CN112803446B

CN112803446B - Multi-energy optimal control method and control system based on client side demand response

Info

Publication number: CN112803446B
Application number: CN202110122589.2A
Authority: CN
Inventors: 邵雪松; 杨斌; 黄奇峰; 王忠东; 易永仙; 蔡奇新; 李悦; 季欣荣; 周玉; 崔高颖; 陈飞; 阮文骏; 高凡; 穆卓文
Original assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Corp of China SGCC; State Grid Jiangsu Electric Power Co Ltd
Current assignee: State Grid Jiangsu Electric Power Co ltd Marketing Service Center; State Grid Corp of China SGCC; State Grid Jiangsu Electric Power Co Ltd
Priority date: 2021-01-28
Filing date: 2021-01-28
Publication date: 2023-05-23
Anticipated expiration: 2041-01-28
Also published as: CN112803446A

Abstract

A multi-energy optimal control method and a control system based on client side demand response analyze load and randomness power supply characteristics of an regional power distribution network and an energy storage optimal configuration principle, and establish a multi-energy optimal configuration model considering virtual energy storage; establishing a multi-energy optimization target based on a client side energy utilization control system, and determining a virtual energy storage energy optimization management strategy under each optimization target; aiming at the control targets of the three aspects, a virtual energy storage system coordination control strategy based on two-layer optimization is provided by combining the virtual energy storage running state, response characteristics and grading conditions, and the optimization of the total virtual energy storage adjustment quantity under different control targets, and the power distribution and coordination control among all units in the virtual energy storage system are realized. The intelligent optimization management method can realize intelligent optimization management of loads, energy storage and distributed power sources in the regional distribution network, reduce energy storage construction cost and operation cost of the power grid, and improve regulation and control capacity of a user side and safe operation level of the distribution network.

Description

Multi-energy optimal control method and control system based on client side demand response

Technical Field

The invention belongs to the technical field of energy storage optimal configuration and virtual energy storage system coordination control, and relates to a multi-energy optimal control method based on demand response of a client-side energy utilization control system, in particular to a multi-energy optimal configuration model establishment taking virtual energy storage into consideration, a multi-energy optimal target establishment based on the client-side energy utilization control system, a multi-time multi-scale optimal control method based on the client-side energy utilization control system and a control system.

Background

Along with the wide access of the distributed power sources of the power distribution system to the power system, the randomness of the distributed power sources respectively brings serious challenges to the supply and demand balance and the safe operation of the power grid from the two aspects of a power supply side and a user side. Because of the intermittence of the output of the random power supply, the fluctuation of the power flowing on the interconnecting line of the power distribution network is larger and larger, and the power supply quality of the electric energy and the stability and the safety operation of the power grid are further affected. The user side resources such as power load, distributed power generation, energy storage devices and the like under the market conditions have the characteristics of diversity, dispersity, randomness and the like, so that it is difficult to establish a unified model to represent the characteristics of the load resources; the increasing development of distributed energy sources and the gradual maturing of the power market are particularly important in consideration of the influence of specific demand response modes (such as peak-valley time-of-use electricity prices) on energy scheduling and energy storage capacity configuration in a distribution network, and the research on the energy storage optimization configuration and the demand response of the existing distribution network is found by analyzing the research on the energy storage optimization configuration and the demand response of the existing distribution network, so that the research on the aspect is relatively deficient

On the other hand, as an important participant of power demand side management and demand response, the user side resource actively and timely participates in the supply and demand balance of the power system, so that the running cost of the power grid can be reduced, more importantly, the safety of the power grid can be improved, and the economic management level of a power grid company can be improved.

In order to reduce the energy storage construction cost and the operation cost of the power grid, improve the energy storage capacity of the distributed renewable energy sources and the service level facing the comprehensive energy source demand of the user, further strengthen the research on the aggregation characteristic and the comprehensive regulation capacity of the user side resources, consider the specific demand response mode to the power energy dispatching demand of the user side, combine the load demand response and the energy storage technology, combine the load with the energy storage characteristic and the energy storage to form a virtual energy storage system, perform hierarchical optimization coordination control, improve the response capacity of the load side and reduce the effective means of the traditional energy storage configuration.

Based on the above, the invention aims to develop and deeply study load virtual energy storage response characteristics and models, energy storage characteristics and optimal configuration technologies, virtual energy storage optimal management and coordination control technologies and the like aiming at a virtual energy storage system formed by a user side multi-element load, energy storage and distributed power supply, develop virtual energy storage coordination control software and demonstration application, enhance continuous adjustment capability under various application functions, support supply and demand balance of a receiving end power grid, ensure safe and economic operation of the power grid, improve user side resource response capability and improve user side regulation capability and distribution network safe operation level by utilizing a big data analysis technology to excavate electricity consumption data value.

Disclosure of Invention

The invention aims to provide a multi-energy optimization control method based on the demand response of a user side energy utilization control system, which guides users to participate in friendly interaction of a power grid, builds a saving type society, reduces the energy storage construction cost of the power grid and the operation cost of the power grid, and improves the regulation capacity of the user side and the safe operation level of a distribution network.

In order to achieve the above purpose, the present invention adopts the following technical scheme:

the multi-energy optimal control method based on the client side demand response is characterized by comprising the following steps of:

step 1, establishing a multi-energy optimal configuration model considering virtual energy storage; the method comprises the steps that a load with energy storage characteristics in a power supply area to be optimally controlled is used as a load virtual energy storage system and an energy storage device to form a virtual energy storage system, an energy storage device optimal configuration model considering virtual energy storage is established, and under the condition that constraint conditions are met, the optimal combination of the total capacity of the virtual energy storage system and the response electricity price of a demand side is solved;

step 2, establishing a multi-energy optimization target based on a client side energy utilization control system, and determining a virtual energy storage energy optimization management strategy under each optimization target;

and 3, realizing power distribution and coordination control of multiple time scales among units in the virtual energy storage system.

The present invention further includes the following priority schemes.

In step 1, the load with energy storage property comprises an air conditioner, a refrigerator and a water heater;

the energy storage device comprises a storage battery and an energy storage capacitor.

