CN107545325B - Multi-microgrid interconnection operation optimization method based on game theory - Google Patents
Multi-microgrid interconnection operation optimization method based on game theory Download PDFInfo
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Abstract
A multi-microgrid interconnection operation optimization method based on a game theory comprises the following steps: and analyzing the load demand response characteristics of each microgrid, and constructing a microgrid group model. And then initializing the system, establishing a day-ahead game model, and calculating the net load of the ith microgrid in the j time period and the total system net load according to day-ahead prediction data. And each micro-grid solves the respective optimal strategy according to the initial state, performs information interaction in the micro-grid group, shares the respective obtained optimal strategy information, and updates the state information. And the system control center judges whether the Nash equilibrium is achieved or not, if the system meets the equilibrium condition, the game is ended, and a final optimization strategy set is output. If not, according to the updated state information, the optimization is carried out again before. The invention can give consideration to the stability and the economy of the system and simultaneously realize the individual benefit maximization and the group optimal operation of the micro-grid.
Description
Technical Field
The invention belongs to the technical field of multi-microgrid interconnection operation optimization, and particularly relates to a multi-microgrid interconnection operation optimization method based on a game theory.
Background
In the face of increasingly severe energy and environmental issues, the development of renewable energy has become an important task in the energy field of today's society. But the renewable energy has the characteristics of strong randomness, intermittence and the like, the utilization maximization of the renewable energy is difficult to realize, and the effective utilization of the renewable energy can be thoroughly realized only by realizing the interconnection and sharing of energy among a plurality of micro-grids and realizing the efficient transmission of the power generation of the renewable energy.
The Micro-Grid (Micro-Grid) is also translated into a Micro-Grid, which refers to a small power generation and distribution system composed of a distributed power supply, an energy storage device, an energy conversion device, a load, a monitoring and protecting device and the like. The appearance of the micro-grid realizes flexible and efficient application of the distributed power supply, so that various renewable energy sources with different distributions can make up for each other, and the resource utilization efficiency is optimized. The micro-grid can participate in auxiliary regulation of the power grid through price signals or excitation signals, and also can participate in electric power market regulation in a multi-party game mode, so that reasonable optimization and configuration of resources are facilitated. However, due to the strong uncertainty of renewable energy, the influence of renewable energy on the electric energy quality of a power grid cannot be fundamentally changed by the integration of a microgrid and distributed generation, and the maximization of the utilization of the renewable energy is difficult to realize. In order to solve the problems, the traditional centralized optimization method has an ever-increasing computational dimension, is no longer suitable for a microgrid interconnection system with a non-cooperative characteristic, and how to perform more efficient optimization scheduling on interconnection operation of a plurality of microgrids is a key of the problem.
Disclosure of Invention
In order to overcome the defects of low response speed, low reliability, low efficiency and the like caused by multiple uncertainties in the existing micro-grid group research, the invention comprehensively considers the interaction between the power market and the demand side, provides a multi-micro-grid interconnection operation optimization method based on the game theory, establishes a non-cooperative game model based on real-time electricity price and by combining the interconnection operation mode of the multi-micro-grid on the basis of considering the load demand response, and solves the Nash equilibrium through iteration, thereby obtaining the optimal scheduling strategy. The invention aims to give consideration to system stability and economy and simultaneously realize individual benefit maximization and group optimal operation of the microgrid.
In order to achieve the purpose, the technical scheme of the invention is as follows:
a multi-microgrid interconnection operation optimization method based on a game theory comprises the following steps:
s1: comprehensively analyzing the load demand response characteristics of each microgrid, and constructing a microgrid group model including a multi-microgrid interconnection system control center, a microgrid load model, an energy storage system model, distributed renewable energy sources and a net load model;
s2: initializing a system, acquiring parameters required by optimization such as original data and the like, including price functions and parameter information related to a micro-grid group, and predicting the photovoltaic output and load requirements of a fan to obtain predicted data;
s3: establishing a game model, and setting the initial iteration number k to be 1;
s4: calculating the net load of the ith microgrid in the jth time period according to the prediction data
Adding all the individual net loads of the micro-grid to obtain the sum which is used as the total net load of the system
S5: each game participant carries out independent optimization decision according to the initial state, the game participants are micro-grid individuals, and under respective optimization targets, respective cost minimization optimal strategies are solved;
s6: performing information interaction in the microgrid group, sharing the respectively obtained optimal strategy information, and updating state information;
s7: the system control center judges whether Nash equilibrium is achieved, namely, no micro-grid can act independently under the condition to increase income; if the condition is not met, k is k +1, and the optimization is carried out again by going to S5 according to the updated state information;
s8: and if the system meets the balance condition, ending the game and outputting the final optimization strategy set.
