CN116070801A - Multi-site optimal operation strategy generation method for load aggregation platform - Google Patents
Multi-site optimal operation strategy generation method for load aggregation platform Download PDFInfo
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Abstract
The invention discloses a method for generating a multi-site optimal operation strategy of a load aggregation platform, which comprises the following steps of modeling scene abstraction, wherein each energy storage site under the load aggregation platform participates in demand response and business scene modeling of daily operation; defining decision variables; setting an optimization target, wherein the optimization target comprises setting power supply income of a power station, power grid electricity purchasing cost and demand response income, and a final objective function is the sum of comprehensive income of all sites to be maximized; adding constraint conditions; and constructing a multi-site demand response problem under the load aggregation platform as a constraint optimization model, and carrying out actual solution on the constraint optimization model to obtain an optimal operation strategy under the current condition. The beneficial effects are that: the running strategy and decision logic with optimal economy are formulated, the decision process is fast and effective and adapts to different demands, and the problem that demand response cannot be actually executed or serious deviation occurs due to abrupt changes of site load power and the like is effectively solved.
Description
Technical Field
The invention relates to the technical field of marketized operation strategies of power demand response, in particular to a method for generating a multi-site optimal operation strategy of a load aggregation platform.
Background
The Demand Response (DR) refers to a short-term behavior that when the price of the electric power market is obviously increased (reduced) or the safety reliability of the system is at risk, the electric power user temporarily changes the electricity consumption behavior according to price or incentive measures, and reduces (increases) the electricity consumption, so as to promote the balance of power supply and Demand, ensure the stable operation of the power grid and inhibit the increase of the electricity price. The load aggregation response mode is characterized in that a market main body of a load aggregator is introduced, the centralized control capability of a load aggregation platform to a plurality of energy storage power stations is utilized, the overall power grid load in a demand response period is regulated and controlled, the purpose of peak clipping and valley filling of power grid power is achieved, and meanwhile, the load aggregator participates in demand response settlement of the power grid as an independent market main body to obtain benefits.
At present, the demand response is inevitably extremely dependent on manual operation, special people are required to pay attention to and perform corresponding operation in real time, and most of the valley filling demand response occurs at night and early morning, so that the actual operation is inconvenient. Meanwhile, along with the expansion of the scale of a power station participating in demand response and the demand response frequency, the method relying on manual operation is no longer fit with the actual needs, and an intelligent and automatic method is needed to replace the method. And the participation demand response needs to prepare electricity or discharge in advance, which affects the benefits of the normal operation of each power station, and because the load condition of each power station is in a change at any time, the fixed charge-discharge curve set in advance may not realize a preset charge-discharge process due to the change of the site load, which greatly affects the benefit settlement of the participation demand response of the load aggregation platform.
Disclosure of Invention
This section is intended to outline some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section as well as in the description summary and in the title of the application, to avoid obscuring the purpose of this section, the description summary and the title of the invention, which should not be used to limit the scope of the invention.
The present invention has been made in view of the above-described problems occurring in the prior art.
Therefore, the technical problems solved by the invention are as follows: a method for generating a multi-site optimal operation strategy of a load aggregation platform is provided.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-site optimal operation strategy generation method of a load aggregation platform comprises the following steps of modeling scene abstraction, wherein each energy storage site under the load aggregation platform participates in demand response and business scene modeling of daily operation; defining decision variables; setting an optimization target, wherein the optimization target comprises setting power supply income of a power station, power grid electricity purchasing cost and demand response income, and a final objective function is the sum of comprehensive income of all sites to be maximized; adding constraint conditions, namely adding demand response power constraint, bus electric quantity balance constraint, energy storage system SOC constraint, energy storage system power constraint, grid power constraint and energy storage power variation constraint; according to the objective function and the added constraint conditions, constructing a multi-station demand response problem under a load aggregation platform as a constraint optimization model, and carrying out actual solution on the constraint optimization model to obtain an optimal operation strategy under the current condition.
Preferably, the decision variable definition includes, considering the running strategy of the load aggregation platform with N sites at T time instants in the future, defining decision variables as follows, X it : direct current discharge power of energy storage of charging station i at t moment, Y it : direct current charging power of energy storage at t time point of charging station i it : the charging station i purchases electric power from the power grid at the moment t; the variables being non-negativeA real number, wherein i=1, 2, ·, N, t=1, 2, T.
