CN110380405B - Micro-grid operation method based on cooperative optimization of demand response and energy storage - Google Patents
Micro-grid operation method based on cooperative optimization of demand response and energy storage Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
- H02J3/32—Arrangements for balancing of the load in a network by storage of energy using batteries with converting means
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses a micro-grid operation method based on cooperative optimization of demand response and energy storage, which relates to the technical field of micro-grid operation optimization, and comprises the following steps: step 1, establishing a collaborative optimization model considering demand response and energy storage for a micro-grid; and step 2, calculating an optimal value of the collaborative optimization model to obtain an operation scheme of the micro-grid. The invention fully considers the response cost of the user for the demand response model, compensates according to the enthusiasm of the user response, and excites the enthusiasm of the user to participate in the demand response. For the problem of larger service life loss of the storage battery device, the influence of the storage battery charge quantity with the largest influence on economic dispatch is considered by researching the factors influencing the service life of the storage battery, and the operation cost of the storage battery is built, so that the effect of quantitative control is achieved.
Description
Technical Field
The invention relates to the technical field of micro-grid optimization operation, in particular to a micro-grid operation method based on cooperative optimization of demand response and energy storage.
Background
The Demand Response (DR) refers to a power consumption behavior of a user for relieving the tension of power supply by changing its conventional power consumption habit after receiving a power price signal or an excitation signal sent by an operator. DR can be divided into an incentive type DR and a price type DR. The introduction of demand response in the micro-grid ensures that both the power supply side and the demand side meet the optimal operating conditions, which can improve the flexibility of the grid to a certain extent, and alleviate the influence of intermittent renewable energy sources, thereby helping to build a more economical and reliable micro-grid.
At present, a plurality of research results are available for research on demand response at home and abroad. The application of demand response in the optimized operation of a micro-grid is an important aspect, and Shao Jingke, wang, tan Yanggong and the like, namely, micro-grid economic optimization scheduling (electric power system and automation report thereof, 2016,28 (10): 31-36.) which are related to demand side response, take transferable loads as main research objects, and combine time-of-use electricity prices to construct a micro-grid optimization scheduling model related to demand response from the electricity demand of users and the economic and environmental benefits of the micro-grid. Sun Yujun, wang Yan, li Qiushuo et al (southern grid technology, 2017, 11 (6): 63-69.) model of two-stage rolling schedule taking into account user-side interactions) simulate the user's response to electricity price changes based on consumer psychology principles, introducing user satisfaction constraints into the schedule model. The model establishes satisfaction constraints only from the changes before and after the average price of electricity, and does not take into account the impact of the operator's provided compensation to the user. The intelligent community flexible load shedding real-time demand response strategy (power system protection and control, 2019,47 (10): 42-50.) by Nansbo, li Gengyin, zhouming and the like divides the reducible load into an interruptible load and an adjustable load, and only sets the power level for the adjustable load by using user illumination and temperature comfort.
The energy storage device can effectively solve the problems of unbalance supply and demand and wind and light abandoning in the system, and is an important device for maintaining stable operation of the micro-grid. Because of the large life loss of the energy storage device, frequent replacement is required, and thus the operation condition of the energy storage device needs to be controlled. Currently, there are two main ways of research for economic dispatch of micro-grids taking into account battery life. Firstly, the times of charge and discharge conversion of a storage battery in a dispatching cycle are limited, such as Yang Xiu, chen Jie, zhu lan and the like, which are written in micro-grid energy storage optimizing configuration based on economic dispatching (protection and control of a power system, 2013,41 (1): 53-60). Secondly, the charge of the battery is limited to a certain range, such as Mercier P, cherkaoui R, oudalov A (Optimizing a Battery Energy Storage System for Frequency Control Application in an Isolated Power System) (IEEE Transactions on Power Systems,2009,24 (3): 1469-1477.).
The existing demand response model does not consider the satisfaction degree of users participating in the demand response project, and lacks modeling of user behavior. And the compensation of users participating in the demand response project is considered as a fixed compensation price, so that the users cannot be fully motivated and guided to participate in the demand response.