In step 1, according to load characteristic distribution and random power supply fluctuation conditions, determining functional requirements of an energy storage device through data simulation, constructing an optimal configuration model considering load virtual energy storage according to an energy storage device optimal configuration principle, and performing simulation verification on the optimal configuration model, wherein verification contents comprise: time-of-use electricity price, configuration of energy storage capacity and charge and discharge power of energy storage in each period.

The optimal configuration model considering the load virtual energy storage comprises an objective function and constraint conditions to be met, wherein the constraint conditions comprise an electricity price constraint condition, an energy storage device charging and discharging power constraint condition and an energy storage device SOC constraint condition.

The load virtual energy storage considered optimal configuration model objective function is as follows:

min f＝C _bess +C _bess.om -C _g (1)

wherein: c (C) _bess Building annual values such as cost for the energy storage device; c (C) _bess.om The annual operation maintenance cost of the storage battery pack is saved; c (C) _g The method is used for obtaining the power grid benefits for peak clipping and valley filling;

wherein, the construction cost C of the energy storage device _bess The calculation formula is as follows:

Wherein: c (C) _bess The construction cost of the energy storage device is equal in annual value; e (E) _bess Rated installation capacity of the energy storage device is given by MWh; k (k) _e The construction cost is the unit capacity of the energy storage device; r is the discount rate; h is the service life;

annual operation maintenance cost C of energy storage device _bess.om The calculation formula of (2) is as follows:

wherein: c (C) _bess.om The annual operation maintenance cost of the energy storage device is saved; e (E) _bess Rated installation capacity for the energy storage device; k (k) _om The maintenance cost is operated for the unit capacity of the energy storage device;

the gain function brought by peak clipping and valley filling is as follows:

C _g ＝a×∑(P _M0 -P _L )·Δt+b∑(P _L -P _mo )·Δt (4)

wherein: a peak clipping gain coefficient; b, filling the grain gain coefficient; p (P) _M0 Peak load before demand response; p (P) _m0 Valley load before demand response; p (P) _L The load after the response of the demand is made,Δt represents the duration of peak elimination or valley filling;

the charge and discharge operation strategy and the charge and discharge power of the energy storage device are as follows:

wherein: p (P) _bess (n) represents the n-th energy storage charging device discharge reference power.

The calculation formula of the electricity price constraint condition is as follows:

p _min ≤p _v ≤p _f ≤p _p ≤p _max (6)

wherein p is _min To restrict the lowest electricity price, p _v To underestimate electricity price, p _f At the usual level, p _p For peak electricity price, p _max To restrict the highest electricity price.

The constraint conditions of the SOC of the energy storage device are as follows:

SOC _min ≤SOC(t)≤SOC _max (7)

0≤E(t)≤E _bess (8)

wherein SOC is _min For the minimum state of charge of the energy storage device, SOC (t) is the current state of charge of the energy storage device, and SOC _max For maximum state of charge of the energy storage device, E (t) is the capacity of the energy storage device, E _bess The rated installation capacity of the energy storage device is provided.

The constraint conditions of the charge and discharge power of the energy storage device are as follows:

-P _N ≤P _bess (n)≤P _N (9)

wherein P is _bess (n) charge/discharge power of energy storage device, P _N Is the rated power of the energy storage device.

The establishing of the multi-energy optimization target based on the client-side energy utilization control system in the step 2 comprises the following steps: and establishing optimization targets for stabilizing random power supply fluctuation, improving regional supply and demand balance and adjusting node voltage by analyzing the distributed power supply output prediction data, the regional load prediction data, the node voltage actual measurement value and the limit value, and obtaining a virtual energy storage energy optimization management strategy under each optimization target.

The virtual energy storage energy optimization management strategy under the target of stabilizing the random power supply fluctuation comprises the following steps: and establishing a stabilized random power supply fluctuation objective function by analyzing the set time scale, the fluctuation rate limit value and the distributed power supply output prediction data, and calculating to obtain distributed power supply power data, regional adjustment power requirements and fluctuation rate comparison data before and after stabilization, wherein the distributed power supply power data and the regional adjustment power requirements meet fluctuation indexes.

The virtual energy storage energy optimization management strategy under the target of improving the regional supply and demand balance comprises the following steps: and (3) establishing an improved regional supply-demand balance objective function by analyzing regional load prediction data and distributed power supply output prediction data in a set time period, taking the reduced supply-demand deviation as an optimization target, and calculating to obtain adjusted distributed power supply output data, load electricity consumption data, load virtual energy storage adjustment data and supply-demand deviation comparison data before and after adjustment.

The virtual energy storage energy optimization management strategy under the voltage target of the regulating node comprises the following steps: and (3) establishing an adjusting node voltage objective function by analyzing the node voltage actual measurement value and the node voltage limit value, aiming at reducing voltage deviation, and calculating to obtain the total adjusting power requirement and the voltage values before and after optimization.

In step 3, the coordination control mode of the virtual energy storage system and the multi-energy power distribution mode taking the virtual energy storage into consideration are determined by analyzing the virtual energy storage energy optimization management strategy under each optimization target in step 2, and the electricity price information and the user comfort level requirements are comprehensively considered on the basis of the demand response through the control mode and the method of the virtual energy storage.

The coordination control mode of the virtual energy storage system comprises the following steps: the method comprises the steps of constructing a virtual energy storage system, determining an adjusting unit to consider the multi-energy adjustable power and the required adjusting power of the virtual energy storage, referring to a response characteristic model of an adjustable load in a traditional energy storage building system, further solving the multi-energy adjusting power value considered for the virtual energy storage, building a multi-energy control mode considered for the virtual energy storage on the basis, and adopting a centralized control mode as the multi-energy control mode considered for the virtual energy storage.

