CN116613743A - Multi-type energy storage and load side flexible resource joint planning method and device - Google Patents

Multi-type energy storage and load side flexible resource joint planning method and device Download PDF

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Publication number
CN116613743A
CN116613743A CN202310605193.2A CN202310605193A CN116613743A CN 116613743 A CN116613743 A CN 116613743A CN 202310605193 A CN202310605193 A CN 202310605193A CN 116613743 A CN116613743 A CN 116613743A
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power
flexible resource
energy storage
day
kth
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张鹏
冯建洲
蔡绍荣
胡泽春
魏明奎
陶宇轩
沈力
文一宇
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Tsinghua University
Southwest Branch of State Grid Corp
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Southwest Branch of State Grid Corp
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
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Abstract

The invention provides a multi-type energy storage and load side flexible resource joint planning method and device, and belongs to the field of power system planning methods. Wherein the method comprises the following steps: acquiring an aggregate feasible domain of various flexible resources by establishing a feasible domain model of a single flexible resource under different categories; based on the aggregate feasible domain of the various flexible resources, establishing a multi-type energy storage and load side flexible resource joint planning model; converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day; and solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day to obtain a planning result of the energy storage and flexible resource. The invention can simultaneously consider the complementary characteristics of the flexible resources of the power distribution side and the multi-type energy storage resources in the power system planning, and reduce the investment and the running cost of the system.

Description

Multi-type energy storage and load side flexible resource joint planning method and device
Technical Field
The invention belongs to the field of power system planning, and particularly relates to a multi-type energy storage and load side flexible resource joint planning method and device.
Background
With the large-scale renewable energy source connected to the power grid, new energy power generation machines and power generation amounts of wind power, photovoltaic and the like are improved year by year, and the main power supply is formed. New energy is gradually changed into a main power supply for providing electric quantity support from providing electric quantity supplement. Because of the inherent intermittent and fluctuating output characteristics of wind power and photovoltaic, the renewable energy sources are influenced by places and natural fluctuation, so that many challenges are brought to safe and stable operation of a power grid in the aspects of power generation, delivery, consumption and the like, the influence on the output characteristics of a power source side is increased, and complex and variable unstable situations are increasingly presented.
Energy storage resources will play an important role in novel power systems based on new energy sources. Along with the progress of technology and the reduction of cost, the novel energy storage equipment represented by the lithium battery comprehensively develops and acts together on a power supply side, a power grid side and a user side, provides trusted capacity, stabilizes randomness and intermittence of new energy, and improves performances such as phase modulation and frequency modulation of the system. The pumped storage power station will be further developed in China, and the variable-speed pumping and storage unit is hopeful to be popularized and applied. On the other hand, on the power distribution side, a plurality of adjustable loads exist, the power consumption of the power distribution side can be adjusted under the condition of price signals or system adjustment instructions, and the power balance of the system can be promoted economically and effectively. In order to make up for the deficiency of the power supply side regulation capability, the demand side flexible resource is regulated and controlled, and the investment and operation cost of the whole system can be reduced due to the lower investment cost relative to the investment of the power generation side and energy storage side flexible resource. Therefore, the potential of flexible resource regulation is excavated and configured, and the method has important significance for guaranteeing the access, the consumption and the delivery of new energy sources of the power grid in large scale in the future.
However, current research into flexible resource planning has the following disadvantages:
1) The research objects of the novel flexible resource coordination control are various, but in the current research, a model for uniformly describing flexible characteristics of multiple types of controllable resources is not yet available.
2) The joint planning method of multi-type energy storage and flexible resources is not considered, so that the resources of the electricity distribution side are fully utilized.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a multi-type energy storage and load side flexible resource joint planning method and device. The invention considers the complementary characteristics of the flexible resources of the power distribution side and the multi-type energy storage resources in the planning, can effectively utilize the flexible resources of the power distribution side with low investment cost, improves the operation efficiency of the power system and reduces the total investment and operation cost of the system.
An embodiment of a first aspect of the present invention provides a method for jointly planning multiple types of energy storage and load side flexible resources, including:
acquiring an aggregate feasible domain of various flexible resources by establishing a feasible domain model of a single flexible resource under different categories;
based on the aggregate feasible domain of the various flexible resources, establishing a multi-type energy storage and load side flexible resource joint planning model;
Converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day to obtain a planning result of the energy storage and flexible resource.
In a specific embodiment of the present invention, the feasible region model of the single flexible resource is composed of an energy boundary model and a power boundary model of the flexible resource; respectively superposing the power boundary and the energy boundary of each flexible resource under the same category to obtain an aggregate feasible domain of the flexible resource;
the aggregate feasible domain expression of any type of flexible resource j is as follows:
wherein ,the power lower bound, the power upper bound, the energy lower bound and the energy upper bound of the ith individual of the flexible resource j in the kth power grid partition in the mth month, the d day and the t time period respectively; n (N) j The number of individual samples for flexible resource j.
In a specific embodiment of the present invention, the individual sample of the flexible resource j is obtained by sampling the group of the flexible resource.