The economic dispatch of micro-grid considering the service life of storage battery mainly has the following two research modes. Firstly, the number of times of charge and discharge conversion of the storage battery in a dispatching period is limited, however, the specific limited times are required to be set according to actual conditions, and the specific values of the specific times are not well determined. In addition, the storage battery with too severe limitation of the number of times may not fully exert the effect of relieving the load fluctuation, and the economy is poor; or the discharge power is overlarge at a certain time, the discharge depth is larger, and the service life is shortened. Therefore, it is not reasonable to pursue a reduction in the number of charge-discharge conversion times of the battery to extend the life of the battery. And secondly, the charge quantity of the storage battery is limited in a certain range, but the consideration of the method is not comprehensive, the specific influence of the charge quantity is not considered, and only qualitative control is performed.
Therefore, those skilled in the art are working to develop a method for optimizing the operation of the micro-grid by taking into account the demand response and the energy storage, and for the demand response model, fully taking into account the response cost of the user, compensating according to the aggressiveness of the user response, and exciting the aggressiveness of the user to participate in the demand response. For the problem of larger service life loss of the storage battery device, the influence of the storage battery charge quantity with the largest influence on economic dispatch is considered by researching the factors influencing the service life of the storage battery, and the operation cost of the storage battery is built, so that the effect of quantitative control is achieved.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention aims to solve the technical problems of fully considering the response cost of the user during the optimized operation of the micro-grid, compensating according to the aggressiveness of the user response, exciting the aggressiveness of the user to participate in the demand response, considering the influence of the charge quantity of the storage battery, constructing the operation cost of the storage battery, and achieving the effect of quantitative control.
In order to achieve the above object, the present invention provides a method for optimizing operation of a micro-grid in cooperation with consideration of demand response and energy storage, which is characterized in that the method comprises the following steps:
and step 2, calculating an optimal value of the collaborative optimization model to obtain an operation scheme of the micro-grid.
Further, the demand response and energy storage collaborative optimization model considered in the step 1 includes two parts of an optimization objective function and a corresponding constraint condition, the optimization objective is that the cost of a micro-grid operator is the lowest, and the cost of the micro-grid operator includes the power supply cost F 1 And demand response cost F 2 The constraint conditions comprise power balance constraint in the micro-grid, output limit constraint of each micro source in the micro-grid, climbing constraint of the diesel generator, tie line constraint, demand response constraint and storage battery charge constraint.
Further, the power supply cost F 1 Including fuel cost C f (t), real-time transaction cost C of micro-grid operators in power transaction center r (t) and Battery running cost C op (t) is represented as follows:
wherein: t is a scheduling period fetch 24;
the demand response cost F 2 The expression is as follows:
wherein: x is the user cut power; y gives the micro-grid operator compensation to users participating in the interruptible load project; j is the total number of users participating in interruptible load projects in the micro-grid; lambda is the node marginal electricity price.
Further, the optimization objective F is to reduce the power supply cost F 1 And demand response cost F 2 As two sub-targets of the optimization model, for the multi-target optimization problem, a linear weighting method is adopted to process, and a weight coefficient w is introduced 1 And w 2 The expression is as follows:
F＝w 1 F 1 +w 2 F 2
wherein: w (w) 1 +w 2 ＝1。
Further, the constraint condition specifically includes:
the power balance constraint in the micro-grid is expressed as follows:
wherein: p (P) W (t)、P S (t)、Pr(t)、P BAT (t)、P L (t) respectively obtaining output power of a wind driven generator, output power of a photovoltaic system, transmission power of a connecting line, transmission power of a storage battery and initial load demand of a micro-grid at time t;
each micro-source output limit constraint in the micro-grid is expressed as follows:
P i,max ≤P i (t)≤P i,min
0≤P W (t)≤W t
0≤P S (t)≤S t
wherein: p (P) i,max 、P i,min The maximum output power and the minimum output power of the diesel generator i are respectively; w (W) t 、S t Predicting the maximum wind power output and the maximum photovoltaic output respectively for time t;