The multi-time scale power distribution among the units inside the virtual energy storage system comprises the following contents:

3.1 constructing a virtual energy storage system and determining an adjustable unit in the virtual energy storage system;

3.2, dividing the response time of the energy storage device into time scales of different magnitudes;

3.3 calculating the response power of the different types of energy storage;

3.4, dividing the calculated response power values of different energy storage systems into two levels KW and MW;

3.5, obtaining the required total power adjustment quantity and the required response speed in the region managed by the virtual energy storage system according to the multi-objective optimization result;

3.6, according to the total power adjustment quantity and the required response speed, firstly starting the traditional energy storage device to adjust and control, and adjusting power among the traditional energy storage devices according to the response time scale and the response power distribution;

and 3.7, selecting the virtual energy storage considered multi-energy adjustable power with the response time scale meeting the requirement according to the divided response time and response power, and further selecting the virtual energy storage considered multi-energy adjustable power with the charge state meeting the requirement. The method comprises the steps of carrying out a first treatment on the surface of the

3.8 starting the energy storage device according to the sequence from the large power value to the small power value under the condition that 3.7 is satisfied until the accumulated regulating power of the energy storage device is lower than and closest to the total power regulating value;

3.9 subtracting the traditional energy storage adjusting power value from the total power adjusting quantity to be used as an adjusting power value of the load virtual energy storage;

3.10 distributing power among the multiple energies considering virtual energy storage according to a traditional energy storage device power distribution strategy in 3.2, and dividing response time scales of the different types of energy storage according to response speeds:

wherein: t (T) _Li The response time scale of the ith energy storage, i=1, 2 … …, n; ts, tm, th represent response time scales of three levels of seconds, minutes, and hours, respectively.

In 3.3, the response power P of the different types of stored energy is determined _Li ：

P _Li ＝f(t,α,β...) (11)

Wherein: p (P) _Li Output power models representing different loads; t response time; characteristic parameters related to different loads, related to load type.

In 3.4, the obtained response power values of the n energy storage systems are divided into the following two levels:

wherein: p (P) _kW Representing a response power between 0 and 999 kW; p (P) _MW Indicating a response power between 1 MW-infinity.

The application also discloses a multi-energy optimization control system based on client side demand response by utilizing the multi-energy optimization control method, which comprises a multi-energy optimization configuration model configuration unit, a multi-energy optimization target and optimization strategy determination unit and a virtual energy storage power distribution and coordination control unit; the method is characterized in that:

The multi-energy optimal configuration model configuration unit takes the load with energy storage characteristics in the power supply area to be optimally controlled as a load virtual energy storage and an energy storage device to form a virtual energy storage system through analyzing the load and the randomness power supply characteristics of the regional power distribution network, establishes an energy storage device optimal configuration model considering virtual energy storage, and solves the optimal combination of the total capacity of the virtual energy storage system and the response electricity price of the demand side under the condition that constraint conditions are met;

the virtual energy storage power distribution and coordination control unit establishes a multi-energy optimization target of a multi-energy optimization control system, and determines a virtual energy storage energy optimization management strategy under each optimization target;

the virtual energy storage power distribution and coordination control unit realizes power distribution and coordination control of multiple time scales among units in the virtual energy storage system.

It is further preferred that the composition comprises,

the multi-energy optimal configuration model configuration unit comprises an objective function module, a constraint condition module and an optimal configuration model calculation module;

the objective function module is used for establishing an optimal configuration model objective function considering load virtual energy storage;

the constraint condition module is used for establishing required constraint conditions, wherein the constraint conditions comprise an electricity price constraint condition, an energy storage device charge and discharge power constraint condition and an energy storage device SOC constraint condition; the optimal configuration model calculation module is used for solving the multi-energy optimal configuration model based on the optimal configuration model objective function and the constraint condition.

The multi-energy optimization target and optimization strategy determining unit comprises an optimization target determining module and a virtual energy storage energy optimization management strategy calculating module;

the optimization target determining module determines a multi-energy optimization target according to actual requirements, wherein the multi-energy optimization target comprises an optimization target for stabilizing random power supply fluctuation, an optimization target for improving regional supply and demand balance and an optimization target for adjusting node voltage;

and the virtual energy storage energy optimization management strategy calculation module calculates the total required virtual energy storage adjustment power under each optimization target respectively.

The virtual energy storage power distribution and coordination control unit comprises a power distribution module and a coordination control module;

the power distribution module is used for realizing distribution of the total regulating power of the needed virtual energy storage among the virtual energy storage; the coordination control module realizes the switching of the centralized control mode and the distributed control mode.

Compared with the prior art, the invention has the following beneficial technical effects:

aiming at a virtual energy storage system formed by a user side multi-element load, energy storage and a distributed power supply, intensive researches are conducted on load virtual energy storage response characteristics and models, energy storage characteristics and an optimal configuration technology, virtual energy storage optimal management and coordination control technology and the like, virtual energy storage coordination control software is developed and demonstrated, continuous adjustment capability under various application functions is enhanced, supply and demand balance of a receiving end power grid is supported, safe and economic operation of the power grid is guaranteed, power consumption data value is mined by applying a big data analysis technology, user side resource response capability is improved, and user side regulation capability and distribution network safe operation level are improved.

Drawings

FIG. 1 is a flow chart of a multi-energy optimization control method based on client side demand response of the invention;

FIG. 2 is a schematic diagram of an energy storage optimization configuration flow;

FIG. 3 is a graph showing the comparison of load curves after time-of-use electricity price and energy storage;

FIG. 4 is a schematic flow diagram of a virtual stored energy power allocation strategy;

FIG. 5 is a block diagram of a multi-energy optimization control system based on user-side demand response in accordance with the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are merely some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art without making any inventive effort, are within the scope of the present invention.

Referring to fig. 1, the invention discloses a multi-energy optimal control method based on client side demand response, which comprises the following steps:

step 1, establishing a multi-energy optimal configuration model considering virtual energy storage

In the present application, a load (also referred to as a virtual load in the present application) having energy storage characteristics in an area, such as an air conditioner, a refrigerator, a water heater, and the like, and a conventional energy storage device (also referred to as an energy storage device, a conventional energy storage device, or an energy storage device in the present application) together form a virtual energy storage system (also referred to as a virtual energy storage system in the present application).

According to load characteristic distribution and random power supply fluctuation conditions, the function and functional requirements of the traditional energy storage device in a distribution network are provided through data simulation. On the basis, a traditional energy storage optimizing configuration method considering load virtual energy storage is provided, peak clipping and valley filling are taken as operation targets, the total cost of a virtual energy storage system which is operated by combining load virtual energy storage based on demand response and a traditional energy storage device is taken as an optimizing target to establish an optimizing configuration model, under the condition that the power grid operation constraint and the energy storage operation constraint are met, the optimal combination of the total capacity of the energy storage system and the response electricity price of the demand side is solved, and after virtual energy storage is introduced through practical case verification, the peak clipping and valley filling effect of the load is further improved by the traditional energy storage device, and the peak valley difference of the system load is effectively reduced.