Preferably, the final objective function is the sum of the total power supply benefit of the power station, the purchase cost of the power grid and the comprehensive benefit of demand response benefit, and the formula is as follows
In the formula, J is a final objective function, J_load is the total power supply benefit of the power station, C_grid is the power grid electricity purchasing cost, and J_demand is the demand response benefit.
Preferably, the demand response power constraint is added as follows:
in the demand response period, the sum of the response power of all stations under the load aggregation platform is equal to the total demand response power, wherein demand_power t Responding to a target power value for demand issued in advance by the power grid, D t If t is the demand response period, D t The value is 1, otherwise 0.
Preferably, adding the bus electric quantity balance constraint includes, at any station in any period, meeting the bus electric quantity balance constraint, as follows:
wherein η_dsg i 、η_chg i Respectively discharging and charging efficiency of the station i energy storage system, S it 、L it Other power source power and load power of the station i in the period t.
Preferably, adding the energy storage system SOC constraint includes that the energy storage system SOC at any site in any period of time should meet between its upper and lower limits, as follows:
wherein cap_min i 、cap_max i The lower limit and the upper limit of the electric quantity of the energy storage system of the station i and the cap respectively i0 For the electric quantity of the initial state of the energy storage system of the station i, cap i D, the electric quantity of the energy storage system of the station i in the period t τ Is the duration of the tau period.
Preferably, adding energy storage system power constraints includes,
the charging and discharging power of the energy storage system of any station in any period is between the upper limit and the lower limit of the charging and discharging power, and the following formula is adopted:
p in the formula i The maximum charge-discharge power of the energy storage system of site i,
、/>
respectively representing the alternating current side power of the energy storage system.
Preferably, adding the power constraint of the power grid includes that the power purchased from the power grid by any station in any period should be not higher than the power limit of a transformer where the station is located, not lower than 0, and the following formula is shown in the specification:
wherein, 
the maximum power of the transformer where the station i is located.
Preferably, adding the energy storage power variation constraint includes, responding to a limitation of a power variation rate of an energy storage system at adjacent moments, and requiring a power variation value of an adjacent time period not to exceed alpha times of rated power of the energy storage system, wherein the value is 0-2, and the formula is as follows:
wherein, 
、/>
the power of the energy storage system of the station i in the t period and the t-1 period is +.>
Power for the energy storage system of site i during the initial period.
Preferably, the constraint optimization model is constructed as a mixed integer programming model of multiple variables, and the following formula is adopted:
wherein J is the final objective function, J_load is the total power supply income of the power station, C_grid is the power grid electricity purchasing cost, J_demand is the demand response income, and X it Direct current discharge power at time t for energy storage of charging station i, Y it Direct-current charging power at time t for energy storage of charging station i, Z it To purchase power from the grid at time t for charging station i,
、/>
the power of the energy storage system of the station i in the t period and the t-1 period is +.>
The power of the energy storage system of site i in the initial period, demand_power t Responding to a target power value for demand issued in advance by the power grid, D t To be an identifier of a demand response period, η_dsg i 、η_chg i Respectively discharging and charging efficiency of the station i energy storage system, S it 、L it Is the power of other power sources and the power for loads of station i in t period, and cap_min i 、cap_max i Lower limit and upper limit of electric quantity of energy storage system of station i respectively，cap i0 For the electric quantity of the initial state of the energy storage system of the station i, cap i D, the electric quantity of the energy storage system of the station i in the period t τ For the duration of tau period, P i Maximum charge/discharge power for the energy storage system of station i, +.>
、/>
Respectively represents the alternating current side power of the energy storage system, +.>
The maximum power of the transformer where the station i is located is set; and applying the constraint optimization model to a load aggregation platform, and obtaining the latest data to continuously solve the established constraint optimization model so as to obtain the optimal operation strategy under the current condition.