the diesel generator climbing constraint is expressed as follows:
-ΔP i,down ≤P i (t+1)-P i (t)≤ΔP i,up
wherein: ΔP i,up 、ΔP i,down The maximum upward and maximum downward slope climbing rates of the diesel generator are respectively;
the tie constraint is expressed as follows:
-Pr max ≤Pr(t)≤Pr max
wherein: pr (Pr) max Is the upper limit of the transmission power of the link;
the demand response constraints are expressed as follows:
y j (t)-(K 1,j x j (t) 2 +K 2,j x j (t)-K 2,j x j (t)θ j )≥0
j＝1,...,J
wherein: k (K) 1 And K 2 Are cost coefficients, which are constants greater than 0; θ is a user type, and classification is carried out according to willingness degree of users of different types to participate in interruptible load items, and the value range is that θ is more than or equal to 0 and less than or equal to 1; b is the budget of the micro-grid operators for implementing the demand response project every day; the CM is the maximum power which can be interrupted every day by users participating in the interruptible load project in advance;
the battery charge constraint is expressed as follows:
SOC min ≤SOC≤SOC max
wherein: SOC (State of Charge) max 、SOC min Respectively the upper limit and the lower limit of the charge quantity of the storage battery,
according to the maximum discharge current of the storage batteryI disch-max Obtaining the upper limit of the change of the charge quantity of the storage battery:
wherein: η (eta) disch For the purpose of the discharge efficiency of the storage battery,
according to the maximum charging current I of the storage battery ch-max Obtaining the lower limit of the change of the charge quantity of the storage battery:
wherein: η (eta) ch The charge efficiency of the storage battery is improved,
at the beginning and end of the scheduling period, the charge of the storage battery should be kept consistent:
SOC(1)＝SOC(T+1)。
further, the fuel cost C f (t) is represented as follows:
wherein: c (C) f,i (P i (t)) is the fuel cost of the ith diesel generator at time t; p (P) i (t) is the power of the ith diesel generator at time t; i is the number of diesel generators in the micro-grid;
the real-time transaction costs are expressed as follows:
C r (Pr(t))＝γ t ·Pr(t)
wherein: gamma ray t Real-time transaction electricity price at time t; pr (t) is the power traded by the microgrid in the spot market at time t, and if the power trades in the spot market, the power trades out in the spot market.
Further, the battery running cost C op (t) is represented as follows:
wherein: c (C) SB Initial investment costs for the battery device; r (t-1) is the life loss coefficient of the storage battery at time t-1; a is that op (t) is the throughput of the storage battery in the time t-1 to t; a is that total Is the effective throughput of the storage battery.
Further, the effective throughput A of the storage battery total The expression is as follows:
wherein: n is the total test number; q (Q) N Is the rated capacity of the storage battery; h is a i Is the depth of discharge at the ith test; n (N) i The number of cycles at the ith test.
Further, the throughput A of the storage battery in the time t-1 to t op (t) is represented as follows:
Ao p (t)＝|ΔSOC(t)|·Q N
wherein: and delta SOC (t) is the absolute value of the change of the charge quantity of the storage battery.
Further, the time t-1 battery life loss coefficient r (t-1) is expressed as follows:
r(t-1)＝m·SOC(t-1)+d
wherein: SOC (t-1) is the charge quantity of the storage battery at time t-1; m and d are fitting constants.
The invention has the beneficial effects that:
1. the power users autonomously select whether to participate in the demand response project according to own will, but most of current demand response models are not comprehensive in consideration of satisfaction degree of the user to participate in the demand response. And, the compensation given to the power consumer is a fixed compensation price, and the consumer cannot be sufficiently encouraged to participate in the demand response project. The interruptible load project has the advantages of high response speed and zero cost when not implemented, and is an important tool for peak shaving and emergency treatment of a power grid dispatching mechanism. Thus consider user participation in interruptible load items. Firstly, the cost of the user participating in the interruptible load project is established, and the income of the user participating in the interruptible load project is obtained, so that the satisfaction degree of the user participating in the demand response project is determined. The compensation given by the micro-grid operator is not a fixed compensation price, and the compensation is comprehensively judged according to the type of the user, the willingness degree of participating in the demand response project and the reduction of the electric quantity.
2. And considering the factors influencing the service life of the storage battery in the micro-grid economic dispatching problem, introducing a cost item related to the running cost of the storage battery into an optimization target, and achieving the effect of quantitative control.