(1) Optimized configuration principle of traditional energy storage device

The following principles should be followed in the selection of the stored energy installation location:

1) As far as possible at the end of the distribution line. The voltage problem is more likely to occur at the tail end of the distribution line, and the energy storage is configured closer to the tail end (close to the load) of the transmission line, so that the effect of reducing the loss of the transmission line and raising the voltage of the load node at the tail end is more obvious.

2) When the power supply is used for stabilizing the power fluctuation of the random power supply, the random power supply connected to the load point is installed at the load point as much as possible, so that the influence of the power fluctuation on the load can be reduced.

3) When the voltage regulator is used for regulating the voltage of the node, the node with the voltage supply problem is preferentially considered. Analyzing the trend change along with load growth or random power supply access, and if the distribution transformer is overloaded, installing energy storage on the low-voltage side of the transformer; if overload of the line occurs, the energy storage is installed at the downstream of the section of line; if a voltage quality problem occurs, the energy storage should be installed at the node where the voltage problem occurs.

4) When no power supply problem exists, the node with high voltage sensitivity is prioritized. The higher the node voltage sensitivity, the more the node voltage is affected by the access of the energy storage system, and the more the voltage of the nodes is supported by the energy storage.

5) When used to improve power quality, decentralized mounting is preferred over centralized mounting. The power supply system is arranged on the user side in a scattered manner, so that the dependence on power supply of a power grid can be effectively reduced, and the line loss is reduced.

6) When the system is used for improving unbalance of supply and demand, centralized configuration is preferable.

According to the principle, the energy storage configuration flow of the power distribution network is shown in fig. 2.

(2) Optimal configuration model considering load virtual energy storage

1) Objective function

Based on the analysis of the load and the randomness power characteristics of the regional power distribution network, the power distribution network with the random power access mainly has the problems of large load peak-valley difference, random power fluctuation, possible node voltage fluctuation and the like. Therefore, the functional requirements of the energy storage configuration are mainly peak load shedding, surge stabilization and voltage regulation. The adjustment costs are minimized while meeting the above specifications.

In the embodiment of the invention, peak clipping and valley filling are taken as main operation targets, the energy storage device takes the storage battery as an example, an optimization model is established by taking the minimum total cost of combined operation of demand response and energy storage as an objective function, and the optimal combination of the total capacity of the storage battery and the response electricity price of the demand side is solved under the condition of meeting the constraint of power grid voltage and tide operation. The specific objective function is set as follows:

min f＝C _bess +C _bess.om -C _g (13)

wherein: c (C) _bess Building cost (annual value of the same year) for the storage battery pack; c (C) _bess.om The annual operation maintenance cost of the storage battery pack is saved; c (C) _g And the method is used for obtaining the power grid benefits for peak clipping and valley filling.

Construction cost C of storage battery pack _bess The method is in direct proportion to the total capacity of the storage battery, meanwhile, the discount rate and the service life are also required to be considered when the discount rate and the service life are converted into equivalent annual values, and the specific formula is as follows:

Wherein: c (C) _bess Building cost (annual value of the same year) for the storage battery pack; e (E) _bess Rated capacity of the storage battery (MWh); k (k) _e The construction cost is the unit capacity of the storage battery; r is the discount rate; h is the service life.

Annual running maintenance cost C of storage battery pack _bess.om It can be considered as proportional to the total capacity of the battery pack, and the equivalent conversion is unnecessary, so the specific formula is:

wherein: c (C) _bess.om The annual operation maintenance cost of the storage battery pack is saved; e (E) _bess Rated installation capacity for the storage battery; k (k) _om And running and maintaining the cost for the unit capacity of the storage battery.

The benefits brought by peak clipping and valley filling are mainly based on the peak value difference and the valley value difference of the power grid load before and after peak clipping and valley filling. When the peak value of the load is reduced, the investment level of the power grid per se is also reduced, and a plurality of power grid investments including spare capacity and the like can be reduced, thereby bringing benefits to the power grid, and the peak clipping benefit coefficient corresponding to the benefits is set as a; after the valley value of the load is increased, the utilization rate of power grid equipment is increased, and the peak valley difference is reduced, so that corresponding benefits can be brought to the power grid, and the valley filling benefit coefficient corresponding to the benefits is set as b. Considering the charge and discharge power of the energy storage system, the gain function of peak clipping and valley filling is obtained as follows:

C _g ＝a×∑(P _M0 -P _L )·Δt+b∑(P _L -P _mo )·Δt (16)

wherein: a peak clipping gain coefficient; b, filling the grain gain coefficient; p (P) _M0 Peak load before demand response; p (P) _m0 Valley load before demand response; p (P) _L Load after demand response.

The charge-discharge operation strategy and the charge-discharge power of the energy storage are as follows:

wherein: p (P) _bess (n) energy storage charge-discharge reference power.

2) Constraint conditions

The constraint conditions to be added on the basis of the minimum sum of the demand side response and the total cost of the energy storage device are mainly electricity price constraint, storage battery charging and discharging power constraint and storage battery SOC constraint.

Electricity price constraint: in actual life, the difference between the peak electricity price and the ordinary electricity price and the valley electricity price cannot be too large, so that related constraint needs to be set, and the specific formula is as follows:

p _min ≤p _v ≤p _f ≤p _p ≤p _max (18)

Battery pack SOC constraints: if the value of the residual electric quantity in the storage battery pack is too high or too low, the storage battery pack can be adversely affected, and the service life of the storage battery pack is reduced. In order to realize that the storage battery pack can continuously and normally work in the expected service life, the residual electric quantity level of the storage battery pack needs to be restrained, and the specific formula is as follows:

SOC _min ≤SOC(t)≤SOC _max (19)

0≤E(t)≤E _bess (20)

wherein SOC is _min The SOC (t) is the current state of charge value of the storage battery, and is the minimum state of charge of the storage battery _max For the maximum state of charge of the battery, E (t) is the battery capacity, E _bess The rated installation capacity of the storage battery is provided.