The invention has the beneficial effects that: the invention realizes abstract modeling of the response of the load aggregation platform participation requirement under the peak-valley electricity price scene, thereby being capable of formulating an operation strategy with optimal economical efficiency according to the model; the method provides centralized and unified decision and control for a plurality of sites, can be used for a small-scale load aggregation platform and a large-scale load aggregation platform, has definite decision logic, and is quick and effective in decision process; thirdly, the model constructed by the invention has good expansibility, additional specific constraints can be added for special sites or special time periods, for example, certain sites cannot actually participate in demand response due to faults, users cannot allow certain specific energy storage sites to participate in demand response at certain moments, and the like (the impermissible reasons are that the sites store energy and need to execute emergency power supply tasks, and the like), and the optimization targets can also be changed and switched at any time according to actual demands, so that different target strategies can be derived and different demands are met; the invention realizes the goal of dynamically and real-time distributing the total demand response power to each station, ensures that each station can be actually executed, and simultaneously, the strategy of dynamic planning and issuing can be quickly adjusted according to the change of the station load power, other power supply power and the state of charge (SOC) of the energy storage system, thereby effectively solving the problems that the demand response cannot be actually executed or serious deviation occurs due to the abrupt change of the station load power and the like.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is an overall flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a service scenario of a load aggregation platform according to the present invention;
FIG. 3 is a diagram showing how to compare the benefits of whether to participate in demand response according to the embodiment;
FIG. 4 is a graph of various parameters for a particular operation of site 41;
FIG. 5 is a graph of various parameters for a particular operation of site 143;
fig. 6 is a graph of various parameters for a particular operation of station 193.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The marketized operation of the power demand response can fully play the role of the market in optimizing the resource allocation and the power supply, and actively solve the contradiction of periodic power supply and demand, and guide the user to optimize the power load. Therefore, with the strong promotion of the development of the electric power market in China, the electric power demand response becomes more and more important, the form of the electric power demand response is further enriched into various forms (such as an electric power selling company business mode, an electric energy market mode, a capacity market mode, an auxiliary service market mode and the like) from the original specific user offer participation, particularly, a brand new load aggregation response mode with development potential is developed in recent years by relying on the mass production of energy storage power stations, the load aggregation response mode refers to the mode that the market main body of a load aggregator is introduced, the centralized control capability of a load aggregation platform on a plurality of energy storage power stations is utilized, the regulation and control of the whole power grid load in a demand response period is realized, the purpose of peak clipping and valley filling of the power grid is achieved, and meanwhile, the load aggregator obtains benefits by participating in the demand response settlement of the power grid as an independent market main body.
At present, the developed demand response is mainly based on an offer type test point, the marketization degree is still at a lower level, and along with the continuous improvement of the marketization level, the trend of the future demand response marketization operation is undoubted. However, at present, various relevant rules are tentatively unknown, and the degree of knowledge of the business of each market subject on demand response is low, so that a detailed technical scheme aiming at the scene is not provided.
For very few market subjects participating in the offer and further participating in the demand response, the current common practice is to control the charging or discharging of each energy storage device in advance after receiving the demand response offer, wherein the charging and discharging power, the duration and the like are manually set, and the charging and discharging power curve of each station is manually set after the demand response period is reached.
Although the aim of controlling a plurality of sites to participate in demand response is achieved to a certain extent, the method is inevitably extremely dependent on manual operation, special people are required to pay attention to the demand response in real time and perform corresponding operation, and most of grain filling demand response occurs at night and in the early morning, so that actual operation is inconvenient. Meanwhile, along with the expansion of the scale of a power station participating in demand response and the demand response frequency, the method relying on manual operation is no longer fit with the actual needs, and an intelligent and automatic method is needed to replace the method.
In addition, the above method has another problem that the economy of operation of the power station is that the participation demand response needs to be prepared or discharged in advance, which affects the benefit of normal operation of each power station, and because the load condition of each power station is in a change at all times, the charge-discharge curve set in advance may not realize a predetermined charge-discharge process due to the change of the load of the station, which greatly affects the benefit settlement of the participation demand response of the load aggregation platform.
Aiming at the problems, the embodiment provides a multi-site optimal operation strategy generation method of a load aggregation platform, which comprises 5 main steps of scene abstract modeling, decision variable definition, optimization target setting, constraint condition addition and actual model solving, wherein a technical scheme flow chart is shown in figure 1, and concretely comprises the following steps,
s1: modeling scene abstraction, namely modeling business scenes of participation of each energy storage site in demand response and daily operation under a load aggregation platform;
s2: defining decision variables;
s3: setting an optimization target, wherein the optimization target comprises setting power supply income of a power station, power grid electricity purchasing cost and demand response income, and a final objective function is the sum of comprehensive income of all sites to be maximized;
s4: adding constraint conditions, namely adding demand response power constraint, bus electric quantity balance constraint, energy storage system SOC constraint, energy storage system power constraint, grid power constraint and energy storage power variation constraint;
s5: according to the objective function and the added constraint conditions, constructing a multi-station demand response problem under a load aggregation platform as a constraint optimization model, and carrying out actual solution on the constraint optimization model to obtain an optimal operation strategy under the current condition.