Drawings
FIG. 1 is a graph showing the relationship between battery charge and life loss factor according to a preferred embodiment of the present invention;
FIG. 2 is a flow chart of a method of operating a micro-grid in accordance with a preferred embodiment of the present invention;
FIG. 3 is a graph showing the system forecast values and node marginal electricity prices over time for a preferred embodiment of the present invention;
FIG. 4 is a graph of microgrid load power versus time for a preferred embodiment of the present invention;
FIG. 5 is a graph of user interrupt power versus time for a preferred embodiment of the present invention;
FIG. 6 is a graph of user-obtained compensation versus time for a preferred embodiment of the present invention;
FIG. 7 is a graph showing battery charge over time according to a preferred embodiment of the present invention;
fig. 8 is a graph showing the change of the charge and discharge power of the battery with time according to a preferred embodiment of the present invention.
Detailed Description
The following description of the preferred embodiments of the present invention refers to the accompanying drawings, which make the technical contents thereof more clear and easier to understand. The present invention may be embodied in many different forms of embodiments and the scope of the present invention is not limited to only the embodiments described herein.
The invention relates to implementing demand response projects within a microgrid and optimizing an operating scheme of an energy storage device in consideration of operating costs. And for the demand response model, fully considering the response cost of the user, compensating according to the enthusiasm of the user response, and exciting the enthusiasm of the user to participate in the demand response. For the problem of larger service life loss of the storage battery device, the influence of the storage battery charge quantity with the largest influence on economic dispatch is considered by researching the factors influencing the service life of the storage battery, and the operation cost of the storage battery is built, so that the effect of quantitative control is achieved. The following is a specific description:
1. demand response and battery operating cost model building
1. Demand response model
1.1 user participation in interruptible load project costs
The cost of users participating in the interruptible load project is the loss brought to the users when the users cut down the electricity consumption. The cost of a user engaging in an interruptible load item can be expressed by the following equation:
c(x,θ)＝K 1 x 2 +K 2 x-K 2 xθ (1)
wherein: k (K) 1 And K 2 The cost coefficients are constants larger than 0, and the parameter estimation is carried out on the formula (1) by utilizing Taylor series secondary expansion according to the historical data of the accumulated user participating in the rate selection of the interruptible load project by using a metering economic analysis method; θ is a user type, and classification is carried out according to willingness degree of users of different types to participate in interruptible load items, and the value range is that θ is more than or equal to 0 and less than or equal to 1; x is the user cut-off power.
1.2 users engaging in interruptible load project revenue
The benefit of a user engaging in an interruptible load item can be expressed by the following equation:
U(θ,x,y)＝y-c(θ,x) (2)
wherein: y gives the microgrid operator compensation to users participating in interruptible load projects. In actual life, only if U is more than or equal to 0, the user can participate in the interruptible load project.
1.3 micro grid operators implement interruptible load project costs
When a power supply occurs, a pressure microgrid operator supplies power to a particular location, its power supply cost can be quite expensive. Thus, the cost of the microgrid operator implementing the interruptible load item requires consideration of the cost saved by not powering the customer, which can be represented by the following formula
B(θ,λ)＝y-λx (3)
Wherein: lambda is the marginal electricity price of the node and can be obtained by utilizing optimal power flow calculation.
1.4 multiple users participating in interruptible load item
Users participating in interruptible load projects report the maximum amount of electricity CM they can interrupt each day to the microgrid operators in advance. Because CM can embody user's willingness degree to participate in the interruptible load project, little electric wire netting operator confirms its theta according to user's CM, order users according to the principle that theta value reduces.
Each user will be willing to participate in the demand side response only when the gain of participation in the interruptible load item is greater than or equal to zero. Thus, there is a rationality constraint for user j:
wherein: j is the total number of users participating in interruptible load projects within the microgrid.
To fully motivate users to participate in the aggressiveness of interruptible load projects, the microgrid operators need to compensate for the aggressiveness of user responses, the more benefit they are willing to respond to should be gained. Thus, there are compatibility constraints:
2. cost of battery operation
2.1 Battery life
The service life of the storage battery can be represented by the cycle times of the storage battery provided by a manufacturer under different discharging depths, and the total number of the storage battery cycles is as follows:
wherein: n is depth of discharge h N The number of cycles of the battery; a, a 1 ～a 5 And providing the correlation coefficient for battery manufacturers.