And (3) restraining the charge and discharge power of the storage battery pack: in the practical process, the charging and discharging power of the storage battery pack has upper and lower limit values, so that constraint conditions are required to be set for the storage battery pack. During the charging of the battery energy storage system, its value is positive, since it can be considered as a load; during its discharge, it is considered a power generation device, so its value is negative. On this basis, the specific formula is obtained as follows:

-P _N ≤P _bess (n)≤P _N (21)

wherein P is _bess (n) charge/discharge power of the storage battery pack, P _N Rated power for the storage battery.

Examples: the method is characterized in that a typical daily load curve in summer in a certain area is taken as a research object, the optimization target is that the total economic benefit is maximum (the total cost is minimum), peak clipping and valley filling control is implemented on the load, the electricity price is divided into peak-to-valley electricity price, the setting of the electricity price when the area is calculated in an optimized mode, the selection of the energy storage capacity and the charging and discharging power of energy storage in each period of 24 hours a day are carried out. Performing simulation verification on an optimal configuration model considering load virtual energy storage, wherein the verification content comprises the following steps: time-of-use electricity price, configuration of energy storage capacity and charge and discharge power of energy storage in each period.

Prior to calculation, the data required in the examples are as follows:

1) Load transfer coefficient

The load transfer coefficient has no practical solving formula, and the embodiment of the invention adopts historical empirical values, and the empirical values are as follows:

wherein p, f, v represent peak-to-valley periods, respectively. E (p, p) represents a load transfer coefficient between peak periods, E (f, f) represents a load transfer coefficient between normal periods, E (v, v) represents a load transfer coefficient between valley periods, E (f, p) and E (p, f) represent load transfer coefficients between peak and normal periods, E (p, v) and E (v, p) represent load transfer coefficients between peak and valley periods, and E (v, f) and E (f, v) represent load transfer coefficients between flat and valley periods.

2) Division of peak-to-valley time periods and initial electricity prices

In this embodiment, the time-sharing electricity price adopts a peak-to-valley electricity price, and the peak-to-valley period is divided as follows:

peak period: 11:00-16:00

Flat period: 9:00-11:00, 16:00-24:00

Cereal period: 1:00-9:00, 24:00-1:00 the next day

Initial electricity rate (electricity rate before time-of-use electricity rate is not implemented): 0.7 yuan/kWh

3) Energy storage power station parameters

Unit capacity construction cost of energy storage power station: k (k) _e =2500 yuan/kWh

Annual operation maintenance cost of energy storage power station unit capacity: k (k) _om =25 yuan/kWh

Design life of energy storage power station: h=15 years

The discount rate of the energy storage power station: r=0.1

Peak clipping and valley filling benefit coefficient: a=0.105, b=0.105

Maximum charge-discharge power of energy storage power station: p (P) _d,max ＝P _c,max ＝5MW

State of charge constraints of the energy storage battery: SOC (State of Charge) _min ＝0.1，SOC _max ＝0.9

4) Daily load parameter

Daily load curves generally take predictive data. In the invention, the typical daily forecast load is selected for the optimal configuration research of the energy storage capacity. Typical daily load forecast data for this region are shown in the following table:

TABLE 1 typical daily load in Beijing area

Time	Load MW	Time	Load MW
					1	78.24	13	116.22
2	51.87	14	130.09
				3	47.65	15	120.44
4	46.95	16	118.68
				5	48	17	118.68
6	48	18	117.98
				7	65.23	19	111.65
8	67.69	20	113.41
				9	103.91	21	103.91
10	125.01	22	87.74
				11	132.75	23	80.7
12	119.74	24	76.48

5) Calculation result

And establishing a corresponding optimization model, and calculating the optimal time-sharing electricity price, the configuration of the energy storage capacity and the charge and discharge power of each energy storage period. The results of the optimization calculation are shown in the following table:

table 2 example optimization calculation results

/>

In Table 2, p _v 、p _f 、p _p For the calculated time-of-use electricity price E _ESS Is the capacity of the energy storage power station, and the unit is kWh and P _ESS (0)～P _ESS (23) The unit of charging and discharging power of the energy storage power station in each period of one day is kW.

Further, after the time-sharing electricity price (without energy storage) and the time-sharing electricity price plus energy storage are respectively compared, as shown in fig. 3, the change situation of the load curve can be seen, after the time-sharing electricity price is singly implemented, the peak value of the original load of the system is reduced by about 30MW, the valley value is increased by about 25MW, and the time-sharing electricity price is implemented, so that the system has good peak clipping and valley filling effects; after the energy storage is introduced, the energy storage further improves the peak clipping and valley filling effects of the load, and effectively reduces the peak valley difference of the system load.

Step 2, establishing a multi-energy optimization target based on the client-side energy utilization control system

A virtual energy storage system is constructed by controllable load and energy storage in a 10kV/400V typical area, optimization targets of stabilizing random power supply fluctuation, improving regional supply and demand balance and adjusting node voltage are considered, and a virtual energy storage energy optimization management strategy under each optimization target is provided.

(1) Stabilizing random power supply fluctuation

And obtaining distributed power data meeting fluctuation indexes, regional adjustment power requirements and fluctuation rate comparison data before and after stabilization according to the set time scale, the fluctuation rate limit value and the distributed power output prediction data. In an embodiment, the photovoltaic power generation system further comprises an output interface, wherein the interface is divided into three areas, namely a photovoltaic area, a wind power area and a virtual energy storage area, wherein a photovoltaic predicted force curve, a photovoltaic actual force curve, a photovoltaic optimization curve and a photovoltaic force curve after actual coordination control are displayed in a coordinate plane, comparison is facilitated, and the wind power area, the virtual energy storage area and the photovoltaic power area are the same.

Objective function:

calculating the fluctuation amount of the set T time scale according to the predicted output:

ΔP _T ＝max P(τ)-minP(τ),τ∈[t,t+T) (23)

wherein: ΔP _T Work on the T time scaleRate fluctuation amount, kW; max P (τ), min P (τ) are the maximum and minimum output powers, kW, respectively, for the continuous period T.