Further, each step of the multi-site optimal operation strategy generation method of the load aggregation platform of the embodiment is described in detail.
S1: scene abstract modeling.
The embodiment models the business scenario that each energy storage site under the load aggregation platform participates in demand response and daily operation as an abstract model, as shown in the schematic diagram of fig. 2. The following will be briefly described in each of these sections:
(1) Station: the station is a market main body capable of independently participating in daily peak clipping and valley filling of the power grid, and meanwhile, the station also serves as a part of a load aggregation platform to execute terminal equipment. The sites are directly connected with a power grid, are provided with energy storage equipment and are simultaneously connected with various load equipment, and part of sites can be provided with other types of power generation equipment, such as photovoltaic, wind power, diesel units and the like; the site energy storage has independent control systems, can autonomously control a single site to perform daily peak clipping and valley filling operation, and can also accept unified scheduling participation demand response from a load aggregation platform.
(2) The power grid: the power grid is connected with each station through a station transformer, the power grid electricity price is the time-sharing peak valley electricity price, in part of time periods, according to the whole network load and the power supply condition, a certain time period or a plurality of time periods are generally determined in advance as a demand response time period, corresponding demand response power is issued, the load aggregation platform responds, and finally the load aggregation platform is regarded as an independent market main body to carry out demand response settlement.
(3) Load aggregation platform: the load aggregation platform comprises various energy storage power stations and a unified decision control center, wherein the various stations and the decision control center can be communicated in real time, the decision control center can control the various stations at any time, and when a demand response instruction from a power grid is received, the decision control center decides out the operation strategy of each station according to the load, energy storage and other power supply operation conditions of the various stations and issues the strategy to each station for execution, so that standby power before demand response and real-time demand response tasks are completed.
S2: and (5) defining decision variables.
Considering the operation strategy of the load aggregation platform with N sites at T moments in the future, the decision variables are defined as follows:
X it : the direct current discharge power of the energy storage of the charging station i at the time t;
Y it : the direct current charging power of the energy storage of the charging station i at the time t;
Z it : the charging station i purchases electric power from the electric network at the time t.
The variables are all non-negative real numbers, wherein, i=1, 2, the contents of the terms, N, (ii) the method comprises the steps of (1), N is a number of the N.
S3: setting an optimization target.
Under the load aggregation platform scene, the expected target mainly comprises two aspects, namely, completing the demand response target as far as possible to obtain the subsidy benefit of the demand response, and secondly, cutting peaks and filling valleys in the non-demand response period to obtain the price difference benefit of daily peak valley electricity price operation. For this purpose, the objective function calculation method of the model is as follows:
power station power supply income: each station receives revenue from the supply of power to the load connected to the station (e.g., by charging the electric vehicle via a charging stake). Wherein J_load is the total power supply income of the power station, and load_price it Monovalent load_power for power station i to power a load during period t it Power value d for supplying power to load in t period of power station i t For the duration of the t period, expressed as the following formula (1):
grid electricity purchasing cost: the cost incurred by purchasing electricity from the power grid at each moment in time for each power station. Wherein C_grid is the total cost of the power station purchasing electricity from the power grid, and grid_price it For unit price of electricity purchase of power station i from power grid in t period it D, for the power value of electricity purchased from the power grid in t period of the power station i t For the duration of the t period, expressed as the following formula (2):
demand response benefit: the load aggregation platform formed by the power stations is used as a whole to participate in settlement income obtained by demand response. Wherein J_demand is the total income of the load aggregation platform to participate in demand response, and demand_price t Subsidized unit price for demand response in period t, power_demand t Demand response power for period t, d t Grid_power is the duration of the t period it For the power purchased by station i from the grid at time t,grid_power_base t the response baseline power at time t is a specific value that is published by the grid before the demand response occurs. Expressed as the following formulas (3) and (4), the formula (3) is a calculation method of actual response power for the load aggregator to participate in the demand response, and the settlement amount of the load aggregator to participate in the demand response is finally determined, and the settlement amount=settlement unit price is the demand response power is the demand response time, as follows:
the final objective function, the sum of the comprehensive benefits of all sites, is maximized as the following formula (5):
in the formula, J is a final objective function, J_load is the total power supply benefit of the power station, C_grid is the power grid electricity purchasing cost, and J_demand is the demand response benefit.
S4: constraint conditions are added.