However, since the battery cannot be guaranteed to have the same depth of discharge every time when actually operating, the effective throughput is generally used to predict the service life of the battery. The effective throughput is the sum of the charge and discharge amounts of the storage battery in the whole life cycle. The effective throughput of the battery can be estimated using the following equation:
wherein: a is that total N is the total test number, which is the effective throughput of the storage battery; q (Q) N Is the rated capacity of the storage battery; h is a i Is the depth of discharge at the ith test; n (N) i The number of cycles at the ith test.
2.2 running costs of the Battery
Throughput A of storage battery in t-1-t time op (t) can be expressed as:
A op (t)＝|ΔSOC(t)|·Q N (8)
wherein: the delta SOC (t) is the absolute value of the SOC variation of the charge quantity of the storage battery
The life loss coefficient K (t) of the storage battery in the time t-1 to t can be expressed as:
the most important influencing factor in the micro-grid economic dispatch is the charge quantity of the storage battery, so the influence of the charge quantity of the storage battery is mainly considered.
When the charge amount of the storage battery is 0.5, the effective throughput of the storage battery is reduced by 1.3 A.h for the actual service life of the storage battery per 1 A.h of charge amount of the storage battery; when the charge amount of the battery is 1, the effective throughput per throughput 1 A.h of the battery is reduced by 0.55 A.h for the actual life of the battery. From this, a relationship between the battery life loss coefficient r and the battery charge amount SOC can be obtained as shown in fig. 1.
Piecewise linear fitting of experimental data can yield the following formula:
r(t)＝m·SOC(t)+d (10)
wherein: m and d are fitting constants.
The running cost C of the storage battery can be obtained op (t)：
Wherein: c (C) SB Is the initial investment cost of the accumulator device.
2. Optimizing micro-grid operation method and solving
1. Objective function
With the lowest cost of the micro-grid operator as an optimization target, the cost F of the micro-grid operator is composed of two parts: cost of power supply F 1 And demand response cost F 2 。
Cost of power supply F 1 Including fuel cost C f (t) real-time transaction cost C of micro-grid operators in power transaction center r (t), battery running cost C op (t)。
Wherein: t is the scheduling period fetch 24.
Fuel cost, transaction cost in formula:
C r (P r (t))＝γ t ·P r (t)
wherein: c (C) f,i (P i (t)) is the fuel cost of the ith diesel generator at time t; i isThe number of diesel generators in the micro-grid; gamma ray t Real-time transaction electricity price at time t; pr (t) is the power traded by the microgrid in the spot market at time t, if the purchase is positive and the sale is negative.
C f,i (P i (t))＝a i P i (t) 2 +b i P i (t)
Wherein: a, a i And b i Is the fuel cost coefficient of the diesel generating set.
Demand response cost F 2 The following are provided:
wherein: j is the number of users in the micro-grid.
Because the implementation of the demand response project has a certain complexity, the micro-grid operators are required to communicate with the users, and the users are encouraged to actively participate in the demand response project. And the electricity consumption condition of the user needs to be researched, and the cost and the enthusiasm of the user for participating in the user are determined. The operation difficulty of the micro-grid operators is increased to a certain extent. Therefore, F is not 1 And F 2 Directly add up, but add F 1 And F 2 As two sub-objectives of the optimization model. For the multi-objective optimization problem, a linear weighting method is adopted to process, and a weight coefficient w is introduced 1 And w 2
F＝w 1 F 1 +w 2 F 2
Wherein: w (w) 1 +w 2 ＝1。
2. Constraint conditions
Power balance within microgrid:
wherein: p (P) W (t)、P S (t)、P L And (t) respectively obtaining the output power of the wind driven generator, the output power of the photovoltaic system and the initial load demand of the micro-grid at the time t.
Each micro-source output limit in the micro-grid:
P i,max ≤P i (t)≤P i,min
0≤P W (t)≤W t
0≤P S (t)≤S t
wherein: p (P) i,max 、P i,min The maximum output power and the minimum output power of the diesel generator i are respectively.
Climbing constraint:
-ΔP i,down ≤P i (t+1)-P i (t)≤ΔP i,up
wherein: ΔP i,up 、ΔP i,down The maximum upward and maximum downward ramp rates of the diesel generator are respectively.
Tie line constraint:
-Pr max ≤Pr(t)≤Pr max
wherein: pr (Pr) max Is the upper link transmission power limit.