In the invention, the micro-grid group environment is composed of a plurality of micro-grid individuals, the power supply side of each micro-grid individual is composed of distributed power supplies such as wind energy, light energy and stored energy, and the demand side is composed of different types of similar charges. Interconnection lines connected with each other exist among the micro-grids, so that when the micro-grid individuals cannot completely consume the distributed energy, redundant electric energy is shared.
Further, in step S1, the established microgrid group model includes:
s1-1, the multi-microgrid interconnection system control center: the method comprises the steps of receiving wind and light energy output and load information of each micro-grid, sending the wind and light energy output and load information to other micro-grids, realizing information exchange and sharing, and carrying out optimization management on each micro-grid through the shared information to realize the decentralized autonomous function of the micro-grid; the control center receives the final optimization scheme, analyzes the energy supply and demand states of each microgrid and sends a signal to control the on-off state of the interconnection lines between the microgrids;
s1-2. microgrid load model: dividing the load into an uncontrollable load and a transferable load, wherein the uncontrollable load does not participate in demand response; the transferable load is taken as an active load to participate in demand response, and meets the consumption demand of the distributed power supply, and the model is as follows:
uncontrollable load: the uncontrollable load at a given ith microgrid is defined as follows:
in the formula: UL (UL)i,jRepresenting the total uncontrollable load of the ith microgrid at the jth time period; n represents the total number of the micro-grids; t is the time length;
2) transferable Load (TLs): the i-th microgrid transferable load is defined as follows: :
in the formula: TLi,jRepresenting the total transferable load of the ith microgrid at the jth time period;
the transferable load means that the consumer can select the time of use and decide the amount of electricity used according to the current electricity price, so that the transferable load satisfies the following characteristics:
in the formula: [ TL ]i min,TLi max]Is a power range of a transferable load；[ti start,ti end]Is the time range in which the load can be transferred; qi minIs the demand of transferable load, i.e. the minimum power consumption of the device to complete the task; the constraints are as follows:
according to the formula (4), the power of the TL equipment is in an allowable range during the operation period; when the operation is finished, the power consumption of the equipment is required to meet the requirement of minimum power consumption to indicate that the equipment finishes the work; the TL equipment can participate in load dispatching to respond to the power grid requirement by transferring the power utilization time period and simultaneously ensuring to complete the work requirement;
s1-3, energy storage system model: the energy storage system comprises two different operation modes of charging and discharging, so the optimization of the energy storage system firstly plans the operation mode of the energy storage system according to the current electricity price, the capacity at each moment is related to the charging or discharging state at the previous moment, and in the charging state, the storage battery working mode of the ith microgrid is as follows:
in the formula (I), the compound is shown in the specification,
storing the residual energy for the period of j;
and
respectively the charging power and the discharging power of the energy storage j time period; etacThe energy storage utilization efficiency is improved; qiThe total energy of the energy storage battery is calculated; Δ t is a scheduling time interval;
storing the SOC of the energy storage battery for the period j, namely the residual capacity;
in addition, the energy storage system also needs to satisfy the charge-discharge power limit constraint and the unit residual capacity constraint:
in the formula (I), the compound is shown in the specification,
and
respectively setting the upper limit of energy storage charging and discharging power;
and
the residual capacity of the energy storage unit is an upper limit and a lower limit;
and
representing the charging and discharging states of the stored energy, and taking 0 or 1, wherein the two states can not coexist;
s1-4. the distributed power model, comprising the following:
a photovoltaic system: the photovoltaic active output power of the ith microgrid is as follows under the specified condition:
a wind turbine generator set: based on a wind speed example in one day, the fan output of the ith microgrid is obtained as follows:
the total output of the distributed power supply is as follows:
Pres,i＝Ppv,i+Pw,i (11)
s1-5, a microgrid net load model: according to the model, the net load of the ith microgrid in the jth period is as follows:
in the formula (I), the compound is shown in the specification,
the payload of the microgrid i at time j. The part of the load needs to trade with a power distribution network or other micro-grids to balance the supply and demand power of the load;
and
charging and discharging power of the energy storage of the micro-grid i at the moment j;
considering the microgrid group as a whole system, calculating the sum of the individual net loads of all the microgrid as the net load of the system:
still further, in step S2, the optimizing the required original parameters includes:
s2-1. microgrid interconnection system price function: the overall cost of the microgrid is a piecewise function of:
in the formula:
represents the total cost of electricity for the microgrid cluster; a. b, c are parameters of a cost polynomial, wherein a>0 and b, c is more than or equal to 0; gamma is the reverse power price of photovoltaic output; Δ t is a scheduling time interval;
since the power cost should be a continuous function, setting c to 0, the cost function is approximated as a quadratic function:
the real-time electricity price function is therefore approximated as:
further, in step S3, the game model establishment includes the following steps:
s3-1, in the microgrid group, a non-cooperative game theory is adopted to study how different microgrid individuals configure the microgrid under given information so as to maximize income. In the non-cooperative game model, all micro-grids are taken as game participants, the power utilization planning of the micro-grid transferable load and the charge and discharge arrangement of stored energy are taken as game decisions, the income maximization is taken as an objective function of micro-grid individuals, respective objectives are realized under given constraints, Nash equilibrium is finally achieved, the overall optimal decision is realized, and the formed non-cooperative game model is expressed as follows:
the participants: u ═ U1,U2,…,UN}
The strategy set is as follows:
an objective function: e ═ E1,E2,…,EN}
In the formula of UiRepresents the ith microgrid; siPower utilization strategy representing micro-grid i, where TLiFor transferable load power planning, PB,i＝Pch,i+Pdch,iRepresenting the energy storage charging and discharging arrangement,
representing an interaction strategy between the ith microgrid and the mth microgrid connected with the ith microgrid; eiThe yield for the ith microgrid is an objective function for its optimization, Ei＝-CiIn which C isiThe ith microgrid cost.
If there is a Nash equilibrium point in the above model
In the current scene, all participants select the optimal strategy, and no participant changes the strategy of the participant in a unilateral way to break the balance, so that each microgrid in the strategy group can achieve the highest gain under the balance;
s3-2, the theorem proving that Nash equilibrium exists is as follows:
theorem: in the game, if the decision space is in Euclidean space of a non-empty convex subset of the decision space and the pay function is continuous and pseudo-concave, a pure strategy Nash equilibrium exists;
the strategy space is a non-empty tight convex set in the Euclidean space, so that only a revenue function E needs to be explainedi,jPi continuous concave simulation, namely the existence of Nash equilibrium of the model can be proved according to theorem;
in the process of the individual microgrid optimization solution,
is constant, so the objective function is transformed into three parts:
wherein
And
is a linear function of the argument, whose second derivative is 0, is a non-concave function,
about
Second derivative of (a' + K)TL) Positive, the function is a convex function; therefore, the cost function Ci,jBeing a convex function, then a gain function Ei,jFor the concave function, all concave functions are continuously simulated concave, and the model has "nash equilibrium" according to the theorem.
Preferably, each microgrid aims at solving an optimization strategy by minimizing cost. When the system satisfies the Nash equilibrium condition, i.e. | Pnl(k)–Pnl(k-1) | < 0.001, and when the variation of the total net load is smaller than the set threshold value 0.001 before and after iteration, the iteration is considered to be converged, and the system reaches the optimal state. If not, the step S5 is skipped to carry out optimization again.
The invention has the beneficial effects that:
1. the load demand analysis of each microgrid can be comprehensively analyzed, and the consumption capacity of the microgrid on new energy can be effectively improved through the transmission and sharing of energy among the microgrids.
2. The load characteristic of the micro-grid system is effectively improved, and meanwhile, the economy of the micro-grid is improved.
3. The optimization method can still keep a relatively stable change trend along with the improvement of the uncertainty of the system, and has a remarkable advantage in weakening the influence of the uncertainty on the operation of multiple microgrids.
Drawings
Fig. 1 is a microgrid photovoltaic output curve.
FIG. 2 is a microgrid fan output curve.
Fig. 3 is a daily load curve for three micro grids.
Fig. 4 is a multi-microgrid net load total amount change curve before and after optimization.
Fig. 5 is a flowchart of a multi-microgrid interconnection operation optimization method based on game theory.
Detailed Description
The invention will be further explained with reference to the drawings.
Referring to fig. 1 to 5, a multi-microgrid interconnection operation optimization method based on a game theory includes the following steps:
s1: load demand response characteristics of each microgrid are comprehensively analyzed, and a microgrid group model including a multi-microgrid interconnection system control center, a microgrid load model, an energy storage system model, distributed renewable energy sources and a net load model is constructed.