In a specific embodiment of the present invention, the multi-type energy storage and load side flexible resource joint planning model is composed of an objective function and constraint conditions;
wherein the objective function is minimized as a sum of total investment cost, maintenance cost and operation cost in one year, and the expression is as follows:
minf＝C inv +C om +C op (2)
wherein ,
wherein ,Cinv To total investment cost, C om For maintenance cost, C op Is the running cost; a is that y,j ,A y,z Investment discount rates of flexible resource j and energy storage z respectively; and />The power capacity and the energy capacity of the energy storage z are respectively; x is x k,j The ratio of the flexible resource j used for planning in the kth power grid partition to the flexible resource aggregation amount considered by the flexible resource aggregation model; /> and />Generating power and starting power of a thermal power cluster n in a k-th power grid partition in an mth-month, d-th, and t-th time period respectively; />Actual response power for flexible resource j in the kth grid partition at the mth month, d day, t time period; />For connecting the power transmitted by the two section interconnection lines l to k in the k power grid section of the mth time section of the mth month and the d day, the power transmitted by the two section interconnection lines l to k is->The method comprises the steps of respectively discarding wind power and light power in a kth grid partition in a mth month, d day and t time period; / >The unit operation cost of flexible resource j, thermal power unit cluster n, wind abandon, light abandon and power transmission in the kth power grid partition in the mth month, d day and t time period is respectively; />The unit investment cost of the energy storage z power capacity, the unit investment cost of the energy storage z energy capacity and the unit investment cost of the flexible resource j are respectively; subscript om represents the annual maintenance cost of the corresponding resource; c (C) start,k,n and Cgen,k,n The starting cost and the generating cost of the thermal power cluster n in the kth power grid partition are respectively; gamma ray w and γpv The unit cost of the abandoned wind and the unit cost of the abandoned light are respectively; c (C) fle,op,j The cost of calling the flexible resource j to respond to the unit electric quantity is set; c (C) l,k The unit power transmission cost of power transmission from the power grid partition l to the power grid partition k is set; /> and />Respectively representing the absolute value of the transmission power and the absolute value of the actual response power of the flexible resource j in the kth grid partition in the mth month, d day and t time period;
the constraint conditions include:
aggregate output constraint of thermal power generating unit:
wherein ,the aggregate online capacity of the thermal power generating unit cluster n in the kth power grid partition in the mth month, d day and t time period; />The starting capacity and the stopping capacity of the thermal power generating unit cluster n in the k power grid partition in the mth month, the d day and the t time period are respectively; / >Is the rated capacity of the thermal power cluster n in the kth power grid partition; />The kth grid segment at the mth month, the d day and the t time periodActual power generation of thermal power generating unit cluster n in the zone; />Respectively obtaining the maximum output ratio and the minimum output ratio of the thermal power cluster n in the kth power grid partition; /> and />Respectively the maximum climbing rate and the minimum climbing rate of the thermal power generating unit cluster n in the kth power grid partition; />Respectively the minimum starting time and the minimum stopping time of the thermal power cluster n in the kth power grid partition;
restraint of hydroelectric generating set:
in the formula ,for the power generated by hydro-generator set i in the kth grid section of the mth month, d day, t time period,/v>Respectively obtaining the minimum power and rated power of the hydroelectric generating set i in the kth power grid partition; t (t) d Is the set of all time intervals on day d；/> and />Respectively generating a daily minimum generating capacity and a maximum generating capacity of the hydroelectric generating set i in the power grid subarea of the mth month and the d day; /> and />Respectively determining the maximum uphill power and the maximum downhill power of the hydroelectric generating set I in the kth power grid partition;
multiple types of energy storage constraints:
wherein ,the self-discharge efficiency, the charging efficiency and the discharging efficiency of the energy storage z in the kth power grid partition are respectively; / > and />Charging power and discharging power of energy storage z in a kth grid partition in a nth time period of an mth month, a d day and a t time period; /> and />The energy in the day and the energy in the daytime of the energy storage z in the k power grid subarea in the mth month, d day and t time period respectively; SOC (State of Charge) 0 The initial charge state of the energy storage in the day; m and D and T are the maximum values of M, D and T respectively; omega shape m ,Ω y ,Ω d Respectively representing energy storage type sets suitable for month balance, year balance and day balance; m is M 1(m) and Mend (m) represents the first and last days of month m, respectively;
flexible resource constraints;
wherein ,Xmax,k,j The total amount of flexible resources j in the kth power grid partition;a lower power limit, an upper power limit, a lower accumulated consumption energy limit and an upper accumulated consumption energy limit of a flexible resource j in a kth power grid partition in a mth month, d day and t time period respectively;
new energy power generation constraint:
wherein , and />Wind power generation potential and photovoltaic power generation potential in a kth grid partition of an mth month, d day and t time period respectively;
power balance constraint and standby constraint:
wherein ,βup 、β dn An up-regulation reserve factor and a down-regulation reserve factor related to the load, respectively; alpha up 、α dn Up-regulation reserve and down-regulation reserve respectively associated with renewable energy sources; Is the baseline load of flexible resource j in the kth grid partition for the mth month, d day, and t time period; />For at the mth monthActual response power of flexible resource j in the kth grid partition of the nth time period of the d day;
inter-zone tie-line constraints:
wherein ,the power output by the power grid partition k in the mth time period of the mth month and the d day through the connecting line pair l between the power grid partitions k and l; p (P) out,min,k,l and Pout,max,k,l The lower and upper power limits of the tie-line between grid partitions k and l, respectively.
In one embodiment of the invention, the representative day is obtained by hierarchical clustering.
In a specific embodiment of the present invention, the converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering typical days includes:
arranging the obtained typical days according to time sequence, and obtaining a load curve, a wind power curve, a photovoltaic power curve and an aggregate feasible region of various flexible resources corresponding to the typical day sequence according to the ordered typical day sequence;
converting formula (5) in the multi-type energy storage and load side flexible resource joint planning model to formula (26), wherein P dω Probability of occurrence for the corresponding typical day; converting formula (16) to formula (27), wherein D dω Total days in the same category as the typical day d when clustering; converting formula (18) to formula (28), wherein ω d As a collection of typical days, M 1,ω(m) and Mend,ω (M) represents the first and last typical days in the mth month, |M ω The I is the month of the last typical day after all typical days are ordered in time sequence, and the I D is ω The 'I' is the last classical after all typical days are ordered in time sequenceDay of the day;
and after the conversion is finished, obtaining the multi-type energy storage and load side flexible resource joint planning model considering the typical day.
In a specific embodiment of the present invention, the planning result of the energy storage and the flexible resource includes power capacity of various energy storage in each grid partitionEnergy capacity->Flexible resource quantity x of actual call k,j N j Is a solution to the optimization of (3).
An embodiment of a second aspect of the present invention provides a multi-type energy storage and load side flexible resource joint planning apparatus, including:
the aggregation feasible region acquisition module is used for acquiring aggregation feasible regions of various flexible resources by establishing feasible region models of single flexible resources under different categories;
the planning model construction module is used for establishing a multi-type energy storage and load side flexible resource joint planning model based on the aggregate feasible domains of the various flexible resources in English;
The typical day conversion module is used for converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and the planning module is used for solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day so as to obtain the planning result of the energy storage and flexible resource.