Demand response constraints:
y j (t)-(K 1,j x j (t) 2 +K 2,j x j (t)-K 2,j x j (t)θ j )≥0
j＝1,...,J
wherein: b is the budget for the microgrid operator to implement demand response items every day.
In the running process, the requirement of certain upper and lower limits on the charge quantity of the storage battery is met:
SOC min ≤SOC≤SOC max
wherein: SOC (State of Charge) max 、SOC min The upper limit and the lower limit of the charge quantity of the storage battery are respectively.
According to the maximum discharge current I of the storage battery disch-max The upper limit of the change of the charge quantity of the storage battery can be obtained:
according to the maximum charging current I of the storage battery ch-max The lower limit of the change of the charge quantity of the storage battery can be obtained:
at the beginning and end of the scheduling period, the charge of the storage battery should be kept consistent:
SOC(1)＝SOC(T+1)
3. examples
As shown in fig. 2, the micro grid operation method according to the embodiment of the present invention includes the following steps:
s1, establishing a collaborative optimization model considering demand response and energy storage for a micro-grid;
and S2, calculating an optimal value of the collaborative optimization model to obtain an operation scheme of the micro-grid.
The algorithm selects a micro-grid with a smaller scale to verify the validity of the optimization model. The micro-grid consists of a diesel generator, a wind driven generator, a photovoltaic cell and a storage battery. The diesel generator parameters are shown in table 1. The user parameters within the microgrid are shown in table 2. The basic parameters of the battery are shown in table 3. Wind power photovoltaic prediction, micro-grid initial load and node marginal electricity price data are shown in fig. 3.
Table 1 diesel generator parameters
TABLE 2 user parameters
Table 3 battery parameters
Fig. 4 shows the variation of the microgrid load power before and after taking into account the demand response. It can be seen that when the peak period of electricity consumption (the photovoltaic system does not work) in the micro-grid is high, the marginal electricity price of the node is high, and the user self-cuts power. Therefore, the problem of power shortage in the micro-grid can be effectively relieved after the demand response is added.
Fig. 5 and 6 show the case of user interrupt power and user obtained compensation, respectively. And in the peak period of micro-grid electricity consumption, the user reduces the power by himself and relieves the electricity utilization tension. The total interruption power of the user 1 is 30kWh, and the obtained total compensation is 124.32$; the total interrupt electric quantity of the user 2 is 35kWh, and the obtained total compensation is 140$; the total interruption power of the user 3 is 40kWh and the total compensation obtained is 158.26$. According to the maximum daily power CM which can be interrupted and reported to the micro-grid operator by three users, the user 3 is the most willing to participate in the demand response project, the user 2 times, and the enthusiasm of the user 1 is the lowest. The example results embody compatibility constraint in the model, and the most compensation is given to users with higher response enthusiasm. The microgrid operators pay compensation in total for 422.57$ throughout the dispatch period.
As can be seen from an examination of fig. 7 and 8, after accounting for battery operating costs, the SOC of the battery is at a higher level for more time periods; the delta SOC of the storage battery is reduced, the change of the SOC of the storage battery is slowed down, and the charging and discharging processes are more gentle. Therefore, after the running cost of the storage battery is added into the model, the storage battery can be run in a state with higher SOC, the charge and discharge processes are slowed down, and the service life of the storage battery is prolonged.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention without requiring creative effort by one of ordinary skill in the art. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (6)
1. A method for optimizing micro-grid operation in cooperation with consideration of demand response and energy storage, the method comprising the steps of:
step 1, establishing a collaborative optimization model considering demand response and energy storage for a micro-grid;
step 2, calculating an optimal value of the collaborative optimization model to obtain an operation scheme of the micro-grid;
the demand response and energy storage collaborative optimization model considered in the step 1 comprises an optimization objective function and corresponding constraint conditions, the optimization objective is that the cost of a micro-grid operator is the lowest, and the cost of the micro-grid operator comprises the power supply cost F 1 And demand response cost F 2 The constraint conditions comprise power balance constraint in the micro-grid, output limit constraint of each micro source in the micro-grid, climbing constraint of the diesel generator, tie line constraint, demand response constraint and storage battery charge constraint;