Demand response power constraints: during the demand response period, the sum of the response powers of all stations under the load aggregation platform is equal to the total demand response power. demand_power t Responding to a target power value for demand issued in advance by the power grid, D t For the identifier of whether or not it is a demand response period, if t period is a demand response period, the value is 1, otherwise it is 0, the following formula (6):
and the bus electric quantity balance constraint is that the bus electric quantity balance constraint is satisfied at any station in any period, namely, the energy storage discharging power, the power purchased from a power grid, the other power supply power, the energy storage charging power and the load electric power. Wherein η_dsg i 、η_chg i Respectively discharging and charging efficiency of the station i energy storage system, S it 、L it The power of other power sources and the power for loads of the station i in the period t can be obtained through prediction by a certain method, and the power is regarded as a known value in the model. The following formula (7):
energy storage system SOC constraints: the SOC (State of charge) of the energy storage system at any site during any period should be between its upper and lower limits. Wherein cap_min i 、cap_max i The lower limit and the upper limit of the electric quantity of the energy storage system of the station i and the cap respectively i0 For the electric quantity of the initial state of the energy storage system of the station i, cap i D, the electric quantity of the energy storage system of the station i in the period t τ Is the duration of the tau period. The following formula (8):
and the energy storage system power is constrained, namely the charging and discharging power of the energy storage system of any station in any period is between the upper limit and the lower limit of the charging and discharging power. P (P) i The maximum charge-discharge power of the energy storage system of site i,
、/>
respectively representing the alternating current side power of the energy storage system. The following formulas (9) and (10):
the power constraint of the power grid is that the power purchased from the power grid by any station in any period of time is not higher than the power purchased by the stationThe power limit of the transformer is not lower than 0, and the power cannot be reversely transmitted to the power grid (also called reverse power protection). Wherein, 
the maximum power of the transformer where the station i is located. The following formula (11):
energy storage power variation constraint: in order to prevent obvious control instruction execution delay caused by severe power fluctuation of a single energy storage device in a short time and to protect an energy storage system, the limitation of responding to the power fluctuation rate of the energy storage system at adjacent moments is generally required, the power fluctuation value of the adjacent time periods is generally required to be not more than alpha times of the rated power of the energy storage system, and the power fluctuation value is a coefficient manually given in advance and is 0-2. The following formulas (12), (13) and (14):
wherein, 
、/>
the power of the energy storage system of the station i in the t period and the t-1 period is +.>
Power for the energy storage system of site i during the initial period.
S5: and (5) actually solving the model.
According to the objective function and the constraint condition, the multi-site demand response problem under the load aggregation platform can be modeled as the following constraint optimization model, and the following formula (15) is adopted:
the constraint optimization model is a multi-variable mixed integer programming model. For the mixed integer programming problem, there are already mature solving theory and solving tools at present, so the solution can be realized by referring to the prior art, and therefore, the embodiment is not described in detail. The above-mentioned demand response problem (i.e., formula 15) can obtain its optimal solution in a short time, and the solution of the problem (formula 15) can be obtained conveniently and rapidly by using a dedicated solution software such as SCIP or other open source operation optimization solver.
In practical applications, since the load power, the stored energy power, the grid power, etc. of each site are constantly changing, it is necessary to solve the problem again from new power data at intervals (typically about 1 second) (equation 15). In practical application, the model is a rolling optimization model, namely the model is built by continuously solving the latest data, so that the optimal strategy under the current condition is obtained.
Obtaining decision variables by solving the model:
X it 、Y it 、Z it (i=1, 2, the contents of the terms, N, t=1, 2, ··, T) optimal solution, recorded as X in order ∗ it 、Y ∗ it 、Z ∗ it Finally take Y ∗ i1 −X ∗ i1 Direct current net charging power (positive charge-negative discharge) at the current moment for energy storage of charging station i, Z ∗ i1 The power purchased from the power grid at the current moment is used for the charging station i. And repeating the decision execution process at the next moment to achieve the effects of rolling solving and real-time execution.
Compared with the prior art, the method for generating the multi-site optimal operation strategy of the load aggregation platform has the following advantages:
1. according to the model, the comprehensive economic benefit is optimal under the superposition of daily peak load shedding and valley filling and participation demand response of each site, and various constraint conditions are fully considered, so that abstract modeling of participation demand response of a load aggregation platform in a peak-valley electricity price scene is realized, and an operation strategy with optimal economy can be formulated according to the model.