An embodiment of a third aspect of the present invention provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform a multi-type energy storage and load side flexible resource joint planning method as described above.
An embodiment of a fourth aspect of the present invention proposes a computer readable storage medium storing computer instructions for causing the computer to execute the above-mentioned method for jointly planning multi-type energy storage and load-side flexible resources.
The invention has the characteristics and beneficial effects that:
1) The invention briefly describes the lumped flexible characteristic of the controllable resources of the cluster by establishing an energy boundary model and a power boundary model for each power distribution side flexible resource and aggregating the energy boundary and the power boundary of a single flexible resource into an aggregate feasible domain of the flexible resource; then, a joint planning model considering multi-type energy storage and flexible resources is established, and a power system long-time scale operation simulation considering the multi-type flexible resources to participate in power grid dispatching is embedded in the model so as to consider complementary flexibility of the multi-type energy storage and the flexible resources; the representative days are selected by hierarchical clustering and rearranged into a continuous time series to accelerate the algorithm solution.
2) In the long-time scale simulation of the power system, the invention considers different types of flexible resources to participate in scheduling, and simultaneously considers different balance periods of multi-type energy storage so as to fully utilize system resources and consider cross-season energy balance of the power system.
3) The invention adopts a hierarchical clustering method to select typical days, rearranges the obtained related data of the typical days according to time sequence, and improves the solving speed of the model on the basis of considering the daytime energy balance of the electric power system.
4) In the system flexibility planning, the complementary characteristics of the flexible resources of the power distribution side and the multi-type energy storage resources are considered, so that the flexible resources of the power distribution side with low investment cost can be effectively utilized, the operation efficiency of the power system is improved, and the total investment and the operation cost of the system are reduced.
Drawings
Fig. 1 is an overall flowchart of a multi-type energy storage and load side flexible resource joint planning method according to an embodiment of the present invention.
Fig. 2 is a graph of exemplary upper and lower energy bounds for flexible resources of an electric vehicle in accordance with an embodiment of the present invention.
FIG. 3 is a graph of exemplary upper and lower energy bounds for an air conditioning flexible resource in accordance with an embodiment of the present invention.
FIG. 4 is a graph of exemplary upper and lower energy bounds for an adjustable industrial load flexible resource in accordance with one embodiment of the present invention.
Detailed Description
The invention provides a method and a device for jointly planning multi-type energy storage and load side flexible resources, wherein the technical scheme in the embodiment of the invention is clearly and completely described below by combining specific embodiments and drawings, and obviously, the described embodiment is only one embodiment of the invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.
An embodiment of a first aspect of the present invention provides a method for jointly planning multiple types of energy storage and load side flexible resources, including:
acquiring an aggregate feasible domain of various flexible resources by establishing a feasible domain model of a single flexible resource under different categories;
based on the aggregate feasible domain of the various flexible resources, establishing a multi-type energy storage and load side flexible resource joint planning model;
converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day to obtain a planning result of the energy storage and flexible resource.
In a specific embodiment of the present invention, the method for jointly planning multi-type energy storage and load side flexible resources, the overall flow is shown in fig. 1, includes the following steps:
1) And generating an aggregate feasible domain of various flexible resources by utilizing Monte Carlo sampling by establishing a feasible domain model of a single flexible resource under different categories. The method comprises the following specific steps:
1-1) constructing a feasible domain model of various flexible resources;
In this embodiment, the feasible domain model of the single flexible resource is composed of an energy boundary model and a power boundary model of the flexible resource, and the power upper limit and the power lower limit in each time period and the accumulated energy absorbed from the power system are respectively described according to the physical electricity characteristics and the operation rule of the flexible resource. For the power boundary model, the starting working time and the ending working time of the flexible resource are generally considered, and the possible maximum operating power and the possible minimum operating power of the resource are also considered in the time period, and the upper and lower power limits of the resource are generally zero outside the time period. For the energy boundary model, two kinds of energy boundary models are needed, one is that the energy boundary model has the highest and lowest consumption energy value at the end of working time, the flexible resource is commonly found in factory load and electric automobile, because the consumption energy and output/battery charge state are related, the energy boundary of the resource should reach the highest and lowest consumption energy value at the end of working time respectively, the middle process is determined according to whether the resource can be discharged or not, if the energy can be discharged, the lower energy limit is generally the energy value discharged to the lowest energy value, the energy value reaches the lowest consumption energy value at the end of working time, otherwise, the energy boundary is firstly operated with the lowest power, and the energy value reaches the lowest consumption energy value at the end of working time. The upper energy limit is at the beginning of the operating time, i.e. at the highest power, until the maximum energy consumption value is reached. Another flexible resource has no explicit maximum and minimum energy consumption values, but has a maximum and minimum two modes of operation, such as air conditioning load, and then has an upper and lower energy limit that is the cumulative energy consumption curve for the mode of operation that is the maximum and minimum.
And on the basis of establishing a single flexible resource boundary model, the power boundary and the energy boundary of the same type of flexible resource are overlapped, so that an aggregate feasible domain reflecting a large number of adjustable capacities of the flexible resource can be obtained. In a specific embodiment of the invention, three typical flexible resource feasible domain model construction methods of electric vehicles, air conditioning loads and adjustable industrial loads are specifically provided, and an energy boundary and a power boundary of the feasible domain model construction method are respectively depicted.
1-1-1) an electric automobile;
in this embodiment, two situations of charging regulation and control of the electric vehicle and feeding (V2G) to the power grid are considered at the same time. Thus, for the followingTime of arrival at time->The upper and lower limits of the change of the electric power with time of the exiting electric automobile i can be expressed as:
in the formula ,the upper and lower power limits of the electric automobile i at the moment t are respectively the power boundaries, and the power is +.> and />The rated charge power and the rated discharge power of the electric automobile i are respectively. In the embodiment, it is assumed that the owner of the electric automobile allows the power battery of the owner to discharge to the power grid through regulation and control. When the electric vehicle is not allowed to discharge, the rated discharge power is only required to be set to be 0.
