240, a random unit combination model of the microgrid is constructed based on the cost model, where the combination model includes constraints and an objective function, where the objective function is to minimize the expected operating cost of a time period.

For the random unit combination problem of the microgrid, the objective is to reduce the expected operating cost C of the microgrid in a time range, so the objective function is:

in,

F _m,k =F _m (P _m,k )T

(美元)分别代表时间段k内发电机、放电电池和充电电池的总运行费用，F_m,k是由于功率不匹配造成的成本。T是时间步长，n₁和n₂分别代表发电机和电池的数量，g_i和b_i分别代表发电机i和电池i，s_gi,k、

分别代表时间段k内发电机i和电池i的二进制状态，由于电池可同一时间内处于既不充电也不放电的状态，故

可为零。c_gi(美元/升)是发电机i的燃料价格，c_bi(美元/千瓦时)是电池i的电量价格(kwh_f价格)，F_gi是发电机i的燃料成本，H_gi(加仑/小时)是发电机i的燃料消耗，

(千瓦时/小时)和

(千瓦时/小时)分别为放电和充电期间电池i的电量消耗，

和

分别为放电和充电期间电池i的运行成本，P_gi,k、

(千瓦)分别代表了时间段k内发电机i、放电电池和充电电池的发送功率，P_m,k是时间段k内由于功率失配造成的成本，F_m指由于功率不匹配造成的单位时间成本。where F _k is the total expected operating cost in time period k, _Sk is the total transition cost including generator start-up and shutdown costs in time period k, N is the time horizon, F _g,k ,

(USD) represent the total operating costs of the generator, discharged battery, and rechargeable battery during time period k, respectively, and _Fm,k is the cost due to power mismatch. T is the time step, n ₁ and n ₂ represent the number of generators and batteries, respectively, _gi and b _i represent generator i and battery i, respectively, s _gi,k ,

respectively represent the binary states of generator i and battery i in time period k. Since the battery can be in a state of neither charging nor discharging at the same time, so

Can be zero. c _gi ($/liter) is the fuel price for generator i, c _bi ($/kWh) is the power price for battery i (kwh _f price), F _gi is the fuel cost for generator i, H _gi (gallon/ hours) is the fuel consumption of generator i,

(kWh/hour) and

(kWh/h) are the power consumption of battery i during discharge and charge, respectively,

and

are the operating costs of battery i during discharge and charge, respectively, P _gi,k ,

(kW) represent the transmit power of generator i, discharged battery and rechargeable battery in time period k, respectively, P _m,k is the cost due to power mismatch in time period k, F _m refers to the unit due to power mismatch Time costs.

In order to better define this problem, the present invention introduces the following conventions:

被认为是负值；1) Charging power

is considered a negative value;

2) Renewable energy sources (photovoltaic and wind turbines) are not dispatchable and are considered loads. The net load P _net,k of time period k is defined as:

P _net,k =∑P _load,k -∑P _PV,k -∑P _W,k

Among them, P _load,k , P _PV,k , P _W,k represent the real-time power of the time period k load, photovoltaic generator and wind turbine, respectively, all three are random, so P _net,k is considered to be Random Variables;

3) The battery is charged only when P _net,k is less than zero;

4) The power mismatch P _m,k in the time period k is the difference between the total power generation and the net load,

When P _net,k ≥ 0,

When P _net,k <0, P _m,k =P _chg,k -P _net ,

where P _gen,k > 0 is the total power generation, and P _chg,k < 0 is the total charging cost.

The realization of grid connection, electricity price and repurchase price in the microgrid is deterministic, so the constraints of the stochastic combination model are defined based on the physical constraints of energy management strategies and equipment in the microgrid, and the constraints include at least one of the following constraints:

R1: The power mismatch is required to be greater than zero under a preset probability.

R2: The battery discharge is not used to charge other batteries; the generator is not used to charge the battery.

R3: Each storage device cannot exceed (or fall below) the maximum (or minimum) charge (or discharge) SOC.

R4: The charge (or discharge) rate of each storage device should not exceed the maximum value (or minimum value).

R5: Each generator is online at least to its minimum output setpoint.

R6: When the generator is online, ensure the minimum set time online; when the generator is powered off, ensure the shortest shutdown time before restarting.