2. The method provided by the invention provides centralized and unified decision and control for a plurality of sites, can be used for a small-scale load aggregation platform and a large-scale load aggregation platform, has definite decision logic, and is quick and effective in decision process.
3. The model disclosed by the invention has good expansibility, additional specific constraints can be added for special sites or special time periods, and the optimization target can be changed at any time according to actual requirements, so that different target strategies can be derived and different requirements can be met.
4. Compared with a traditional power distribution mode similar to a proportional mode, various constraints are considered, so that each station can be ensured to be actually executed, meanwhile, a dynamic planning and issuing strategy can be quickly adjusted according to the change of station load power, other power supply power and the state of charge (SOC) of an energy storage system, and the problem that the demand response cannot be actually executed or serious deviation occurs due to the mutation of station load power is effectively solved. Meanwhile, as the load condition of each power station is in the change at any time, the problem caused by the objective condition cannot be solved in the prior art, but the method can be used for self-adaptive adjustment according to the objective condition of the changed load.
The embodiment of the invention now applies the policy generation method in practice to further verify the effect obtained by the method of the invention based on the method for generating the multi-site optimal operation policy of the load aggregation platform.
Specifically, 3 energy storage sites capable of participating in demand response under a certain load aggregation platform take 30 hours as an optimization period. Some basic information of each site is shown in the following table 1, other parameters (such as electricity price, load power and the like) related to time periods and model solving results refer to fig. 3-6, wherein fig. 3 compares the overall operation conditions when participating in and not participating in demand response, fig. 4-6 respectively show site operation conditions under two conditions, the upper part of fig. 3 shows the situation of participating in demand response, 4286.78 is the income amount obtained by the system participating in demand response, 3418.53 is the cost of purchasing electricity from a power grid as a whole during the participation demand response, and-868.25 is the estimated comprehensive cost (power grid electricity purchasing cost-demand response income) of the system participating in demand response; the lower part of fig. 3 shows a comparison of not participating in demand response, wherein 0 indicates that the demand response benefit amount is 0 because not participating in demand response, 3142.18 is the cost of purchasing power from the grid as a whole during the time of not participating in demand response, and the estimated total cost of system operation (grid purchase cost-demand response benefit) is still 3142.18.
The 634.13 on the upper left side of fig. 4 shows a site with an ID of 41, the purchase cost of participating in demand response is 634.13 yuan, and 559.44 on the upper right side shows a site with an ID of 41, the purchase cost of not participating in demand response (i.e., daily operation) is 559.44 yuan.
The significance of 559.16, 496.5 in fig. 5 and 2225.24, 2086.24 in fig. 6 is similar to the numerical significance of the response position in fig. 3, and represents the electricity purchase cost in the two cases of participation and non-participation in demand response of each site.
TABLE 1 basic information of each site under a load aggregation platform
In fig. 3, the hatched portions (9-11 period, 14-15 period, 20-22 period) are demand response periods, and the remaining portions are non-demand response periods. According to the model, the strategy of purchasing electricity from the power grid in the whole time period can be adjusted according to whether the demand response is participated or not, and the comprehensive cost of purchasing electricity from the power grid can be reduced by adjusting the energy storage power to participate in the demand response. In this embodiment, the comprehensive cost of not participating in demand response is about 3142.18 yuan, because the corresponding demand exists in the total 5 time periods of 9-11, 14-15 and 20-22, and the corresponding demand response subsidy price is higher, when the sites 41, 143 and 193 participate in demand response, the action sequence of energy storage of each site in the corresponding state is obtained by considering the benefits obtained by the demand response and combining the model, see the comparison of the power curve and the capacity curve in fig. 4-6, so that after the energy storage charging and discharging sequence is adjusted to participate in demand, the electricity purchasing cost of the power grid is increased by 276.35 yuan to 3418.53 yuan, but the demand response benefit 4286.78 yuan can be additionally obtained, thereby reducing the comprehensive cost to minus 868.25 yuan, and the economic benefit is obvious.
Compared with daily peak clipping and valley filling which does not participate in demand response, the operation strategy of each station participating in demand response can be adaptively adjusted from the station operation conditions of fig. 4-6, and on the premise of ensuring that demand response power is achieved, the cost of purchasing electricity from a power grid of each station is minimized as an objective function in a model, so that the obtained energy storage charging and discharging action sequence can increase the cost of purchasing electricity from the power grid of each station as little as possible.
Therefore, the final optimization objective in the embodiment is ensured by the linear programming solver, so the embodiment does not relate to the solver, and aims to provide an optimization operation strategy generation method for participating in demand response at multiple sites under a load aggregation platform, and the linear programming solver is not described in detail.