S2: the system initializes and obtains parameters needed by optimization, such as original data and the like, including price functions and parameter information of related micro-grid groups, and predicts the photovoltaic output and load requirements of the fan to obtain predicted data.
S3: and establishing a game model, and setting the initial iteration number k to be 1.
S4: calculating the net load of the ith microgrid in the jth time period according to the prediction data
Adding all the individual net loads of the micro-grid to obtain the sum which is used as the total net load of the system
S5: and each game participant (microgrid individual) makes an independent optimization decision according to the initial state. And under respective optimization objectives, solving respective cost minimization optimal strategies.
S6: and exchanging information in the microgrid group, sharing the respectively obtained optimal strategy information, and updating the state information.
S7: the system control center judges whether Nash equilibrium is achieved, namely, no micro-grid can act independently to increase income under the condition. If the condition is not satisfied, k is k +1, and the process proceeds to S5 to perform optimization again based on the updated state information.
S8: and if the system meets the balance condition, ending the game and outputting the final optimization strategy set.
The micro-grid group environment comprises a plurality of micro-grid individuals, the power supply side of each micro-grid individual comprises distributed power supplies such as wind energy, light energy and stored energy, and the demand side of each micro-grid individual comprises different types of similar charges. Interconnection lines connected with each other exist among the micro-grids, so that when the micro-grid individuals cannot completely consume the distributed energy, redundant electric energy is shared.
Further, in step S1, the established microgrid group model includes:
s1-1, the multi-microgrid interconnection system control center: the multi-microgrid system designed by the invention combines the concept of decentralization to a certain extent, and the control center of the multi-microgrid system does not have the control function of the traditional core module and only has the most basic data receiving and sending functions. The control center receives the wind and light energy output and load information of each micro-grid and sends the information to other micro-grids to realize information exchange and sharing. And each micro-grid carries out optimization management on the micro-grid through the shared information, so that the distributed autonomous function of the micro-grid is realized. In addition, the control center receives the final optimization scheme, analyzes the energy supply and demand states of the micro-grids, and sends signals to control the on-off state of the interconnection lines between the micro-grids.
S1-2. microgrid load model: all consumers participating in demand response should have a certain percentage of flexible load, which means space to adjust the size and time of the load. The invention divides the load into an uncontrollable load and a transferable load according to different characteristic loads. The power on and off of uncontrollable loads such as lighting equipment can influence the normal life of residents and do not participate in demand response; the electricity utilization time of the transferable load is flexible, and the requirement can be met only by finishing the work requirement within the specified time; therefore, the transferable load can be used as an active load to participate in demand response, and the distributed power supply consumption demand is met. The concrete model is as follows:
uncontrollable load: the load is connected with the intelligent socket, so that the load distribution can be predicted, but the control cannot be carried out, such as illumination, television, telephone, refrigerator and the like, whether the load is normally operated or not is related to whether the basic requirements of a user can be normally met or not, and fixed power supply must be ensured; the uncontrollable load at a given ith microgrid is defined as follows:
in the formula: UL (UL)i,jRepresenting the total uncontrollable load of the ith microgrid at the jth time period; n represents the total number of the micro-grids; t is the time length.
Transferable Load (TLs): the working time interval of the load has a certain range, the working time can be scheduled in the range, and the transferable load of the ith micro-grid is defined as follows:
in the formula: TLi,jRepresenting the total transferable load of the ith microgrid at the jth time period;
the transferable load means that the consumer can select the time of use and decide the amount of electricity used according to the current electricity price, so that the transferable load satisfies the following characteristics:
in the formula: [ TL ]i min,TLi max]Is the power range of the transferable load; [ t ] ofi start,ti end]Is the time range in which the load can be transferred; qi minIs the demand of transferable load, i.e. the minimum power consumption of the device to complete the task; the specific constraints are as follows:
according to the formula (4), the power of the TL equipment is in an allowable range during the operation period; when the operation is finished, the power consumption of the equipment is required to meet the requirement of minimum power consumption to indicate that the equipment finishes the work; the TL device can participate in load dispatching to respond to the power grid requirement by transferring the power utilization period and simultaneously ensuring to complete the work requirement.