In small systems such as microgrids, microgrid electricity should not be used to charge energy storage due to the relatively low round-trip efficiency of the energy storage unit ESS. Therefore, the energy storage unit should not charge other energy storage units, nor should the electricity be used for energy storage, only renewable energy can be used to charge the energy storage unit, which is reflected in the constraint R2. The above constraints are specified as follows:

和

分别是电池i充电状态的最小值和最大值，

和

分别是放电电池i发送功率的最小值和最大值，

和

分别是充电电池i发送功率的最小值和最大值，

是k时段发电机i在线时的发送功率，

和

分别是发电机i发送功率的最小值和最大值，

和

分别是k时段发电机i的上线时间和离线时间，

和

分别是发电机i上线时间和离线时间的最小值，α_k和β_k是参数。Among them, P(x) is the probability of satisfying the condition of x, AND(a,b)=0 means that a and b cannot be 1 at the same time, SOC _bi,k is the state of charge of battery i in the k period,

and

are the minimum and maximum values of the state of charge of battery i, respectively,

and

are the minimum and maximum transmit power of the discharged battery i, respectively,

and

are the minimum and maximum values of the transmit power of the rechargeable battery i, respectively,

is the transmit power of generator i when it is online in period k,

and

are the minimum and maximum values of the power sent by generator i, respectively,

and

are the on-line time and off-line time of generator i in period k, respectively,

and

are the minimum values of generator i on-line time and off-line time, respectively, and α _k and β _k are parameters.

Further, formula R2 can be upgraded to:

和

分别是时间段k内放电期间和充电期间电池i的电池电量消耗(kwh_f消耗)，

为电池i在时间段k内的电池电量成本(kwh_f成本)。in,

and

are the battery power consumption (kwh _f consumption) of battery i during discharging and charging in time period k, respectively,

is the battery power cost (kwh _f cost) of battery i in time period k.

By implementing constraint R1, when P _net,k >0, no matter how α _k changes, P _m,k is non-negative; when P _net,k <0, no matter how β _k changes, P _m,k is non-negative . Based on the formula in Convention 4), the constraint R1 can be further rewritten as:

The parameter _αk controls the level of probability of importing/exporting electricity from the grid to the microgrid. If α _k =0, the probability of the microgrid generating enough power internally is zero, and the required power must be imported from the grid. At the other extreme, if α _k =1, then the probability that sufficient power is always generated in the microgrid is 1. This is not realistic, so α _k is strictly limited to less than 1.0 (but ideally fairly close to 1.0). Similarly, if β _k = 0, all net renewable energy will be used to charge the energy storage. For example, if β _k = 0.5, it means that there is a 50% probability that surplus electricity from renewable sources will be exported to the grid. Choosing a larger beta will reduce the likelihood that renewable energy will be used to charge energy storage, allowing more renewable energy to be exported to the grid. Therefore, αk and _βk can be flexibly chosen and varied (or kept constant) within a potential range over time period _k , taking into account energy management policies. The amount of electricity imported/exported is determined by the choice of α and β parameters. Choosing a smaller α will increase the likelihood that the system will import power from the grid to supply the load, while choosing a larger β will increase the likelihood that the system will export excess renewable energy to the grid. α and β are the degree of freedom parameters to decide whether and how much electricity is imported/exported from the grid according to the desired energy management policy.

因此，实际负载，PV，风力发电和净负荷的实现可以表示为：In order to implement the cost function F _k and the constraint R1 , the cumulative distribution function (CDF) and the mean value P _net,k need to be specified. In fact, the actual load of the time period k, the predicted value of the photovoltaic generator and the wind turbine can be obtained separately

Therefore, the realization of the actual load, PV, wind generation and net load can be expressed as:

Among them, ΔP _load , ΔP _pv , and ΔP _WT are the actual load error, photovoltaic power generation error and wind power generation error, respectively, and are the prediction errors that depend on the prediction method and prediction range. To model the uncertainty of load and renewable energy forecasts, ΔP _load , ΔP _pv , ΔP _WT are considered as random variables. Although the Weber, Cauchy, and mixed Laplace distributions can more accurately describe wind power forecast errors, it can be approximated by a zero-mean normal distribution. Furthermore, since the load demand and PV generation forecast errors are very close to a normal distribution, the net load error ΔP _net,k (the sum of all errors) can be approximated by a zero mean normal distribution. The standard deviation of ΔP _net,k can be calculated as follows:

Therefore, the following expectations and probabilities can be calculated:

P( _Pnet,k >0)=1- _pk

是P_net,k的期望值，p_k是净负荷小于0的概率，E(y|x)是在满足x条件下y的期望，如

就是当P_net,k＞0时P_m,k的期望。因此约束R1可以理解为：where φ is the cumulative distribution function obeying the (0,1) standard normal distribution, E(P _net,k ) and

is the expected value of P _net,k , p _k is the probability that the net load is less than 0, E(y|x) is the expectation of y under the condition of x, such as

is the expectation of P _m,k when P _net,k > 0. Therefore, the constraint R1 can be understood as:

By choosing different values for _αk and _βk , the system will generate (or charge) more or less power. The _Fk formula can be further expressed as:

Among them, c _ex,k is the electricity price exported to the grid, and c _im,k is the electricity price imported to the grid.

Subsequently, in step S260, a predetermined method is used to solve the combination model to obtain optimal unit combination parameters, and the unit combination is performed according to the optimal result to realize the optimization of regional multi-energy interconnection operation.

According to one embodiment, the predetermined method is a dynamic programming (DP) method. The crew combination problem can be classified as a continuous decision problem, of which dynamic programming (DP) is better known. Dynamic programming is a method of finding the shortest route to a destination by breaking it down into steps over a period of time. At each step, based on the best possible subsequence from the previous steps, the possible dominant sequences (routing) are determined, and finally the best sequence for the final step is found. The main advantage of DP is that the feasibility of the solution can be maintained by the ability to find the optimal order to find the optimal order. The main disadvantage of DP is that it is computationally heavy. For example, in a system composed of N units, there are 2 ^N -1 combinations in each time period, and for M time periods, the total number of combinations is (2 ^N -1) ^M . For large systems, the computation required to traverse this space can be overwhelming. However, in microgrid applications, a small number of cells and a large number of constraints reduce the search space significantly, so DP can be an appropriate choice for the algorithm.

Due to the uncertainties associated with stochastic problems, the cost of each stage is usually a random variable. Therefore, in stochastic DP techniques, the problem is to minimize the expected cost. When applying DP, the state space at stage k is defined as follows:

是单元x_i的二进制状态，x_i代表发电机、放电电池或充电电池。如果满足约束R2，R3，R6和以下条件，则

是有效状态：where L _k is the set of feasible states for stage k, m _k is the number of states in the L _k set,

is the binary state of cell _{xi ,} which represents generator, discharging battery or charging battery. If constraints R2, R3, R6 and the following conditions are satisfied, then

is a valid state:

Further, the forward stochastic programming method is used in the present invention, and FIG. 3 shows a schematic diagram of the forward DP algorithm, an algorithm for calculating the minimum cost of reaching state A in stage B:

是到达状态

的最小费用，

是对于状态

的运作费用，

是从状态

到状态

的过渡费用。运营成本可以通过执行经济调度(ED)来最小化具有约束R1，R4和R5的F_k成本函数。关于该经济调度方法，本领域技术人员可以采用现有常见的调度方法，如基于粒子群算法的电网调度方法，本发明对此不作限定。根据一个实施例，可以采用最陡下降算法来解决经济调度问题，关于该最陡下降算法的详细参数细节，本领域技术人员可以根据需要自行设定，本发明对此同样不作限定。in,

is the state of arrival

the minimum cost of

is for the state

operating costs,

is from the state

to state

transition costs. Operational costs can be minimized by performing economic dispatch (ED) to minimize the _Fk cost function with constraints R1, R4 and R5. Regarding the economic dispatching method, those skilled in the art can adopt the existing common dispatching method, such as the power grid dispatching method based on the particle swarm algorithm, which is not limited in the present invention. According to an embodiment, the steepest descent algorithm can be used to solve the economic scheduling problem, and the detailed parameters of the steepest descent algorithm can be set by those skilled in the art as required, which is also not limited in the present invention.

According to the technical solution of the present invention, a new battery operating cost model considering battery charging/discharging efficiency and cycle life time is proposed, which makes the battery be regarded as an equivalent natural gas generator during charging and discharging. In addition, a probability constraint method for introducing the uncertainty of renewable energy and load demand into the charging and discharging problem in the microgrid is proposed, and the unit combination problem in the microgrid is solved by stochastic dynamic programming.