the power supply cost F 1 Including fuel cost C f (t), real-time transaction cost C of micro-grid operators in power transaction center r (t) and Battery running cost C op (t) is represented as follows:
wherein: t is a scheduling period fetch 24;
the demand response cost F 2 The expression is as follows:
wherein: x is the user cut power; y gives the micro-grid operator compensation to users participating in the interruptible load project; j is the total number of users participating in interruptible load projects in the micro-grid; lambda is the marginal electricity price of the node;
the optimization objective F is to make the power supply cost F 1 And demand response cost F 2 As two sub-targets of the optimization model, for the multi-target optimization problem, a linear weighting method is adopted to process, and a weight coefficient w is introduced 1 And w 2 The expression is as follows:
F＝w 1 F 1 +w 2 F 2
wherein: w (w) 1 +w 2 ＝1；
The constraint condition specifically comprises:
the power balance constraint in the micro-grid is expressed as follows:
wherein: p (P) W (t)、P S (t)、Pr(t)、P BAT (t)、P L (t) respectively obtaining output power of a wind driven generator, output power of a photovoltaic system, transmission power of a connecting line, transmission power of a storage battery and initial load demand of a micro-grid at time t;
each micro-source output limit constraint in the micro-grid is expressed as follows:
P i,max ≤P i (t)≤P i,min
0≤P W (t)≤W t
0≤P S (t)≤S t
wherein: p (P) i,max 、P i,min Respectively diesel engineMaximum output power and minimum output power of the motor i; w (W) t 、S t Predicting the maximum wind power output and the maximum photovoltaic output respectively for time t;
the diesel generator climbing constraint is expressed as follows:
-ΔP i,down ≤P i (t+1)-P i (t)≤ΔP i,up
wherein: ΔP i,up 、ΔP i,down The maximum upward and maximum downward slope climbing rates of the diesel generator are respectively;
the tie constraint is expressed as follows:
-Pr max ≤Pr(t)≤Pr max
wherein: pr (Pr) max Is the upper limit of the transmission power of the link;
the demand response constraints are expressed as follows:
y j (t)-(K 1,j x j (t) 2 +K 2,j x j (t)-K 2,j x j (t)θ j )≥0
j＝1,...,J
wherein: k (K) 1 And K 2 Are cost coefficients, which are constants greater than 0; θ is a user type, and classification is carried out according to willingness degree of users of different types to participate in interruptible load items, and the value range is that θ is more than or equal to 0 and less than or equal to 1; b is the budget of the micro-grid operators for implementing the demand response project every day; the CM is the maximum power which can be interrupted every day by users participating in the interruptible load project in advance;
the battery charge constraint is expressed as follows:
SOC min ≤SOC≤SOC max
wherein: SOC (State of Charge) max 、SOC min Respectively the upper limit and the lower limit of the charge quantity of the storage battery,
according to the maximum discharge current I of the storage battery disch-max Obtaining the upper limit of the change of the charge quantity of the storage battery:
wherein: η (eta) disch For the purpose of the discharge efficiency of the storage battery,
according to the maximum charging current I of the storage battery ch-max Obtaining the lower limit of the change of the charge quantity of the storage battery:
wherein: η (eta) ch The charge efficiency of the storage battery is improved,
at the beginning and end of the scheduling period, the charge of the storage battery should be kept consistent:
SOC(1)＝SOC(T+1)。
2. the method for optimizing microgrid operation in coordination with consideration of demand response and energy storage according to claim 1, wherein said fuel cost C f (t) is represented as follows:
wherein: c (C) f,i (P i (t)) is the fuel cost of the ith diesel generator at time t; p (P) i (t) is the power of the ith diesel generator at time t; i is the number of diesel generators in the micro-grid;
the real-time transaction costs are expressed as follows:
C r (Pr(t))＝γ t ·Pr(t)
wherein: gamma ray t Real-time transaction electricity price at time t; pr (t) is the power traded by the microgrid in the spot market at time t, and if the power trades in the spot market, the power trades out in the spot market.
3. The method for optimizing microgrid operation in coordination with demand response and energy storage according to claim 1, wherein said battery operation cost C op (t) is represented as follows:
wherein: c (C) SB Initial investment costs for the battery device; r (t-1) is the life loss coefficient of the storage battery at time t-1; a is that op (t) is the throughput of the storage battery in the time t-1 to t; a is that total Is the effective throughput of the storage battery.