Judging whether the fluctuation amount is smaller than or equal to a set limit value, and if so, eliminating the need of virtual energy storage adjustment; if it is larger, the maximum value is reduced (ΔP _T - Δp)/2, minimum increase (Δp _T - Δp)/2, again calculating the decision until the limit value is met. Thus, the total regulated power of the virtual energy storage required is as follows:

wherein: p (P) _ref The total power of the virtual energy storage is required to be regulated, and kW is required to be regulated; ΔP is the power fluctuation limit on the T time scale, kW.

(2) Improving regional supply and demand balance

And in a set time period, according to the load prediction data and the distributed power supply output prediction data in the area, taking the supply and demand deviation reduction as an optimization target, and outputting the adjusted distributed power supply output data, the load electricity utilization data, the load virtual energy storage adjustment data and the supply and demand deviation comparison data before and after adjustment through optimization calculation. Regional supply and demand balancing strategy: when the photovoltaic output is larger than the load output, the virtual energy storage system is required to be charged, and redundant photovoltaic output is absorbed; when the photovoltaic output force is smaller than the load output force, the virtual energy storage system is required to discharge, and insufficient power is compensated. Therefore, with charge negative and discharge positive, the total regulated power reference for the desired virtual energy storage system is as follows:

P _ref ＝P _lf -P _vf (25)

Wherein: s is S _k Short circuit capacity of grid-connected access points; p (P) _ref The total power of the virtual energy storage is required to be regulated, and kW is required to be regulated; p (P) _lf Load prediction power, kW; p (P) _vf Photovoltaic predicted power, kW.

(3) And according to the actual measurement value of the node voltage and the node voltage limit value, taking voltage deviation reduction as a target, outputting the total regulation power requirement and the voltage values before and after optimization through optimization calculation.

When the system load changes, the current on the line will change to Δi, and the voltage at the corresponding access point will change to Δu. The voltage variation value at the grid-connected point when the load power fluctuates can be simply estimated as follows.

Objective function:

wherein: s is S _k Short circuit capacity of grid-connected access points; ΔS _l Load power variation of the system; θ is a power factor variation value caused by load increase; r is R _l +jX _l Load equivalent impedance; u access point voltage;

is the grid impedance angle seen from the access point.

In general, the phase shift across the line is not large, Δu approximates its horizontal component, and its vertical component is negligible, so that the relative rate of change of the voltage can be obtained as follows.

From the above equation, the voltage relative change rate depends on ΔS _l 、S _k The three main factors affecting the system supply voltage deviation are the variation of the load power, the short-circuit capacity of the incorporated system and the power factor of the system. The maximum factor for improving voltage deviation after energy storage is connected is that the change condition of load power can be effectively regulated, and the delta Sl value is reduced through reasonable charge and discharge control so as to achieve the purpose of reducing voltage change.

When the voltage is in positive deviation, the energy storage is needed to charge and absorb power, and when the voltage is in negative deviation, the energy storage is needed to discharge. And the virtual energy storage is charged to be negative, and the discharge is positive, so that the required regulation power is obtained as follows:

is the grid impedance angle seen from the access point.

Step 3, power distribution and coordination control of multiple time scales among units in the virtual energy storage system

The multiple time scale power distribution and coordination control modes between the units in the virtual energy storage system are numerous on the basis of the

steps

1 and 2, and the multiple time scale power distribution and coordination control modes comprise a hybrid energy storage VSG power coordination control strategy, a fuzzy optimization strategy of an energy storage unit, an advanced control strategy, IDA-PBC-based hybrid energy storage control, switch control, temperature control and periodic pause control. Both of these power distribution and coordination control modes can achieve the expected technical effects.

However, in order to obtain a better technical effect, the following multi-time-scale power distribution method is preferably adopted in the embodiment of the present invention.

(1) A virtual stored energy power allocation strategy flow diagram is shown in fig. 4. The method comprises the following steps:

1) The load with energy storage characteristics in the area, such as an air conditioner, a refrigerator, a water heater and the like, and the traditional energy storage together form a virtual energy storage system VESS. The regulating unit is determined to be a multi-energy adjustable power taking virtual energy storage into account and a required regulated power.

2) The response time scales of the different types of energy storage are divided according to the response speed, and the response time scales comprise a second level, a minute level and an hour level:

wherein: t (T) _Li Ith reservoirA response time scale of energy, i=1, 2 … …, n; ts, tm, th represent response time scales of three levels of seconds, minutes, and hours, respectively.

3) Calculating the response power P of the different types of energy storage _Li ：

P _Li ＝f(t,α,β...) (30)

4) The obtained response power values of the n energy storage systems are divided into the following two levels:

5) And according to the multi-objective optimization result, stabilizing random power supply fluctuation, improving regional supply and demand balance and regulating node voltage, obtaining the required total power regulating quantity and the required response speed in the region managed by the virtual energy storage system.

6) And according to the total power adjustment quantity and the required response speed, firstly starting the traditional energy storage adjustment control, and adjusting power among the traditional energy storage units according to the response time scale and the response power distribution.

7) Selecting a virtual energy storage considered multi-energy adjustable power with the response time scale meeting the requirement according to the divided response time and response power;

8) Under the condition of meeting the step 7), starting the energy storage device according to the sequence from the large power value to the small power value which is smaller than the total power adjustment amount until the accumulated adjustment power of the energy storage device is lower than and closest to the total power adjustment amount;

9) Subtracting the traditional energy storage adjusting power value from the total power adjusting quantity to serve as an adjusting power value of the load virtual energy storage;

10 A) distributing power among the pluripotency considering the virtual energy storage according to a conventional energy storage device power distribution strategy.

The power distribution strategy of the traditional energy storage unit is that of the traditional energy storage unit in the prior art:

a single type of energy storage means both battery energy storage systems or both super capacitor energy storage systems, where a plurality of distributed battery energy storage systems are configured in the system as an example. The distributed energy storage systems do not consider the difference in space (i.e. do not consider the influence of the installation position of the energy storage), only consider the output strategy when the states of the energy storage systems are respectively different in time and deal with different load types and load changes, and fully consider the power and time required for stabilizing the load (because only one energy storage is used, the load types have little difference on the energy storage). The method mainly considers the influence of factors such as the capacity of each energy storage system, the state of charge (SOC) of a battery, instantaneous allowed charge and discharge power, instantaneous allowed switching power, long-term allowed charge and discharge power and the like on the current power distribution, and because the energy storage types are the same, the power distribution is calculated only by adopting an algebraic addition and subtraction method, and processes such as filtering and the like are not needed.