S1-3, energy storage system model: the energy storage system mainly comprises two different operation modes of charging and discharging, so the optimization of the energy storage system firstly plans the operation mode of the energy storage system according to the current electricity price, and the capacity at each moment is related to the charging or discharging state at the previous moment. In the charging state, the storage battery working mode of the ith microgrid is as follows:
in the formula (I), the compound is shown in the specification,
storing the residual energy for the period of j;
and
respectively the charging power and the discharging power of the energy storage j time period; etacThe energy storage utilization efficiency is improved; qiThe total energy of the energy storage battery is calculated; Δ t is a scheduling time interval;
the SOC, i.e., the remaining capacity, of the energy storage battery is stored for period j.
In addition, the energy storage system also needs to satisfy the charge-discharge power limit constraint and the unit residual capacity constraint:
in the formula (I), the compound is shown in the specification,
and
respectively setting the upper limit of energy storage charging and discharging power;
and
the residual capacity of the energy storage unit is an upper limit and a lower limit;
and
the energy storage charging and discharging states are represented, and 0 or 1 is adopted, so that the two states cannot coexist.
S1-4. distributed power model:
a photovoltaic system: the output power of the photovoltaic cell, which varies in light intensity and ambient temperature, has only one Maximum Power Point (MPP) for each specific case. Photovoltaic systems typically employ Maximum Power Point Tracking (MPPT) to operate photovoltaic modules in MPP states in diverse environments. The photovoltaic active output power of the ith microgrid is as follows under the specified condition:
a wind turbine generator set: wind power generation is to convert kinetic energy of wind into electric energy, and wind energy and wind speed form a cubic function relationship. Based on a certain daily wind speed example, the invention obtains the fan output of the ith microgrid as follows:
the total output of the distributed power supply is as follows:
Pres,i＝Ppv,i+Pw,i (11)
s1-5, a microgrid net load model: according to the model, the net load of the ith microgrid in the jth period is as follows:
in the formula (I), the compound is shown in the specification,
the payload of the microgrid i at time j. The part of the load needs to trade with a power distribution network or other micro-grids to balance the supply and demand power of the load;
and
and charging and discharging the energy storage power of the micro-grid i at the moment j.
Considering the microgrid group as a whole system, calculating the sum of the individual net loads of all the microgrid as the net load of the system:
still further, in step S2, the optimizing the required original parameters includes:
s2-1. microgrid interconnection system price function: in order to prevent the situation that under the action of time-of-use electricity prices, users are excessively excited to transfer loads to cause peak load transfer to generate bounce peaks in non-peak periods, and the optimization purpose cannot be achieved, the method adopts a real-time electricity price model, determines the current electricity prices in real time according to supply and demand relations in a power system at each moment and various constraint conditions, and enables the distribution of the user loads to be kept as uniform as possible. Therefore, the user can more reasonably arrange the self power consumption time period, the power consumption cost is reduced, and the peak clipping and valley filling of the power grid are realized. Generally, the power cost and the system load are in a quadratic function relationship, and since the model used in the research comprises the output of renewable energy sources, when the renewable energy sources are excessive, the profit is the negative cost of the microgrid. Therefore, the overall cost of the microgrid is a piecewise function of:
in the formula:
represents the total cost of electricity for the microgrid cluster; a. b, c are parameters of a cost polynomial, wherein a>0 and b, c is more than or equal to 0; gamma is the reverse power price of photovoltaic output; Δ t is the scheduling time interval.
Since the power cost should be a continuous function, setting c to 0, the cost function can be approximated as the following quadratic function for simplicity of calculation:
the real-time electricity price function can thus be approximated as:
further, in step S3, the game model establishment includes the following steps:
s3-1, in the microgrid cluster, the benefits of the microgrids are correlated, and since the total cost of the microgrid cluster system is a function of the system payload per period, the cost of each microgrid is closely related to the power usage arrangements of other microgrids. The method adopts a non-cooperative game theory to research how different microgrid individuals configure the microgrid under given information so as to maximize income. In the non-cooperative game model, all micro-grids are taken as game participants, the power utilization planning of the micro-grid transferable load and the charge and discharge arrangement of stored energy are taken as game decisions, the income maximization is taken as an objective function of micro-grid individuals, respective objectives are realized under given constraints, Nash equilibrium is finally achieved, the overall optimal decision is realized, and the formed non-cooperative game model is expressed as follows:
the participants: u ═ U1,U2,…,UN}
The strategy set is as follows:
an objective function: e ═ E1,E2,…,EN}
In the formula of UiRepresents the ith microgrid; siPower utilization strategy representing micro-grid i, where TLiFor transferable load power planning, PB,i＝Pch,i+Pdch,iRepresenting the energy storage charging and discharging arrangement,
representing an interaction strategy between the ith microgrid and the mth microgrid connected with the ith microgrid; eiThe yield for the ith microgrid is an objective function for its optimization, Ei＝-CiIn which C isiThe ith microgrid cost.