In this embodiment, the upper and lower boundary, i.e., energy boundary curves of the accumulated power consumption of the electric vehicle over time are shown in fig. 2. In fig. 2, the horizontal axis represents time and the vertical axis represents energy; Respectively an upper boundary and a lower boundary of the accumulated electricity consumption of the electric automobile i; />The state of charge when the electric automobile i arrives; e (E) battery,i The capacity of a power battery of the electric automobile i; />The minimum state of charge that needs to be reached when the electric vehicle i leaves. The maximum allowable state of charge of the electric vehicle i is +.>
As can be seen from fig. 2, the upper limit of the cumulative consumed electric power of the electric automobileCorresponding to its fastest power track (a-B-C). On this track, the electric vehicle is charged with the rated charge power after switching on until its state of charge reaches the maximum allowable state of charge +.>Until that point. In another extreme case, the cumulative power consumption of the electric vehicle follows the trajectory (a-D-F-G). On the track, the electric automobile is rated with discharge power after being connectedThe discharge is performed until its state of charge reaches a minimum allowable state of charge. When approaching the departure time of the electric automobile, the electric automobile charges the battery to the minimum charge state set by the user with rated charging power. Thus, the feasible range of the change of the charging and discharging power of the electric vehicle with time is described by the upper and lower bounds of the instantaneous power consumption and the accumulated consumed electric quantity of the electric vehicle. In other words, any trajectory of power consumption that satisfies this set of constraints is feasible for the electric vehicle.
1-1-2) an air conditioner;
in this embodiment, the air conditioning user participates in load regulation by accepting that the indoor temperature is higher or lower than the set value. The air conditioner i needs to ensure that the indoor temperature is within the acceptable temperature range of the user [ T ] min ,i,,T max,i ]，T min,i Is the minimum acceptable temperature of the air conditioner i, T max,i Is the maximum acceptable temperature for air conditioner i. The upper and lower bound energy boundary curves of the cumulative power consumption of the air conditioner over time are shown in fig. 3. In fig. 3, the horizontal axis represents time and the vertical axis represents energy;respectively an upper boundary and a lower boundary of the accumulated electricity consumption of the air conditioner i; />Respectively set to the lowest temperature T for the air conditioner min,i Time on and off, +.>Respectively set to the highest temperature T for the air conditioner max,i Time on and off.
The upper limit of the accumulated energy consumption of the air conditioner load is as follows: the air conditioner i is operated to the lowest temperature T with the maximum refrigerating power min,i Cooling was then stopped (traces a-B). When the indoor temperature exceeds the temperature dead zone delta (trace B-C), the air conditioner is operated again at the maximum cooling power. The above process is repeated until the air conditioner is turned off. The lower boundary of the corresponding energy is set to the maximum acceptable temperature T for the air conditioner i temperature max,i A cumulative energy consumption curve at that time. The switching frequency and switching time of the air conditioner at a certain set temperature can be calculated according to a first-order thermodynamic model of the air conditioner.
wherein ,rated power for air conditioner i +.>Is the upper and lower power limit of air conditioner i, +.>The start and end operating times for air conditioner i.
1-1-3) adjustable industrial load;
in this embodiment, for the adjustable industrial load i, the start working time and the end working time in the scheduling period are respectively and /> Rated power and minimum operating power for an adjustable industrial load i, +.>Is the upper and lower power limits of the adjustable industrial load i. During scheduling, a minimum yield of the plant must be met. Thus, the upper boundary of the cumulative power consumption curve is at the start of operationThe highest throughput is achieved at maximum power, and the lower boundary is the minimum operating power at the beginning of the operation and the minimum throughput requirement at the end of the operation time. The upper and lower bounds of the cumulative power consumption of the adjustable industrial load with time are shown in fig. 4, the horizontal axis represents time, and the vertical axis represents energy;respectively an upper limit and a lower limit of the accumulated electricity consumption of the adjustable industrial load i; /> and />Power consumption corresponding to the daily maximum and minimum output of the adjustable industrial load i.
1-2) according to the flexible resource feasible region model obtained in the step 1-1), carrying out Monte Carlo sampling on key parameters related to each type of flexible resources by considering the operation characteristics (such as start and end working time distribution) of each type of flexible resources, external factors (such as air temperature, holidays and working days) and the like, so as to obtain the aggregate feasible region of each type of flexible resources.
In this embodiment, the power boundary and the energy boundary of each single flexible resource in the whole flexible resources are added to obtain the aggregate feasible region of each flexible resource, and in this embodiment, the power boundaries in steps 1-1-1) to 1-1-3) are addedEnergy boundary +.>Extending to multiple days, increasing subscripts m, d, and extending to multiple grid partitions, increasing subscript k. Any type of flexible resource j is represented by j (j includes ac, ev, ind) instead of superscript ac, ev, ind. Thereby get +.>To represent the lower power bound, upper power bound, lower energy bound, upper energy bound of the ith individual of flexible resource j in the kth grid partition of the mth, d, t time period; wherein the lower power bound and the upper power bound form a power bound and the lower energy bound and the upper energy bound form an energy bound. The power boundary and the energy boundary of the same type of flexible resource are overlapped, and the aggregate feasible domain of the flexible resource j can be obtained as follows:
wherein ,the aggregate feasible region of the flexible resource j at the t-th time period of the mth month and the d-th day is formed. N (N) j Consider the number of samples of the aggregated flexible resource j for the aggregate model. In this embodiment, the power and energy boundaries of the ith individual of the flexible resource j may be obtained by monte carlo sampling.
It should be noted that in this embodiment, only electric vehicles, air conditioners and adjustable industrial loads are selected, and in practice, the flexible resource types included in j may be more.
And (4) obtaining the aggregate feasible region of various flexible resources by using the formula (4).
In the electric vehicle, parameters to be sampled include a time to reach the charging station, a time to leave the charging station, a maximum leaving state of charge, a minimum leaving state of charge, a battery capacity, and a maximum depth of discharge. The distribution of the parameters can be obtained according to the historical data of the charging station, particularly, the distribution can be changed along with the difference of air temperature, holidays and workdays, different distributions are selected according to the historical data, the parameters of each electric automobile i are assigned by Monte Carlo sampling, and then the energy boundary and the power boundary of each electric automobile i are obtained.
For air conditioners, parameters to be sampled include rated power of each air conditioner, acceptable maximum and minimum temperature, thermal resistance of a room, heat capacity of the room and on-off time of the air conditioner. The data distribution can be obtained through historical data of an electric power department, and the Monte Carlo sampling is utilized to assign value to 10000 air conditioners. The data to be obtained are time-varying air temperatures of the investigation region, which can be obtained from the weather department. The start-stop state of each air conditioner in each time period can be calculated by using the air temperature, the room heat capacity, the thermal resistance and the maximum and minimum acceptable temperatures, and then the power boundary and the energy boundary are calculated. And superposing the energy boundary and the power boundary of 10000 air conditioners to obtain a feasible region of the adjustable capacity of the air conditioner group.
Similarly, for an adjustable industrial load, the parameters that need to be sampled include rated power, minimum operating power, start and end operating times, maximum and minimum power consumption. The distribution can be obtained through historical data of an electric power department, and the Monte Carlo sampling is utilized to assign values for 300 adjustable industrial loads, so that a power boundary and an energy boundary are calculated. And superposing the energy boundary and the power boundary of 300 adjustable industrial loads to obtain a feasible region of the adjustable capacity of the adjustable industrial load group.
2) Based on the aggregate feasible domain of the various flexible resources obtained in the step 1), establishing a multi-type energy storage and load side flexible resource joint planning model formed by an objective function and constraint conditions; the method comprises the following specific steps of;
2-1) constructing an objective function of a multi-type energy storage and load side flexible resource joint planning model;
in this embodiment, the objective function of the optimized planning model for multiple types of energy storage and load side flexible resources is calculated as the total investment cost C within one year inv Cost of maintenance C om And operating cost C op The sum is minimized as a goal. Wherein the total investment cost comprises the investment cost of energy storage and the investment cost of equipment required by the deployment of flexible resources to participate in power grid dispatching, and the operation cost comprises The method comprises the steps of generating cost and starting cost of the thermal power generating unit, power transmission cost among all partitions of the system, wind and light discarding cost and flexible resource calling cost. The expression of the objective function is as follows:
minf＝C inv +C om +C op (5)
wherein ,
/>
wherein ,Ay,j 、A y,z The investment discounts for flexible resource j and energy storage z, respectively. and />The power capacity and the energy capacity of the planned energy storage z, respectively. X is x k,j The ratio of the flexible resource j used for planning in the kth grid partition to the flexible resource aggregation amount considered by the flexible resource aggregation model. The subscripts m, d, t, k indicate the nth time period of the mth month and the nth day in the kth grid partition, respectively, unless explicitly stated otherwise, and will not be described in detail. /> and />The power generation power and the starting power of the thermal power cluster n in the k-th power grid partition in the mth-month, d-th and t-th time periods are respectively. />For the actual response power of the flexible resource j in the kth power grid partition in the mth month, d day and t time period, the value is the difference between the actual power used by the flexible resource and the baseline power of the flexible resource j. />For connecting the power transmitted by the two section interconnection lines l to k in the k power grid section of the mth time section of the mth month and the d day, the power transmitted by the two section interconnection lines l to k is->And the wind discarding power and the light discarding power in the k-th grid partition in the mth and the d-th and t-th time periods respectively. / >The unit operation cost of flexible resource j, thermal power unit cluster n, wind abandon, light abandon and power transmission in the kth power grid partition in the mth month, d day and t time period is respectively shown. />The unit investment cost of the energy storage z power capacity, the unit investment cost of the energy storage z energy capacity and the unit investment cost of the flexible resource j are respectively. Subscript om represents the annual maintenance cost of the corresponding resource. C (C) start,k,n and Cgen,k,n The starting cost and the generating cost of the thermal power cluster n in the kth power grid partition are respectively. Gamma ray w and γpv The unit cost of the waste wind and the unit cost of the waste light are respectively. C (C) fle,op,j Is the cost of invoking the flexible resource j to respond to the unit power. C (C) l,k The unit power transmission cost of power transmission from the power grid partition l to the power grid partition k is set. In order to take into account the direction of the energy on the transmission line, and the direction of the flexible resource response power,by introducing auxiliary variables-> and />The absolute value of the transmission power and the absolute value of the actual response power of the flexible resource j in the kth grid partition in the mth month, d day and t time period are respectively shown.
2-2) constructing constraint conditions of a multi-type energy storage and load side flexible resource joint planning model, wherein the constraint conditions are as follows:
2-2-1) aggregate output constraint of a thermal power unit;
The large-scale unit combination problem is generally composed of a large number of 0-1 variables, and is difficult to solve. The aggregate output constraint of the thermal power generating unit gathers units with similar climbing capacity, power generation and cost into a unit cluster, and converts integer variables into continuous variables. The start-up/shut-down process of the units within the cluster of units is represented by a continuous variable. In this embodiment, the thermal power generating unit includes a coal-to-electricity unit, a natural gas unit, and a thermoelectric unit.
wherein ,is the aggregate online capacity of thermal power generating unit cluster n in the kth grid partition of the mth month, d day and t time period. />The starting capacity and the stopping capacity of the thermal power generating unit cluster n in the k power grid partition in the mth month, the d day and the t time period are respectively. />Is the rated capacity of the thermal power cluster n in the kth power grid partition. />Is the actual power generated by the thermal power generating unit cluster n in the k power grid partition of the mth month, d day and t time period. />The maximum output ratio and the minimum output ratio of the thermal power cluster n in the kth power grid partition are respectively. /> and />And the maximum ramp rate and the minimum ramp rate of the thermal power generating unit cluster n in the kth power grid partition are respectively. />The minimum starting time and the minimum stopping time of the thermal power cluster n in the kth power grid partition are respectively set. Equation (10) describes the relationship between the startup/shutdown capacity and the online capacity of the thermal power plant cluster, and limits the startup/shutdown capacity and the online capacity not to exceed the rated power of the thermal power plant cluster, and also specifies the upper limit and the lower limit of the actual power generation power of the thermal power plant cluster. Equation (11) limits the maximum ramp up and down rate of the thermal power plant cluster. Equation (12) limits the minimum start-up and shut-down time of the thermal power plant.
2-2-2) hydroelectric generating set constraint;
hydroelectric generating sets are used as generators which are mainly limited by upstream water supply and storage capacity, and rated power constraint, total daily power generation constraint and climbing constraint are generally considered.
in the formula ,for the power generated by hydro-generator set i in the kth grid section of the mth month, d day, t time period,/v>The minimum power and the rated power of the hydro-generator set i in the kth grid partition are respectively. T (t) d Is the set of all time intervals on day d, +.> and />The daily minimum power generation amount and the maximum power generation amount of the hydroelectric generating set i in the grid partition of the kth on the mth month and the d day are respectively determined by the reservoir capacity and the upstream water inflow. /> and />And respectively determining the maximum uphill power and the maximum downhill power of the hydroelectric generating set i in the kth power grid partition. The upper and lower limits of the power of the hydroelectric generating set are limited by the formula (13), and the maximum and minimum power generation energy and the maximum power generation of the hydroelectric generating set are respectively limited by the formulas (14) and (15)Downhill climbing power.
2-2-3) multi-type energy storage constraints;
in this embodiment, in order to consider different types of stored energy, different balancing periods, and seasonal energy balance of the power system, the stored energy is divided into daily energy and daytime energy. The daily energy treatment is the unbalance of power consumption and power generation in one day, and the daily energy treatment is the long-term energy balance problem caused by the periodical change of the renewable energy source power generation, such as inter-week balance or inter-month balance. Equation (16) calculates the energy of the current period from the charge/discharge power of the previous period. Equation (17) limits the charge/discharge power not to exceed the rated capacity. Equation (18) specifies that daytime energy needs to be balanced daily. The calculation of the daytime energy is given by equation (19), i.e. the remaining value of the daytime energy plus the unbalance of the energy in the previous day. Equation (20) ensures that the total energy does not exceed the energy capacity of the stored energy. The formula (21) is energy balance constraint of energy storage in different balance periods of different types, and is respectively that daily energy of daily balance energy storage is kept equal, daily energy of first and last days of monthly balance energy storage is kept equal, and daily energy of first and last days of yearly balance energy storage is kept equal.
wherein ,the self-discharge efficiency, the charging efficiency and the discharging efficiency of the energy storage z in the kth power grid partition are respectively. /> and />The charging power and the discharging power of the energy storage z in the kth grid partition in the mth month, the d day and the t time period. /> and />The energy within day and the energy between days of the energy storage z in the kth grid section in the mth month, d day and t time period respectively. SOC (State of Charge) 0 Is the initial state of charge of the stored energy in the day. And (3) M, D and T are the maximum values of M, D and T respectively. Omega shape m ,Ω y ,Ω d Respectively represent sets of energy storage types suitable for month balance, year balance and day balance, respectively. M is M 1(m) and Mend (m) represents the first and last day of month m, respectively.
2-2-4) flexible resource constraints;
wherein ,xk,j The ratio of the flexible resource j used for planning in the kth grid partition to the flexible resource aggregation amount considered by the flexible resource aggregation model. X is X max,k,j Flexibility in partitioning for kth gridTotal amount of resource j. The lower power limit, the upper power limit, the lower accumulated consumption energy limit and the upper accumulated consumption energy limit of the flexible resource j in the kth power grid partition in the mth month, the d day and the t time period are respectively. Equation (22) specifies that the amount of flexible resources that are called must not exceed the total amount of flexible resources, while the actual power and energy used by the flexible resources must not exceed the corresponding upper and lower limits.
2-2-5) new energy power generation constraint;
wherein , and />The actual net power of the new energy power generation is limited to be smaller than the actual net power of the new energy power generation by the equation (23), and the difference value is the abandoned wind abandoned light power.
2-2-6) power balancing constraints and standby constraints;
/>
wherein ,βup 、β dn An up-regulation reserve factor and a down-regulation reserve factor related to the load, respectively; alpha up 、α dn Up-regulation reserve and down-regulation reserve, respectively, associated with renewable energy sources.Is the baseline load of flexible resource j in the kth grid partition for the mth month, d day, and t time period. />The actual response power for flexible resource j in the kth grid partition for the mth month, d day, t time period. Equations (25) - (26) provide for up-down adjustments to maintain safe operation of the power system. Equation (27) is a system power balancing constraint in which the contribution of the flexible resource is its actual electricity usage increment or decrement from the baseline, which is given by equation (24).
2-2-7) inter-zone tie constraint;
wherein ,is the power output by grid partition k through the tie-line pair l between grid partitions k and l at the mth and the d-th time period. P (P) out,min,k,l and Pout,max,k,l The lower and upper power limits of the tie-line between grid partitions k and l, respectively. Equation (28) ensures that line power is not out of limit while specifying the direction of power flow on the link.
3) And (3) selecting a typical day based on hierarchical clustering, and converting the model in the step (1) into a multi-type energy storage and load side flexible resource joint planning model considering the typical day.
In this embodiment, for one year of power system operation simulation, millions of continuous variables are involved, even at 1 hour intervals. To increase computational efficiency, it is necessary to cluster data and select a typical day. In the clustering method, hierarchical clustering is based on generating a nested cluster tree, and thus can provide more information than hierarchical clustering. Hierarchical clustering allows us to select appropriate typical days at different heights of the tree as needed. Specifically, the number of typical days can be comprehensively selected according to the scale of actual calculation, the required solution speed and the number of layers of the clustering tree in combination with the distinction of the daily payload curves. The larger the number of typical days, the slower the solution speed, but the more accurate the result, the smaller the number of typical days, the faster the solution speed and the less accurate the result. In one embodiment of the present invention 58 representative days were selected from the 365 day data.
In order to consider the daytime energy balance relation of the power system, it is necessary to rearrange the selected typical daily data into a continuous time sequence in time sequence, wherein the typical daily data comprise load, wind power, photovoltaic power and flexible resource aggregate feasible domains, and after rearranging the data in the corresponding typical daily sequence, the aggregate feasible domains of the load curve, the wind power curve, the photovoltaic power curve and the various flexible resources under study are obtained.
At the same time, to ensure the practical meaning of the cost calculation, the running cost formula (8) should also be converted into formula (29), where P dω Is the probability of occurring for the typical day. For multi-type energy storage constraints, to ensure accurate daytime energy balance, equation (19) is converted to equation (30), where D dω For the total number of days in the same class as the typical day D when clustered, D is given in formula (30) dω Multiplying the corresponding unbalanced daily energy. Converting formula (21) to formula (31), wherein ω d As a collection of typical days, M 1,ω(m) and Mend,ω (M) represents the first and last typical days in the mth month, |M ω I is all typicalThe last typical day is in month, |D, after the days are ordered in chronological order ω And I is the last typical day after all typical days are ordered in time sequence. Thus, the balance energy storage constraint of the month is that the daytime energy of the first and last typical days of the month should be kept equal, and the balance energy storage constraint of the year is that the daytime energy of the first and last typical days of the year should be kept equal.
/>
Through the transformation, the variable number of model optimization can be greatly reduced, and the calculation efficiency is improved.
4) And 3) solving the model converted in the step 3) to obtain planning results of energy storage and flexible resources of each partition of the power grid.
In this embodiment, the models composed of formulas (4) - (7), (9) - (18), (20), (22) - (31) are linear programming models, and the power capacities of various stored energy in the Kth grid partition can be obtained by efficiently solving through existing commercial solvers such as Cplex, groubi and the likeEnergy capacity->Flexible resource quantity x of actual call k,j N j The three optimal solutions are the energy capacity and the power capacity of the energy storage planning of the k-th power grid partitionThe amount, and the total amount of flexible resources actually required to be invoked.
In order to achieve the above embodiments, an embodiment of a second aspect of the present invention provides a multi-type energy storage and load side flexible resource joint planning apparatus, including:
the aggregation feasible region acquisition module is used for acquiring aggregation feasible regions of various flexible resources by establishing feasible region models of single flexible resources under different categories;
the planning model construction module is used for establishing a multi-type energy storage and load side flexible resource joint planning model based on the aggregate feasible domains of the various flexible resources in English;
The typical day conversion module is used for converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and the planning module is used for solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day so as to obtain the planning result of the energy storage and flexible resource.
It should be noted that the foregoing explanation of the embodiment of the method for jointly planning multi-type energy storage and load-side flexible resources is also applicable to the device for jointly planning multi-type energy storage and load-side flexible resources in this embodiment, and is not repeated herein. According to the multi-type energy storage and load side flexible resource joint planning device provided by the embodiment of the invention, the aggregate feasible regions of various flexible resources are obtained by establishing a feasible region model of single flexible resource under different categories; based on the aggregate feasible domain of the various flexible resources, establishing a multi-type energy storage and load side flexible resource joint planning model; converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day; and solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day to obtain a planning result of the energy storage and flexible resource. Therefore, the complementary characteristics of the flexible resources of the power distribution side and the multi-type energy storage resources can be considered in planning, the flexible resources of the power distribution side with low investment cost can be effectively utilized, the operation efficiency of the power system is improved, and the total investment and the operation cost of the system are reduced.
To achieve the above embodiments, an embodiment of a third aspect of the present invention provides an electronic device, including:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions configured to perform a multi-type energy storage and load side flexible resource joint planning method as described above.
To achieve the above embodiments, a fourth aspect of the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to execute the above method for jointly planning multi-type energy storage and load-side flexible resources.
It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.
The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to perform a multi-type energy storage and load side flexible resource joint planning method of the above embodiment.
Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means 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 present application. In this specification, schematic representations of the above terms are not necessarily directed 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, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented as software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.

Claims (10)

1. A multi-type energy storage and load side flexible resource joint planning method is characterized by comprising the following steps: acquiring an aggregate feasible domain of various flexible resources by establishing a feasible domain model of a single flexible resource under different categories;
based on the aggregate feasible domain of the various flexible resources, establishing a multi-type energy storage and load side flexible resource joint planning model;
converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day to obtain a planning result of the energy storage and flexible resource.
2. The method of claim 1, wherein the feasible region model of the single flexible resource consists of an energy boundary model and a power boundary model of the flexible resource; respectively superposing the power boundary and the energy boundary of each flexible resource under the same category to obtain an aggregate feasible domain of the flexible resource;
The aggregate feasible domain expression of any type of flexible resource j is as follows:
wherein ,the power lower bound, the power upper bound, the energy lower bound and the energy upper bound of the ith individual of the flexible resource j in the kth power grid partition in the mth month, the d day and the t time period respectively; n (N) j The number of individual samples for flexible resource j.
3. The method according to claim 2, wherein the individual samples of the flexible resource j are obtained by monte carlo sampling a population of such flexible resources.
4. The method of claim 2, wherein the multi-type energy storage and load side flexible resource joint planning model is comprised of an objective function and constraints;
wherein the objective function is minimized as a sum of total investment cost, maintenance cost and operation cost in one year, and the expression is as follows:
minf＝C inv +C om +C op (2)
wherein ,
wherein ,Cinv To total investment cost, C om For maintenance cost, C op Is the running cost; a is that y,j 、A y,z Investment discount rates of flexible resource j and energy storage z respectively; and />The power capacity and the energy capacity of the energy storage z are respectively; x is x k,j The ratio of the flexible resource j used for planning in the kth power grid partition to the flexible resource aggregation amount considered by the flexible resource aggregation model; / > and />Generating power and starting power of a thermal power cluster n in a k-th power grid partition in an mth-month, d-th, and t-th time period respectively; />Actual response power for flexible resource j in the kth grid partition at the mth month, d day, t time period; />For connecting the power transmitted by the two section interconnection lines l to k in the k power grid section of the mth time section of the mth month and the d day, the power transmitted by the two section interconnection lines l to k is->The method comprises the steps of respectively discarding wind power and light power in a kth grid partition in a mth month, d day and t time period; />The unit operation cost of flexible resource j, thermal power unit cluster n, wind abandon, light abandon and power transmission in the kth power grid partition in the mth month, d day and t time period is respectively; />The unit investment cost of the energy storage z power capacity, the unit investment cost of the energy storage z energy capacity and the unit investment cost of the flexible resource j are respectively; subscript om represents the annual maintenance cost of the corresponding resource; c (C) start,k,n and Cgen,k,n The starting cost and the generating cost of the thermal power cluster n in the kth power grid partition are respectively; gamma ray w and γpv The unit cost of the abandoned wind and the unit cost of the abandoned light are respectively; c (C) fle,op,j The cost of calling the flexible resource j to respond to the unit electric quantity is set; c (C) l,k The unit power transmission cost of power transmission from the power grid partition l to the power grid partition k is set; / > and />Respectively representing the absolute value of the transmission power and the absolute value of the actual response power of the flexible resource j in the kth grid partition in the mth month, d day and t time period;
the constraint conditions include:
aggregate output constraint of thermal power generating unit:
wherein ,the aggregate online capacity of the thermal power generating unit cluster n in the kth power grid partition in the mth month, d day and t time period; />The starting capacity and the stopping capacity of the thermal power generating unit cluster n in the k power grid partition in the mth month, the d day and the t time period are respectively; />Is the rated capacity of the thermal power cluster n in the kth power grid partition; />The actual power generation of the thermal power generating unit cluster n in the kth power grid partition in the mth month, d day and t time period; />Respectively obtaining the maximum output ratio and the minimum output ratio of the thermal power cluster n in the kth power grid partition; /> and />Maximum climbing of thermal power generating unit cluster n in kth power grid partitionRate and minimum ramp rate; />Respectively the minimum starting time and the minimum stopping time of the thermal power cluster n in the kth power grid partition;
restraint of hydroelectric generating set:
in the formula ,for the power generated by hydro-generator set i in the kth grid section of the mth month, d day, t time period,/v >Respectively obtaining the minimum power and rated power of the hydroelectric generating set i in the kth power grid partition; t (t) d Is the set of all time intervals on day d; /> and />Respectively generating a daily minimum generating capacity and a maximum generating capacity of the hydroelectric generating set i in the power grid subarea of the mth month and the d day; /> and />Respectively determining the maximum uphill power and the maximum downhill power of the hydroelectric generating set I in the kth power grid partition;
multiple types of energy storage constraints:
wherein ,the self-discharge efficiency, the charging efficiency and the discharging efficiency of the energy storage z in the kth power grid partition are respectively; /> and />Charging power and discharging power of energy storage z in a kth grid partition in a nth time period of an mth month, a d day and a t time period; and />The energy in the day and the energy in the daytime of the energy storage z in the k power grid subarea in the mth month, d day and t time period respectively; SOC (State of Charge) 0 The initial charge state of the energy storage in the day; m and D and T are the maximum values of M, D and T respectively; omega shape m ,Ω y ,Ω d Respectively representing energy storage type sets suitable for month balance, year balance and day balance; m is M 1(m) and Mend (m) represents the first and last days of month m, respectively;
flexible resource constraints;
wherein ,Xmax,k,j The total amount of flexible resources j in the kth power grid partition;a lower power limit, an upper power limit, a lower accumulated consumption energy limit and an upper accumulated consumption energy limit of a flexible resource j in a kth power grid partition in a mth month, d day and t time period respectively;
New energy power generation constraint:
wherein , and />Wind power generation potential and photovoltaic power generation potential in a kth grid partition of an mth month, d day and t time period respectively;
power balance constraint and standby constraint:
wherein ,βup 、β dn An up-regulation reserve factor and a down-regulation reserve factor related to the load, respectively; alpha up 、α dn Up-regulation reserve and down-regulation reserve respectively associated with renewable energy sources;is the baseline load of flexible resource j in the kth grid partition for the mth month, d day, and t time period; />Actual response power for flexible resource j in the kth grid partition at the mth month, d day, t time period;
inter-zone tie-line constraints:
wherein ,the power output by the power grid partition k in the mth time period of the mth month and the d day through the connecting line pair l between the power grid partitions k and l; p (P) out,min,k,l and Pout,max,k,l The lower and upper power limits of the tie-line between grid partitions k and l, respectively.
5. The method of claim 4, wherein the typical day is obtained by hierarchical clustering.
6. The method of claim 4, wherein said converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model taking into account typical days comprises:
Arranging the obtained typical days according to time sequence, and obtaining a load curve, a wind power curve, a photovoltaic power curve and an aggregate feasible region of various flexible resources corresponding to the typical day sequence according to the ordered typical day sequence;
converting formula (5) in the multi-type energy storage and load side flexible resource joint planning model to formula (26), wherein P dω Probability of occurrence for the corresponding typical day; converting formula (16) to formula (27), wherein D dω Total days in the same category as the typical day d when clustering; converting formula (18) to formula (28), wherein ω d As a collection of typical days, M 1,ω(m) and Mend,ω (M) represents the first and last typical days in the mth month, |M ω The I is the month of the last typical day after all typical days are ordered in time sequence, and the I D is ω The I is the last typical day after all typical days are ordered in time sequence;
and after the conversion is finished, obtaining the multi-type energy storage and load side flexible resource joint planning model considering the typical day.
7. The method of claim 6, wherein the planning of the energy storage and flexible resources comprises power capacity of each type of energy storage in each grid section Energy capacity->Flexible resource quantity x of actual call k,j N j Is a solution to the optimization of (3).
8. A multi-type energy storage and load side flexible resource joint planning device is characterized by comprising:
the aggregation feasible region acquisition module is used for acquiring aggregation feasible regions of various flexible resources by establishing feasible region models of single flexible resources under different categories;
the planning model construction module is used for establishing a multi-type energy storage and load side flexible resource joint planning model based on the aggregate feasible domains of the various flexible resources in English;
the typical day conversion module is used for converting the multi-type energy storage and load side flexible resource joint planning model into the multi-type energy storage and load side flexible resource joint planning model considering the typical day by selecting the typical day;
and the planning module is used for solving the multi-type energy storage and load side flexible resource joint planning model considering the typical day so as to obtain the planning result of the energy storage and flexible resource.
9. An electronic device, comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores instructions executable by the at least one processor, the instructions being arranged to perform the method of any of the preceding claims 1-7.
10. A computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
CN202310605193.2A 2023-05-26 2023-05-26 Multi-type energy storage and load side flexible resource joint planning method and device Pending CN116613743A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117439090A (en) * 2023-12-19 2024-01-23 浙江大学 Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117439090A (en) * 2023-12-19 2024-01-23 浙江大学 Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index
CN117439090B (en) * 2023-12-19 2024-04-02 浙江大学 Flexible resource allocation or scheduling method taking flexible adjustment coefficient as index

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