It should be appreciated that embodiments of the invention may be implemented or realized by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer readable storage medium configured with a computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, in accordance with the methods and drawings described in the specific embodiments. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes (or variations and/or combinations thereof) described herein may be performed under control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications), by hardware, or combinations thereof, collectively executing on one or more processors. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable computing platform, including, but not limited to, a personal computer, mini-computer, mainframe, workstation, network or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and so forth. Aspects of the invention may be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optical read and/or write storage medium, RAM, ROM, etc., such that it is readable by a programmable computer, which when read by a computer, is operable to configure and operate the computer to perform the processes described herein. Further, the machine readable code, or portions thereof, may be transmitted over a wired or wireless network. When such media includes instructions or programs that, in conjunction with a microprocessor or other data processor, implement the steps described above, the invention described herein includes these and other different types of non-transitory computer-readable storage media. The invention also includes the computer itself when programmed according to the methods and techniques of the present invention. The computer program can be applied to the input data to perform the functions described herein, thereby converting the input data to generate output data that is stored to the non-volatile memory. The output information may also be applied to one or more output devices such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including specific visual depictions of physical and tangible objects produced on a display.
As used in this application, the terms "component," "module," "system," and the like are intended to refer to a computer-related entity, either hardware, firmware, a combination of hardware and software, or software in execution. For example, the components may be, but are not limited to: a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of example, both an application running on a computing device and the computing device can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. Furthermore, these components can execute from various computer readable media having various data structures thereon. The components may communicate by way of local and/or remote processes such as in accordance with a signal having one or more data packets (e.g., data from one component interacting with another component in a local system, distributed system, and/or across a network such as the internet with other systems by way of the signal).
It should be further understood that the present invention has been described in terms of embodiments, and that the embodiments are only capable of implementing the clear and complete description of the technical solutions set forth in the claims of the present invention, i.e., the explanation of the claims, so that when judging whether the technical solutions described in the present invention are sufficiently disclosed, the gist of the solutions defined in the claims should be fully considered, and other technical problems unrelated to the technical solutions set forth in the embodiments are necessarily present in the description, and the corresponding technical features and technical solutions are not necessarily indicated by the gist of the embodiments, so that the technical solutions can be implemented by fully combining the prior art with the common general knowledge with the implicit disclosure, and thus are not necessary to be described in detail.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A method for generating a multi-site optimal operation strategy of a load aggregation platform is characterized by comprising the following steps of: comprises the steps of,
modeling scene abstraction, namely modeling business scenes of participation of each energy storage site in demand response and daily operation under a load aggregation platform;
defining decision variables;
setting an optimization target, wherein the optimization target comprises setting power supply income of a power station, power grid electricity purchasing cost and demand response income, and a final objective function is the sum of comprehensive income of all sites to be maximized;
adding constraint conditions, namely adding demand response power constraint, bus electric quantity balance constraint, energy storage system SOC constraint, energy storage system power constraint, grid power constraint and energy storage power variation constraint;
according to the objective function and the added constraint conditions, constructing a multi-station demand response problem under a load aggregation platform as a constraint optimization model, and carrying out actual solution on the constraint optimization model to obtain an optimal operation strategy under the current condition.
2. The load aggregation platform multi-site optimal operation strategy generation method according to claim 1, wherein the method comprises the following steps: the decision variable definition includes the definition of the decision variable,
considering the operational policy of a load aggregation platform with N sites at T moments in the future, the decision variables are defined as follows,
X it : the direct current discharge power of the energy storage of the charging station i at the time t;
Y it : the direct current charging power of the energy storage of the charging station i at the time t;
Z it : charging station iPurchasing electric power from a power grid at the time t;
the variables are all non-negative real numbers, wherein, i=1, 2, the contents of the terms, N, (ii) the method comprises the steps of (1), N is a number of the N.
3. The load aggregation platform multi-site optimal operation strategy generation method according to claim 1, wherein the method comprises the following steps: the final objective function is the sum of the total power supply benefit of the power station, the power grid purchase cost and the comprehensive benefit of demand response benefit, and the formula is as follows:
in the formula, J is a final objective function, J_load is the total power supply benefit of the power station, C_grid is the power grid electricity purchasing cost, and J_demand is the demand response benefit.