If there is a Nash equilibrium point in the above model
In other words, in the current scene, all participants select the optimal strategy, and no participant changes the own strategy unilaterally to break the balance, so that each microgrid in the strategy group can achieve the highest yield under the balance.
S3-2, the theorem proving that Nash equilibrium exists is as follows:
theorem: in a game, if the payfunctions are continuous and pseudo-concave in euclidean space of a non-empty convex subset of the decision space, there is a pure policy nash equilibrium.
Since the strategy space of the model in the invention is a non-empty tight convex set in the Euclidean space, only the income function E needs to be explainedi,jPi successive pseudo-concave, the existence of nash equilibrium in the model can be proved according to theorem.
In the model, because the interaction power needs to reasonably distribute the redundant electric energy of each microgrid based on the optimized result, in the optimization solving process of the individual microgrid,
being constant, the objective function can be transformed into three parts:
wherein
And
is a linear function of the argument, whose second derivative is 0, is a non-concave function,
about
Second derivative of (a' + K)TL) Positive, the function is a convex function. Therefore, the cost function Ci,jBeing a convex function, then a gain function Ei,jIs a concave function. All the concave functions are continuously simulated concave, and according to the theorem, the model has 'Nash equilibrium'.
In steps S5-S8, the operation cost of the microgrid is as shown in formulas (14) to (16), and the optimization strategy is solved for each microgrid with the cost minimized. When the system satisfies the Nash equilibrium condition, i.e. | Pnl(k)–Pnl(k-1) | < 0.001, and when the variation of the total net load is smaller than the set threshold value 0.001 before and after iteration, the iteration is considered to be converged, and the system reaches the optimal state. If not, the step S5 is skipped to carry out optimization again.
In order to enable the technical personnel in the field to better understand the invention, the applicant uses actual data of a certain micro-grid group to carry out energy trading according to the real-time power price of a power distribution network during interconnection so as to verify the effectiveness of the provided control strategy. The micro-grid group is located in Hangzhou China, and comprises a residential area, a commercial area and an office area which are named as a micro-grid 1, a micro-grid 2 and a micro-grid 3 respectively. The specific power supply and energy storage capacity configuration is shown in table 1, and the parameters are shown in table 2.
| Microgrid designation | Photovoltaic capacity/kW | Fan capacity/kW | Energy storage capacity/ |
| Microgrid | |||
| 1 | 400 | 400 | 600 |
| Microgrid 2 | 300 | 400 | 600 |
| Microgrid 3 | 800 | 800 | 1000 |
TABLE 1
TABLE 2
The invention selects the actual wind speed and the sunlight intensity of a certain place as the optimized initial data, and the graphs 1 and 2 are respectively the output curves of the three micro-grid photovoltaic fans. It is assumed that there are tens or hundreds of transferable load appliances or devices in the microgrid. Such appliances or devices may include plug-in hybrid vehicles, washing machines, dryers, dishwashers, disinfectants, etc., and the daily load curves for these three micro-grids are shown in fig. 3.
Fig. 4 is a change curve of the total amount of payload of the multi-microgrid after interconnection optimization. As can be seen from the figure, through the optimization scheme provided by the invention, the net load of each micro-grid tends to be stable, and the stability of system operation is greatly improved; and the interconnected operation mode distributes the surplus or insufficient electric energy of each micro-grid through the connecting lines, reduces the back-feeding amount of the renewable energy to the power distribution network, is favorable for improving the utilization efficiency of the energy, reduces the waste, and has positive promoting effect on strengthening the stable operation of the whole power system.
Along with the increase of the iteration times, the optimization result is closer to the convergence value, and when the iteration times reach 11 times, the system reaches the convergence value, namely the Nash equilibrium of the game is reached. Under the operation mode, each microgrid can obtain the highest economic benefit and the optimal energy consumption effect.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples described in this specification can be combined and combined by one skilled in the art.
Although embodiments of the present invention have been shown and described, it is to be understood that the embodiments described herein are merely illustrative of the forms of the invention, and that the scope of the invention should not be construed as limited to the specific forms set forth herein, but rather as encompassing equivalent technical means which may occur to those skilled in the art upon consideration of the present disclosure.