In concrete practice, the present invention tests the proposed method through a case study. Figure 4 shows a typical microgrid connected to the low voltage side of a distribution transformer to power residential loads. The microgrid consists of a 50kW natural gas generator, two 20kW wind turbines, a 50kW photovoltaic array, 10kW/40kWh vanadium batteries and 12kW/30kWh AGM lead-acid batteries. The total load at peak is 50kW. The cost of the AGM battery is estimated at $8,000. The replacement cost of the vanadium battery VRB is estimated at $20,000. Natural gas generators were specifically used in this case study. The generator and battery data extracted from the manufacturer's data and their initial states are shown in Tables 2 and 3, and Figure 5 presents the day-ahead forecast values for total load, photovoltaic and wind power generation. The standard deviations of load, photovoltaic and wind power forecast errors are 3.12%, 12.5% and 13.58%, respectively. The α _k and β _k parameters were chosen to be 0.9 and 0.1, respectively. A high value α _k represents a high probability of satisfying all loads internally. A low value of _βk indicates a lower probability of exporting renewable energy to the grid (ie, prioritizing the use of excess power to charge the energy storage unit).

Table 2 Natural gas generator data

Table 3 Battery data

Table 4 Standard deviation of payload forecast error

The standard deviation σ _net,k of the calculated hourly net load forecast error is given in Table 4. The deterministic charging results are shown in Figure 6. By introducing a battery's operating cost function, economic dispatch tends to favor batteries with longer cycle life, lower replacement costs, and higher efficiency. In this case, the vanadium battery is lower, but the lead-acid battery is more efficient, so the resulting power generation is similar. Compared to natural gas generators, batteries are less expensive to run due to lower “fuel” prices and higher efficiency. But the maximum depth of discharge of the battery is limited, so the battery can only be discharged for a few hours at night, as the results show. The results of random charging in contrast to the deterministic case are shown in Figure 7. By comparing the random and deterministic cases, the impact of the choice of α _k and β _k can be seen:

1) When the load is high and the renewable energy generation is low (about 15 to 24 hours), the stochastic algorithm outperforms the natural gas generator set compared to the deterministic case shown by P _DG* > P _DG . Since α _k is chosen to be a value greater than 0.9, this indicates that the load is satisfied internally with high probability. Since renewable energy is not available during these hours, it is required that the natural gas generator must be able to withstand any potential changes in load.

2) When the load is high and the renewable energy generation is high (about 9 to 14 hours), the random algorithm overcharges the energy storage unit. Since β _k is chosen to be a value less than 0.1, this indicates that there is little possibility of excess generation being sent to the grid, thereby increasing the possibility of charging the battery.

3) When the load is low and there is no renewable energy, the random combination algorithm can closely fit the deterministic case (0 to 8 hours).

By choosing α _k and β _k , the risk allowed in the system can be adjusted. In this example, the values of both αk and _βk remain _constant throughout the 24 hours, but in practice these values may change in response to expected changes in load or renewable energy. In addition, although not explicitly stated, the two energy storage units are matched to operate in accordance with their respective operating conditions detailed previously to maximize their lifespan.

This case study provides a framework for assessing the impact of the method proposed in the paper. The main features of this method are: better describe the actual performance of energy storage system performance; adjust the response of resource allocation. Specifically, most energy management and resource allocation approaches do not explicitly account for life-cycle degradation due to deep discharge, nor do they consider efficiency as a function of output (or input) power. In this case study, vanadium batteries are similar to lead-acid batteries, and their better life cycle attributes and efficiency characteristics yield similar long-term economic benefits for lead-acid batteries, even though vanadium batteries are quite expensive to install. Also, both batteries are more cost-effective than natural gas generators. Therefore, increasing the scale of the energy storage system relative to the scale of natural gas generators makes more economic sense. Furthermore, the results of the stochastic analysis show that natural gas generators are increasingly dependent on deterministic situations, as natural gas generators become a more reliable resource as uncertainty increases. This can be counteracted by reducing the size of α.

A9. The method of A8, wherein,

F _m,k =F _m (P _m,k )T

分别代表时间段k内发电机i和电池i的二进制状态，c_gi是发电机i的燃料价格，c_bi是电池i的电量价格，F_gi是发电机i的燃料成本，H_gi是发电机i的燃料消耗，

和

分别为放电和充电期间电池i的电量消耗，

和

分别为放电和充电期间电池i的运行成本，P_gi,k、

分别代表了时间段k内发电机i、放电电池和充电电池的发送功率，P_m,k是时间段k内由于功率失配造成的成本，F_m指由于功率不匹配造成的单位时间成本。where N is the time range, T is the time step, n ₁ and n ₂ represent the number of generators and batteries, respectively, _gi and b _i represent generator i and battery i, respectively, s _gi,k ,

represent the binary states of generator i and battery i in time period k, respectively, c _gi is the fuel price of generator i, c _bi is the electricity price of battery i, F _gi is the fuel cost of generator i, and H _gi is the generator i the fuel consumption of i,

and

are the power consumption of battery i during discharging and charging, respectively,

and

are the operating costs of battery i during discharge and charge, respectively, P _gi,k ,

represent the transmit power of generator i, discharged battery and rechargeable battery in time period k, respectively, P _m,k is the cost caused by power mismatch in time period k, and F _m is the unit time cost caused by power mismatch.