4. The method for optimizing microgrid operation in coordination with consideration of demand response and energy storage according to claim 3, wherein the effective throughput a of said storage battery total The expression is as follows:
wherein: n is the total test number; q (Q) N Is the rated capacity of the storage battery; h is a i Is the depth of discharge at the ith test; n (N) i The number of cycles at the ith test.
5. The method for optimizing micro-grid operation in cooperation with consideration of demand response and energy storage according to claim 3, wherein throughput A of the storage battery in t-1 to t time op (t) is represented as follows:
A op (t)＝|ΔSOC(t)|·Q N
wherein: and delta SOC (t) is the absolute value of the change of the charge quantity of the storage battery.
6. A method of operating a microgrid according to claim 3, wherein said time t-1 battery life loss factor r (t-1) is expressed as follows:
r(t-1)＝m·SOC(t-1)+d
wherein: SOC (t-1) is the charge quantity of the storage battery at time t-1; m and d are fitting constants.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105977991A (en) * | 2016-05-10 | 2016-09-28 | 浙江工业大学 | Independent micro grid optimization configuration method considering price-type demand response |
| CN107194516A (en) * | 2017-06-07 | 2017-09-22 | 华北电力大学 | Multi-energy complementary micro-grid distributed optimization dispatching method containing multiagent |
| CN107403256A (en) * | 2017-07-01 | 2017-11-28 | 华中科技大学 | One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response |
| CN108429288A (en) * | 2018-04-12 | 2018-08-21 | 荆州市荆力工程设计咨询有限责任公司 | A kind of off-network type micro-capacitance sensor energy storage Optimal Configuration Method considering demand response |
| CN108539739A (en) * | 2018-05-10 | 2018-09-14 | 安徽理工大学 | Micro-capacitance sensor runs energy management optimization method |
| CN109658012A (en) * | 2019-01-22 | 2019-04-19 | 武汉理工大学 | It is a kind of meter and Demand Side Response micro-capacitance sensor multiple target economic load dispatching method and device |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10949777B2 (en) * | 2017-06-07 | 2021-03-16 | Johnson Controls Technology Company | Building energy optimization system with economic load demand response (ELDR) optimization |
-
2019
- 2019-07-04 CN CN201910598580.1A patent/CN110380405B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105977991A (en) * | 2016-05-10 | 2016-09-28 | 浙江工业大学 | Independent micro grid optimization configuration method considering price-type demand response |
| CN107194516A (en) * | 2017-06-07 | 2017-09-22 | 华北电力大学 | Multi-energy complementary micro-grid distributed optimization dispatching method containing multiagent |
| CN107403256A (en) * | 2017-07-01 | 2017-11-28 | 华中科技大学 | One kind considers the probabilistic photovoltaic microgrid battery energy storage collocation method of demand response |
| CN108429288A (en) * | 2018-04-12 | 2018-08-21 | 荆州市荆力工程设计咨询有限责任公司 | A kind of off-network type micro-capacitance sensor energy storage Optimal Configuration Method considering demand response |
| CN108539739A (en) * | 2018-05-10 | 2018-09-14 | 安徽理工大学 | Micro-capacitance sensor runs energy management optimization method |
| CN109740846A (en) * | 2018-11-30 | 2019-05-10 | 国网江苏省电力有限公司电力科学研究院 | Intelligent residential district demand response dispatching method and system |
| CN109658012A (en) * | 2019-01-22 | 2019-04-19 | 武汉理工大学 | It is a kind of meter and Demand Side Response micro-capacitance sensor multiple target economic load dispatching method and device |
Non-Patent Citations (3)
| Title |
|---|
| "计及储能和用户需求响应的并网型微网优化调度模型";李盛伟;《电工电能新技术》;20180930;第37卷(第9期);正文第1-3节 * |
| 基于需求侧响应与成本模型的风电中的储能系统运行优化;田德等;《农业工程学报》;20180808(第15期);全文 * |
| 考虑需求响应不确定性的光伏微电网储能系统优化配置;李姚旺等;《电力系统保护与控制》;20181016(第20期);全文 * |
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