Claims (4)
1. A multi-microgrid interconnection operation optimization method based on a game theory is characterized by comprising the following steps:
s1: comprehensively analyzing the load demand response characteristics of each microgrid, and constructing a microgrid group model including a multi-microgrid interconnection system control center, a microgrid load model, an energy storage system model, distributed renewable energy sources and a net load model;
s2: initializing a system, acquiring parameters required by optimization such as original data and the like, including price functions and parameter information related to a micro-grid group, and predicting the photovoltaic output and load requirements of a fan to obtain predicted data;
s3: establishing a game model, and setting the initial iteration number k to be 1;
s4: calculating the net load of the ith microgrid in the jth time period according to the prediction data
Adding all the individual net loads of the micro-grid to obtain the sum which is used as the total net load of the system
S5: each game participant carries out independent optimization decision according to the initial state, the game participants are micro-grid individuals, and under respective optimization targets, respective cost minimization optimal strategies are solved;
s6: performing information interaction in the microgrid group, sharing the respectively obtained optimal strategy information, and updating state information;
s7: the system control center judges whether Nash equilibrium is achieved, namely, no micro-grid can act independently under the condition to increase income; if the condition is not met, k is k +1, and the optimization is carried out again by going to S5 according to the updated state information;
s8: if the system meets the balance condition, ending the game and outputting a final optimization strategy set;
in step S1, the established microgrid group model includes:
s1-1, the multi-microgrid interconnection system control center: the method comprises the steps of receiving wind and light energy output and load information of each micro-grid, sending the wind and light energy output and load information to other micro-grids, realizing information exchange and sharing, and carrying out optimization management on each micro-grid through the shared information to realize the decentralized autonomous function of the micro-grid; the control center receives the final optimization scheme, analyzes the energy supply and demand states of each microgrid and sends a signal to control the on-off state of the interconnection lines between the microgrids;
s1-2. microgrid load model: dividing the load into an uncontrollable load and a transferable load, wherein the uncontrollable load does not participate in demand response; the transferable load can be used as an active load to participate in demand response and meet the consumption demand of the distributed power supply, and the model is as follows:
uncontrollable load: the uncontrollable load at a given ith microgrid is defined as follows:
ULi@[ULi,1,ULi,2,L,ULi,T],i∈[1,2,L,N] (1)
in the formula: UL (UL)i,jRepresenting the total uncontrollable load of the ith microgrid at the jth time period; n represents the total number of the micro-grids; t is the time length;
2) the transferable load: the i-th microgrid transferable load is defined as follows:
TLi@[TLi,1,TLi,2,L,TLi,T],i∈[1,2,L,N] (2)
in the formula: TLi,jRepresenting the total transferable load of the ith microgrid at the jth time period;
the transferable load means that the consumer can select the time of use and decide the amount of electricity used according to the current electricity price, so that the transferable load satisfies the following characteristics:
in the formula: [ TL ]i min,TLi max]Is the power range of the transferable load; [ t ] ofi start,ti end]Is the time range in which the load can be transferred; qi minIs the demand of transferable load, i.e. the minimum power consumption of the device to complete the task; the constraints are as follows:
according to the formula (4), the power of the TL equipment is in an allowable range during the operation period; when the operation is finished, the power consumption of the equipment is required to meet the requirement of minimum power consumption to indicate that the equipment finishes the work; the TL equipment can participate in load dispatching to respond to the power grid requirement by transferring the power utilization time period and simultaneously ensuring to complete the work requirement;
s1-3, energy storage system model: the energy storage system comprises two different operation modes of charging and discharging, so the optimization of the energy storage system firstly plans the operation mode of the energy storage system according to the current electricity price, the capacity at each moment is related to the charging or discharging state at the previous moment, and in the charging state, the storage battery working mode of the ith microgrid is as follows:
in the formula (I), the compound is shown in the specification,
storing the residual energy for the period of j;
and
respectively storing energy charging and discharging power of the micro-grid i at the moment j; etacThe energy storage utilization efficiency is improved; qiThe total energy of the energy storage battery is calculated; Δ t is a scheduling time interval;
storing the SOC of the energy storage battery for the period j, namely the residual capacity;
in addition, the energy storage system also needs to satisfy the charge-discharge power limit constraint and the unit residual capacity constraint:
in the formula (I), the compound is shown in the specification,
and
respectively setting the upper limit of energy storage charging and discharging power;
and
the residual capacity of the energy storage unit is an upper limit and a lower limit;
and
representing the charging and discharging states of the stored energy, and taking 0 or 1, wherein the two states can not coexist;
s1-4. the distributed power model, comprising the following:
a photovoltaic system: the photovoltaic active output power of the ith microgrid is as follows under the specified condition:
a wind turbine generator set: based on a wind speed example in one day, the fan output of the ith microgrid is obtained as follows:
the total output of the distributed power supply is as follows:
Pres,i＝Ppv,i+Pw,i (11)
s1-5, a microgrid net load model, according to the model, the net load of the ith microgrid in the jth period is as follows:
in the formula (I), the compound is shown in the specification,
the net load of the micro-grid i at the moment j needs to be traded with a power distribution network or other micro-gridsBalancing the self supply and demand power;
and
charging and discharging power of the energy storage of the micro-grid i at the moment j;
considering the microgrid group as a whole system, calculating the sum of the individual net loads of all the microgrid as the net load of the system:
2. the method for optimizing operations of interconnected multiple piconets based on game theory as claimed in claim 1, wherein in the step S2, optimizing the required raw parameters includes:
s2-1. microgrid interconnection system price function: the overall cost of the microgrid is a piecewise function of:
in the formula:
represents the total cost of electricity for the microgrid cluster; a. b, c are parameters of a cost polynomial, wherein a>0 and b, c is more than or equal to 0; gamma is the reverse power price of photovoltaic output; Δ t is a scheduling time interval;
since the power cost should be a continuous function, setting c to 0, the cost function is approximated as a quadratic function:
the real-time electricity price function is therefore approximated as:
3. the method for optimizing operations of interconnected multiple piconets based on game theory as claimed in claim 1, wherein in the step S3, the game model establishment includes the following steps:
s3-1, in a microgrid group, a non-cooperative game theory is adopted, how different microgrid individuals configure a microgrid under given information is researched to maximize profits, in a non-cooperative game model, all the microgrids serve as game participants, the power utilization planning and energy storage charging and discharging arrangement of the microgrid capable of transferring loads serve as game decisions, the profits serve as target functions of the microgrid individuals, respective targets are achieved under given constraints, Nash balance is finally achieved, the overall optimal decision is achieved, and the formed non-cooperative game model represents the following steps:
the participants: u ═ U1,U2,L,UN}
The strategy set is as follows:
an objective function: e ═ E1,E2,L,EN}
In the formula of UiRepresents the ith microgrid; siPower utilization strategy representing micro-grid i, where TLiFor transferable load power planning, PB,i＝Pch,i+Pdch,iRepresenting the energy storage charging and discharging arrangement,
representing an interaction strategy between the ith microgrid and the mth microgrid connected with the ith microgrid; eiThe yield for the ith microgrid is an objective function for its optimization, Ei＝-CiIn which C isiThe cost of the ith microgrid;
if there is a Nash equilibrium point in the above model
In the current scene, all participants select the optimal strategy, and no participant changes the own strategy unilaterally to break the balance, so that each microgrid under the strategy set can achieve the highest yield under the balance;
s3-2, the theorem proving that Nash equilibrium exists is as follows:
theorem: in the game, if the decision space is in Euclidean space of a non-empty convex subset of the decision space and the pay function is continuous and pseudo-concave, a pure strategy Nash equilibrium exists;
the strategy space is a non-empty tight convex set in the Euclidean space, so that only a revenue function E needs to be explainedi,jPi continuous concave simulation, namely the existence of Nash equilibrium of the model can be proved according to theorem;
in the process of the individual microgrid optimization solution,
is constant, so the objective function is transformed into three parts:
wherein
And
is a linear function of the argument, whose second derivative is 0, is a non-concave function,
about
Second derivative of (a' + K)TL) Positive, the function is a convex function; therefore, the cost function Ci,jBeing a convex function, then a gain function Ei,jFor the concave function, all concave functions are continuously simulated concave, and the model has "nash equilibrium" according to the theorem.
4. The method for optimizing operations of interconnected multiple micro grids based on game theory as claimed in claim 1, wherein in step S7, each micro grid solves the optimization strategy with the cost minimization as the target, and when the system meets nash equilibrium conditions, i.e. | P is obtainednl(k)–Pnl(k-1) | is less than 0.001, and when the variation of the total net load is less than the set threshold value of 0.001 before and after iteration, the iteration is considered to be converged, and the system reaches the optimal state; if not, the step S5 is skipped to carry out optimization again.
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