A10. The method of A9, wherein,

是P_net,k的期望值。Among them, P _net,k is the net load in the k period, p _k is the probability that the net load is less than 0, E(y|x) is the expectation of y under the condition of x, c _ex,k is the electricity price exported to the grid, c _im,k is the price of electricity imported to the grid, α _k and β _k are parameters, where α _k is the probability level controlling the import/export of electricity from the grid to the microgrid, P _gen,k is the total power generation, and P _chg,k is the total charging cost,

is the expected value of P _net,k .

A11. The method of A10, wherein, when P _net,k ≥ 0,

A12. The method of A10, wherein, when P _net,k <0,

A13. The method according to any one of A1-A12, wherein the constraints include at least one of the following six constraints:

和

分别是电池i充电状态的最小值和最大值，

和

分别是放电电池i发送功率的最小值和最大值，

和

分别是充电电池i发送功率的最小值和最大值，

是k时段发电机i在线时的发送功率，

和

分别是发电机i发送功率的最小值和最大值，

和

分别是k时段发电机i的上线时间和离线时间，

和

分别是发电机i上线时间和离线时间的最小值。Among them, P(x) is the probability of satisfying the condition of x, AND(a,b)=0 means that a and b cannot be 1 at the same time, SOC _bi,k is the state of charge of battery i in the k period,

and

are the minimum and maximum values of the state of charge of battery i, respectively,

and

are the minimum and maximum transmit power of the discharged battery i, respectively,

and

are the minimum and maximum values of the transmit power of the rechargeable battery i, respectively,

is the transmit power of generator i when it is online in period k,

and

are the minimum and maximum values of the power sent by generator i, respectively,

and

are the on-line time and off-line time of generator i in period k, respectively,

and

are the minimum values of generator i on-line time and off-line time, respectively.

A14. The method described in A13, wherein the constraint condition R1 is:

where φ is the cumulative distribution function obeying the (0,1) standard normal distribution, σ _net,k is the standard deviation of the net load error ΔP _net,k , where ΔP _net,k is the actual load error ΔP _load , PV power generation The sum of the error ΔP _pv and the wind power generation error ΔP _WT , ΔP _load , ΔP _pv , and ΔP _WT are the prediction errors depending on the prediction method and the prediction range.

A15. The method according to any one of A1-A14, wherein the predetermined method is a stochastic dynamic programming method, wherein the state space in stage k is:

是单元x_i的二进制状态，x_i代表发电机、放电电池或充电电池。where L _k is the set of feasible states for stage k, m _k is the number of states in the L _k set,

is the binary state of cell _{xi ,} which represents generator, discharging battery or charging battery.

A16. The method according to A15, wherein the stochastic dynamic programming method is a forward stochastic dynamic programming method, wherein the minimum cost of reaching state A in stage B is:

是到达状态

的最小费用，

是对于状态

的运作费用，

是从状态

到状态

的过渡费用。in,

is the state of arrival

the minimum cost of

is for the state

operating costs,

is from the state

to state

transition costs.

In the description provided herein, numerous specific details are set forth. It will be understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.

Similarly, it is to be understood that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or its description. This disclosure, however, should not be interpreted as reflecting an intention that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the Detailed Description are hereby expressly incorporated into this Detailed Description, with each claim standing on its own as a separate embodiment of this invention.

Those skilled in the art will appreciate that the modules or units or components of the apparatus in the examples disclosed herein may be arranged in the apparatus as described in this embodiment, or alternatively may be positioned differently from the apparatus in this example in one or more devices. The modules in the preceding examples may be combined into one module or further divided into sub-modules.