Here, the economic and technical constraint conditions of the energy storage body are met, and two factors are mainly considered: 1) Energy storage rated power and capacity; 2) The state of charge of the energy storage system. The following cases can be classified.

1) The charge states of the distributed energy storage systems are the same, and rated power and capacity are also the same

Under the condition that the parameters and the states of the distributed energy storage systems are the same, the power is distributed by adopting a load sharing method, and the energy storage total regulating power obtained by wind storage/light storage coordination control is shared, as shown in a formula (32).

Wherein, the liquid crystal display device comprises a liquid crystal display device,

power allocated to the kth energy storage system; n is the number of distributed energy storage systems.

2) The charge states of the distributed energy storage systems are the same, and rated power and capacity are different

When the rated power and capacity of each distributed energy storage system are different, a load sharing method cannot be adopted. Under the condition of the same state of charge, the rated power is high, and more power should be distributed; the rated power is small and less power should be allocated, i.e., proportionally allocated based on the rated power, as shown in formulas (3-11).

power allocated to the kth energy storage system; />

Is the rated power of the kth energy storage system.

3) The charge states of the distributed energy storage systems are different

When the states of charge of the distributed energy storage systems are different, in order to ensure the safe and economical operation of the energy storage systems, power distribution is carried out by taking the real-time state of charge (SOC) of the energy storage systems as a constraint condition. The SOC is divided into five sections, and the charge and discharge power of each section is determined according to the charge and discharge characteristic curve and the SOC value of the battery. The SOC partition and each partition energy storage charge and discharge limiting conditions are as follows:

1) Upper limit region of SOC: SOC is greater than or equal to SOC _max When the energy storage battery is in a normal state, the energy storage battery is in a normal state;

2) SOC high limit region: SOC (State of Charge) _high ≤SOC＜SOC _max When the energy storage battery is in a basic principle of less charge and more discharge, the increase rate of the SOC is slowed down as much as possible;

3) SOC normal operating region: SOC (State of Charge) _low ≤SOC＜SOC _high When the energy storage battery is in a normal charge and discharge state;

4) SOC low limit region: SOC (State of Charge) _min ≤SOC＜SOC _low When the energy is stored in the batteryThe principle of low discharge and high charge is to slow down the rate of decrease of SOC as much as possible;

5) Lower limit region of SOC: SOC < SOC _min When the energy storage battery is in a normal state, the energy storage battery is in a normal state;

according to the above-mentioned partitions, the total charge-discharge power, charge quantity and discharge quantity balance are followed, and the power calculation formula and constraint condition are shown as formula (34) to formula (38).

rated power for the kth energy storage system; />

Rated capacity of the kth energy storage system; / >

The charging power at the ith moment of the kth energy storage system; />

The discharge power at the ith moment of the kth energy storage system; p (P) _TCi The total charging power required to be regulated for the energy storage system at the ith moment; p (P) _TDi The total discharge power required to be regulated by the energy storage system at the ith moment; t (T) _C Charging time for the kth energy storage system; t (T) _D The discharge time of the kth energy storage system; alpha is the charging adjustment coefficient of the energy storage system, and is generally in the range of [0,1 ] according to the partition of the SOC and the charging characteristic curves of different energy storage batteries]In the range, the fine adjustment is carried out on the power balance of the whole energy storage system; beta is a discharge regulation coefficient of the energy storage system, and the value is taken into consideration according to the partition of the SOC and the discharge characteristic curves of different energy storage batteries, and the power balance of the whole energy storage system is finely tuned; SOC (State of Charge) _max 、SOC _min The upper limit value and the lower limit value of the energy storage SOC are respectively, the values of different energy storage batteries are different, for example, 90% -40% of lead-acid batteries are generally taken, 90% -10% of lithium batteries are generally taken, and the energy storage SOC is determined according to the requirements of the batteries in practical application. SOC (State of Charge) _high 、SOC _low The values of different energy storage batteries are different, such as 70% -50% of lead-acid batteries and 80% -20% of lithium batteries, and the values are determined according to the requirements of the batteries in practical application.

(2) Multi-energy control mode considering virtual energy storage

In the power market environment, demand responses can be classified into price-based demand responses and incentive-based demand responses in different ways of user response. The price-based demand response is quite voluntary in the process of participating in the demand response, so that the randomness is high, and the target of the demand response cannot be accurately completed, and therefore, the air conditioner load is less researched in the demand response. After integrating the air conditioner load into a polymer, the control modes of the air conditioner participating in the system operation can be mainly divided into: a centralized control mode and a distributed control mode.

1) Centralized control mode: the load control center directly decomposes the control instruction and directly transmits the control information to each air conditioning load in the aggregate. The control mode has high requirements on real-time performance, confidentiality and safety of information, and a special power information transmission channel needs to be paved between a load control center and an air conditioner load.

2) Distributed control mode: the intelligent control device is arranged on the air conditioner load, and the control device combines the signal sent by the load control center and the state of the air conditioner to generate a control signal of the air conditioner. The distributed control mode does not relate to a bidirectional communication mechanism of a load control center and a user side, and adverse factors such as complexity, unreliability and the like in the communication process are avoided. However, this control method cannot accurately provide the capacity required at the current moment, which is easy to cause insufficient response capacity or excessive response.

On the basis of demand response, the electricity price information and the user comfort level requirements are comprehensively considered, the method is more suitable for controlling temperature control type load virtual energy storage by adopting a centralized control mode and a temperature control method, mainly transfers the load from a high electricity price period to a low electricity price period according to the change condition of electricity price and outdoor temperature, reduces the user cost, or adjusts a temperature setting value according to the system operation condition, provides auxiliary service for the system, and ensures the stable operation of the power system.

As shown in fig. 5, the application also discloses a multi-energy optimization control system based on client side demand response by using the multi-energy optimization control method, which comprises a multi-energy optimization configuration model configuration unit, a multi-energy optimization target and optimization strategy determination unit, and a virtual energy storage power distribution and coordination control unit.