4. The load aggregation platform multi-site optimal operation strategy generation method according to claim 2, wherein the method comprises the following steps: adding the demand response power constraint is as follows:
in the demand response period, the sum of the response power of all stations under the load aggregation platform is equal to the total demand response power, wherein demand_power t Responding to a target power value for demand issued in advance by the power grid, D t If t is the demand response period, D t The value is 1, otherwise 0.
5. The load aggregation platform multi-site optimal operation strategy generation method according to claim 2, wherein the method comprises the following steps: adding the bus bar charge balancing constraint includes,
at any station in any period, the bus electric quantity balance constraint is satisfied, and the following formula is adopted:
wherein η_dsg i 、η_chg i Respectively discharging and charging efficiency of the station i energy storage system, S it 、L it Other power source power and load power of the station i in the period t.
6. The load aggregation platform multi-site optimal operation strategy generation method according to claim 2, wherein the method comprises the following steps: adding the energy storage system SOC constraint includes,
the energy storage system SOC at any site during any period should meet between its upper and lower limits as follows:
wherein cap_min i 、cap_max i The lower limit and the upper limit of the electric quantity of the energy storage system of the station i and the cap respectively i0 For the electric quantity of the initial state of the energy storage system of the station i, cap i D, the electric quantity of the energy storage system of the station i in the period t τ Is the duration of the tau period.
7. The load aggregation platform multi-site optimal operation strategy generation method according to claim 1, wherein the method comprises the following steps: adding the energy storage system power constraint includes,
the charging and discharging power of the energy storage system of any station in any period is between the upper limit and the lower limit of the charging and discharging power, and the following formula is adopted:
；
p in the formula i The maximum charge-discharge power of the energy storage system of site i,
、/>
respectively represent the alternating current side power of the energy storage system。
8. The load aggregation platform multi-site optimal operation strategy generation method according to claim 2, wherein the method comprises the following steps: adding the grid power constraint includes,
the power purchased from the power grid by any station in any period should not be higher than the power limit of the transformer where the station is located, not lower than 0, and the following formula is adopted:
；
wherein, 
the maximum power of the transformer where the station i is located.
9. The load aggregation platform multi-site optimal operation strategy generation method according to claim 7, wherein: adding the stored energy power variation constraint includes,
and responding to the limitation of the power fluctuation rate of the energy storage system at adjacent moments, wherein the power fluctuation value of the adjacent time period is required to be not more than alpha times of the rated power of the energy storage system, and the value is 0-2, and the following formula is adopted:
wherein, 
、/>
the power of the energy storage system of the station i in the t period and the t-1 period is +.>
Power at initial period for energy storage system of site i, P i Maximum charge-discharge power of energy storage system of station i, X it DC discharge at time t for energy storage of charging station iElectric power, X i,t-1 Direct current discharge power at t-1 for energy storage of charging station i, Y it Direct-current charging power at time t for energy storage of charging station i, Y i,t-1 The direct current charging power at time t-1 is stored for charging station i.
10. The load aggregation platform multi-site optimal operation strategy generation method according to claim 9, wherein: and constructing the constraint optimization model into a multi-variable mixed integer programming model, wherein the formula is as follows:
wherein J is the final objective function, J_load is the total power supply income of the power station, C_grid is the power grid electricity purchasing cost, J_demand is the demand response income, and X it Direct current discharge power at time t for energy storage of charging station i, Y it Direct-current charging power at time t for energy storage of charging station i, Z it To purchase power from the grid at time t for charging station i,
、/>
the power of the energy storage system of the station i in the t period and the t-1 period is +.>
The power of the energy storage system of site i in the initial period, demand_power t Responding to a target power value for demand issued in advance by the power grid, D t To be an identifier of a demand response period, η_dsg i 、η_chg i Respectively discharging and charging efficiency of the station i energy storage system, S it 、L it Is the power of other power sources and the power for loads of station i in t period, and cap_min i 、cap_max i The lower limit and the upper limit of the electric quantity of the energy storage system of the station i and the cap respectively i0 For the electric quantity of the initial state of the energy storage system of the station i, cap i D, the electric quantity of the energy storage system of the station i in the period t τ For the duration of tau period, P i Maximum charge/discharge power for the energy storage system of station i, +.>
、/>
Respectively represents the alternating current side power of the energy storage system, +.>
The maximum power of the transformer where the station i is located is set;
and applying the constraint optimization model to a load aggregation platform, and obtaining the latest data to continuously solve the established constraint optimization model so as to obtain the optimal operation strategy under the current condition.
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