Those skilled in the art will understand that the modules in the device in the embodiment can be adaptively changed and arranged in one or more devices different from the embodiment. The modules or units or components in the embodiments may be combined into one module or unit or component, and further they may be divided into multiple sub-modules or sub-units or sub-assemblies. All features disclosed in this specification (including accompanying claims, abstract and drawings) and any method so disclosed may be employed in any combination, unless at least some of such features and/or procedures or elements are mutually exclusive. All processes or units of equipment are combined. Each feature disclosed in this specification (including accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.

Furthermore, those skilled in the art will appreciate that although some of the embodiments described herein include certain features, but not others, included in other embodiments, that combinations of features of different embodiments are intended to be within the scope of the invention within and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

Furthermore, some of the described embodiments are described herein as methods or combinations of method elements that can be implemented by a processor of a computer system or by other means for performing the described functions. Thus, a processor having the necessary instructions for implementing the method or method element forms means for implementing the method or method element. Furthermore, an element of an apparatus embodiment described herein is an example of a means for carrying out the function performed by the element for the purpose of carrying out the invention.

As used herein, unless otherwise specified, the use of the ordinal numbers "first," "second," "third," etc. to describe common objects merely refers to different instances of similar objects, and is not intended to imply such The objects being described must have a given order in time, space, ordinal, or in any other way.

While the invention has been described in terms of a limited number of embodiments, those skilled in the art will appreciate, having the benefit of the above description, that other embodiments are conceivable within the scope of the invention thus described. Furthermore, it should be noted that the language used in this specification has been principally selected for readability and teaching purposes, rather than to explain or define the subject matter of the invention. Accordingly, many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the appended claims. This disclosure is intended to be illustrative and not restrictive with regard to the scope of the present invention, which is defined by the appended claims.

Claims (10)

1. A regional multi-energy interconnection operation optimization method, executed in a computing device, the method comprising: Establish a battery operating cost model, which includes battery power price and battery power consumption during charging and discharging; Based on the cost model, a random unit combination model of the microgrid is constructed, and the combination model includes an objective function and constraints, and the objective function is to minimize the expected operating cost of a time period; The combination model is solved by a predetermined method, and the optimal unit combination parameters are obtained, and the unit combination is carried out according to the optimal result to realize the optimization of regional multi-energy interconnection operation. 2. The method according to claim 1, wherein, the calculation formula of the battery price c _bat is:

in,

represents the price of energy used to charge the battery,

Denotes the available cost of battery capacity, which refers to the available cost of having 1 kWh of storage capacity, _C∑ is the full life cycle capacity of the battery, and _crep is the replacement cost. 3. The method of claim 2, wherein, for lead-acid and lithium-ion batteries, C _∑ =C _r DOD _r [L _r -0.2*(1+2+...+L _r )/L _r ] =C _r DOD _r (0.9L _r -0.1) (kWh) For vanadium redox batteries: C _∑ =C _r DOD _r L _r (kWh) where DOD _r is the depth of discharge, C _r is the rated capacity of the battery, and L _r is the rated life. 4. The method according to claim 1, wherein the power consumption of the battery during the discharging process is the energy usage H _bat supplied to the load per unit time, and the battery power consumption during the charging process is the power loss L _bat of the charging battery per unit time , and the calculation formulas are:

in,

is the battery output power,

is the discharge power loss,

is the battery input power,

is the charging power loss. 5. The method of claim 4, wherein, for lead-acid and lithium-ion batteries,

where SOC is the state of charge, V _r is the battery's rated voltage, Q _r is the battery's rated capacity, R is the internal ohmic resistance, and K is a constant calculated from the manufacturer's data. 6. The method of claim 4, wherein, for vanadium redox cells,

where V _OC is the open circuit voltage of the battery,

and

are the stack current of the battery during discharge and charge, respectively,

are battery loss model coefficients, which are parameters related to rated voltage V _r or rated current I _r , respectively. 7. The method of claim 6, wherein,

8. The method according to any one of claims 1-7, wherein the objective function is:

where F _k is the total expected operating cost in time period k, _Sk is the total transition cost including generator startup and shutdown costs in time period k, F _g,k ,

are the total operating costs of the generator, discharged battery and rechargeable battery in time period k, respectively, and F _m,k is the cost due to power mismatch. 9. A computing device comprising: at least one processor; and a memory storing program instructions, wherein the program instructions are configured to be adapted for execution by the at least one processor, the program instructions comprising instructions for performing the method of any of claims 1-8 . 10. A readable storage medium storing program instructions that, when read and executed by a computing device, cause the computing device to perform the method of any one of claims 1-8.

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