The multi-energy optimal configuration model configuration unit is used for forming a virtual energy storage system by analyzing the load and the randomness power supply characteristic of the regional power distribution network, taking the load with the energy storage characteristic in the power supply region to be optimally controlled as a load virtual energy storage and an energy storage device together, establishing an energy storage device optimal configuration model considering virtual energy storage, and solving the optimal combination of the total capacity of the virtual energy storage system and the response electricity price of the demand side under the condition that constraint conditions are met.

The virtual energy storage power distribution and coordination control unit is used for realizing power distribution and coordination control of multiple time scales among units in the virtual energy storage system;

Finally, it should be noted that: the above embodiments are only for illustrating the technical aspects of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the above embodiments, it should be understood by those of ordinary skill in the art that: modifications and equivalents may be made to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is intended to be covered by the claims.

Claims

1. The multi-energy optimal control method based on the client side demand response is characterized by comprising the following steps of:

Step 1, establishing a multi-energy optimal configuration model considering virtual energy storage; the method comprises the steps that a load with energy storage characteristics in a power supply area to be optimally controlled is used as a load virtual energy storage system and an energy storage device to form a virtual energy storage system, an energy storage device optimal configuration model considering virtual energy storage is established, and under the condition that constraint conditions are met, the optimal combination of the total capacity of the virtual energy storage system and the response electricity price of a demand side is solved; the optimal configuration model considering the load virtual energy storage comprises an objective function and constraint conditions to be met, wherein the constraint conditions comprise an electricity price constraint condition, an energy storage device charge and discharge power constraint condition and an energy storage device SOC constraint condition; the optimal configuration model objective function considering the load virtual energy storage is as follows:

minf＝C _bess +C _bess.om -C _g (1)

wherein: c (C) _bess Building cost for the energy storage device; c (C) _bess.om The annual operation maintenance cost of the storage battery pack is saved; c (C) _g The method is used for obtaining the power grid benefits for peak clipping and valley filling; construction cost C of energy storage device _bess The calculation formula is as follows:

wherein: c (C) _bess The construction cost of the energy storage device is equal in annual value; e (E) _bess Rated installation capacity of the energy storage device is given by MWh; k (k) _e The construction cost is the unit capacity of the energy storage device; r is the discount rate; h is the service life; annual operation maintenance cost C of energy storage device _bess.om The calculation formula of (2) is as follows:

wherein: c (C) _bess.om The annual operation maintenance cost of the energy storage device is saved; e (E) _bess Rated installation capacity for the energy storage device; k (k) _om The maintenance cost is operated for the unit capacity of the energy storage device; the gain function brought by peak clipping and valley filling is as follows:

C _g ＝a×∑(P _M0 -P _L )·Δt+b∑(P _L -P _mo )·Δt (4)

wherein: a peak clipping gain coefficient; b, filling the grain gain coefficient; p (P) _M0 Peak load before demand response; p (P) _m0 Valley load before demand response; p (P) _L Load after demand response, Δt represents duration of peak elimination or valley filling;

wherein: p (P) _bess (n) represents the n-th energy storage charging device discharge reference power; the calculation formula of the electricity price constraint condition is as follows:

p _min ≤p _v ≤p _f ≤p _p ≤p _max (6)

wherein p is _min To restrict the lowest electricity price, p _v To underestimate electricity price, p _f At the usual level, p _p For peak electricity price, p _max To restrict the highest electricity price; the constraint condition of the SOC of the energy storage device is as follows:

SOC _min ≤SOC(t)≤SOC _max (7)

0≤E(t)≤E _bess (8)

wherein SOC is _min For the minimum state of charge of the energy storage device, SOC (t) is the current state of charge of the energy storage device, and SOC _max For maximum state of charge of the energy storage device, E (t) is the capacity of the energy storage device, E _bess Rated installation capacity for energy storage devicesThe method comprises the steps of carrying out a first treatment on the surface of the The constraint conditions of the charge and discharge power of the energy storage device are as follows:

-P _N ≤P _bess (n)≤P _N (9)

wherein P is _bess (n) charge/discharge power of energy storage device, P _N Rated power of the energy storage device;

step 3, realizing power distribution and coordination control of multiple time scales among units in the virtual energy storage system; the virtual energy storage power distribution and coordination control unit comprises a power distribution module and a coordination control module;

2. The client side demand response based multi-energy optimal control method according to claim 1, wherein:

3. The client side demand response based multi-energy optimal control method according to claim 2, wherein:

4. The client side demand response based multi-energy optimal control method according to claim 1, wherein:

5. The client side demand response based multi-energy optimal control method of claim 4, wherein:

6. The client side demand response based multi-energy optimal control method of claim 4, wherein:

7. The client side demand response based multi-energy optimal control method of claim 4, wherein:

8. The client side demand response based multi-energy optimal control method according to claim 1, wherein:

9. The client side demand response based multi-energy optimal control method of claim 8, wherein:

10. The client side demand response based multi-energy optimal control method of claim 9, wherein:

3.3, calculating the response power of different types of energy storage;

3.7, selecting the virtual energy storage considered multi-energy adjustable power with the response time scale meeting the requirement according to the divided response time and response power, and further selecting the virtual energy storage considered multi-energy adjustable power with the charge state meeting the requirement;

3.10 Power is distributed among the pluripotency considering the virtual energy storage according to conventional energy storage device power distribution strategies.

11. The client side demand response based multi-energy optimal control method of claim 10, wherein:

in 3.2, the response time scales of different types of stored energy are divided according to the response speed:

12. The client side demand response based multi-energy optimization control method according to claim 10 or 11, wherein:

P _Li ＝f(t,α,β...) (11)

13. The client side demand response based multi-energy optimal control method of claim 12, wherein:

14. A multi-energy optimization control system based on client side demand response using the multi-energy optimization control method of any one of claims 1-13, comprising a multi-energy optimization configuration model configuration unit, a multi-energy optimization target and optimization strategy determination unit, a virtual stored energy power allocation and coordination control unit; the method is characterized in that:

15. The client side demand response based multi-energy optimal control system of claim 14, wherein:

16. The client side demand response based multi-energy optimal control system of claim 14 or 15, wherein: