CN115860349A - Microgrid shared energy storage planning and income distribution method, system and storage medium - Google Patents
Microgrid shared energy storage planning and income distribution method, system and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of multi-energy cloud energy storage, in particular to a method, a system and a storage medium for micro-grid shared energy storage planning and income distribution, which comprises the following steps: establishing a microgrid shared energy storage planning model and a system operation optimization model considering cloud energy storage service of a thermodynamic system by taking the minimized microgrid operation cost as an objective function, and solving to obtain an optimal microgrid energy storage planning scheme and a system optimization operation scheme; establishing a microgrid operation model with no energy storage under different alliance modes, independently putting part of operation main bodies into energy storage and jointly building the energy storage by the operation main bodies, solving the microgrid operation model, and calculating the income distribution result of each operation main body by using a sharley value method. The method can fully explore the equivalent electric energy storage capacity of the thermodynamic system, and simultaneously cooperates with the conventional electric energy storage, thereby maximizing the energy storage effect, reducing the energy storage cost and promoting the local consumption of the distributed renewable energy of the microgrid; and the income distribution is fair and reasonable, and all benefit bodies of the micro-grid can be stimulated to participate.
Description
Technical Field
The invention relates to the technical field of multi-energy cloud energy storage, in particular to a method and a system for sharing energy storage planning and income distribution of a micro-grid and a storage medium.
Background
The rapid development of renewable energy technology and the increase of the power consumption ratio of renewable energy power generation have become one of the key concerns in academic circles and industrial circles. The power department as a foundation stone for social and economic development and the largest fossil energy consumption and carbon emission industry plays an important role and mission in improving the proportion of renewable energy and reducing the carbon emission. How to construct a high-proportion renewable energy power system has become a key point of research in the industry. In the related art path exploration research, energy storage technology is considered as an important means for coping with a high proportion of renewable energy access.
However, the cost of energy storage devices remains relatively high, which severely restricts the widespread use of energy storage. To solve this problem, scholars have proposed concepts such as cloud energy storage and multi-energy storage systems. The cloud energy storage technology is mainly based on the idea of energy storage resource sharing, provides shared energy storage service for a plurality of users, and achieves the purposes of reducing unit energy storage cost and improving energy storage utilization efficiency by utilizing the scale effect and the time complementarity of energy storage requirements of different users. The specific concept of cloud energy storage is detailed in the literature, "kang Chong Qing, liu Jing Kun, zhang Ning". The new form of energy storage of the future power system: cloud energy storage [ J ], power system automation, 2017, 41 (21), 2-8". The multi-energy storage system focuses on the equivalent energy storage characteristics of the energy systems except the power system, and achieves the purpose of providing equivalent energy storage service for the power system by exciting the interaction between the energy systems and the power system.
The cloud energy storage technology and the multi-energy storage technology are organically combined, and a multi-energy cloud energy storage concept is generated. The cloud energy storage technology can effectively manage sleeping energy storage resources in the system, realize high-efficiency aggregation application of the energy storage resources, improve the utilization efficiency of the energy storage device and reduce the use cost of energy storage service; the multi-energy storage technology promotes multi-system cooperation, the equivalent electric energy storage capacity of the energy subsystem is explored, and extra income sources are created for the energy subsystem while electric energy storage resources are enriched. At present, reports of an energy storage device optimization configuration method in a microgrid considering multi-energy coordination are not found, and the problem of income distribution of a system multi-type operation subject in the multi-energy cloud energy storage mode is yet to be further solved.
Disclosure of Invention
The invention aims to provide a microgrid shared energy storage planning and income distribution method, system and storage medium, which can effectively integrate and utilize equivalent energy storage resources of a comprehensive energy system, provide a fair and reasonable benefit distribution scheme, promote cooperation achievement and improve the economic benefits of all operation main bodies.
The embodiment of the invention is realized by the following technical scheme: the microgrid shared energy storage planning and income distribution method comprises the following steps:
considering multi-type energy storage resource operation constraints, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model considering the cloud energy storage service of the thermodynamic system;
solving the microgrid shared energy storage planning model and the system operation optimization model to obtain an optimal microgrid energy storage planning scheme and a system operation optimization scheme;
the optimal energy storage planning scheme and the system optimization operation scheme of the micro-grid are integrated, micro-grid operation models without energy storage, with part of operation subjects independently putting in energy storage and with the operation subjects jointly building energy storage under different alliance modes are built and solved, and the optimal energy storage planning scheme and the system optimization operation scheme of each alliance and the income calculation results of each operation subject are obtained;
and calculating the income distribution result of each operation subject by adopting a shapey value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance.
According to a preferred embodiment, the operation subject includes a microgrid operator, a load user, a thermal system and a battery energy storage.
According to a preferred embodiment, the objective function of the microgrid shared energy storage planning model is as follows:
in the above formula, C represents the total cost of the microgrid, C e Representing the total electricity cost of the microgrid, C b Representing the investment and operating costs of the batteries of the microgrid configuration, C DHS Represents the total heat cost of the microgrid, T represents the set of all periods of a typical day, v g,t Grid electricity price, P, representing time period t l,t Representing the load user power at time t, P pv,t Actual photovoltaic power generation power after light rejection, P, taken into account, representing time period t bg,t Battery discharge power, P, representing time period t bc,t Battery charging power, k, representing time period t CHP Representing the price of fuel, alpha, of a cogeneration unit p Representing the amount of fuel consumed by the cogeneration unit per unit of electric power, p CHP，t Cogeneration unit generated power, f, representing time period t b,cap Representing the cost per unit energy storage capacity of the battery, E b Denotes the capacity, P, of the microgrid-configured cell b Power of a micro-grid configuration battery is shown, r represents a discount rate, T b Indicates the service life of the battery, v b Represents the operating cost per unit time and power of the battery, alpha DHS Represents the amount of fuel consumed by the cogeneration unit per unit of thermal power, H CHP,t The heating value of the cogeneration unit for the time period t is represented.
According to a preferred embodiment, the multi-type energy storage resource operation constraints include: the method comprises the following steps of operation constraint of a cogeneration unit, pipeline and water temperature constraint of a heating node of a thermodynamic system, actual photovoltaic power generation power and microgrid power constraint and energy storage system operation constraint.
According to a preferred embodiment, before solving the microgrid shared energy storage planning model and the system operation optimization model, a large M method is adopted to introduce auxiliary variables to linearize nonlinear constraint conditions.
According to a preferred embodiment, the expression for calculating the revenue allocation result of each operator by using the shapey value method is as follows:
in the above formula, phi i (r) represents net income distributed by an operation subject I, r(s) represents a characteristic value of the alliance, the characteristic value represents the minimum net income which can be obtained when s and I-s are in game under any alliance of the I-s, if the net income is negative, the net income actually represents cost, s represents the alliance, I represents an operation subject set, s \ I represents a set after the operation subject I is deleted from the alliance s, | s | represents the number of members contained in the alliance s, n represents a set of all the operation subjects in the shared energy storage planning model, and n = | I |.
According to a preferred embodiment, the net profit for each operator is defined as follows:
the net income of the microgrid operator is the cost of the cogeneration unit subtracted from the electricity selling result;
the net profit of the load user is a negative value of the sum of the electricity purchasing cost and the heat consumption cost, wherein the heat consumption cost is a fixed value;
the net benefit of the thermodynamic system is that the heat cost of a load user is subtracted by the heating cost of the cogeneration unit, wherein the net benefit is 0 when the thermodynamic system meets the load demand and operates in a mode of maximizing the net benefit;
the net gain of a battery is the negative of the sum of its investment cost and the cost of operating and maintaining.
According to a preferred embodiment, the microgrid operation model without energy storage and with energy storage built by part of operation subjects alone and energy storage built by operation subjects together in different alliance modes comprises:
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
the cogeneration unit and the thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator does not put in a battery;
the cogeneration unit and the thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user does not put in service a battery, and a microgrid operator puts in service a battery;
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator share a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator does not put in a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user does not put in service a battery, and a microgrid operator puts in service a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator build a battery together.
The invention also provides a microgrid shared energy storage planning and income distribution system, which is applied to the method and comprises the following steps:
the model construction model is used for calculating the operation constraints of various types of energy storage resources, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model for calculating the cloud energy storage service of the thermodynamic system;
the first processing module is used for solving the microgrid shared energy storage planning model and the system operation optimization model to obtain an optimal energy storage planning scheme and a system optimization operation scheme of the microgrid;
the second processing module is used for integrating the optimal energy storage planning scheme and the system optimization operation scheme of the microgrid, establishing microgrid operation models which have no energy storage and are used for separately putting part of operation bodies into energy storage and jointly building energy storage by the operation bodies in different alliance modes and solving the microgrid operation models to obtain the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation body of each alliance;
and the calculation module is used for calculating the income distribution result of each operation subject by adopting a shape value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance.
The invention further provides a computer-readable storage medium, on which a computer program is stored, and when executed by a processor, the computer program implements the microgrid shared energy storage planning and revenue allocation method.
The technical scheme of the embodiment of the invention at least has the following advantages and beneficial effects: the method provided by the invention takes the thermodynamic system as a provider of cloud energy storage service of the power system, can fully explore the equivalent electricity energy storage capacity of the thermodynamic system, and simultaneously cooperates with conventional electricity energy storage, thereby maximizing the energy storage utility, reducing the energy storage use cost and promoting the local consumption of distributed renewable energy of the microgrid; the method provided by the invention provides a profit allocation scheme among all benefit bodies in the alliance when the microgrid operator, the load user, the thermodynamic system and the battery are in alliance, so that the fairness and the rationality of the cloud energy storage mode are ensured, and the provided multi-energy cloud energy storage mode can stimulate all benefit bodies of the microgrid to participate.
Drawings
Fig. 1 is a schematic flowchart of a microgrid shared energy storage planning and revenue allocation method according to embodiment 1 of the present invention;
fig. 2 is a schematic structural diagram of a microgrid in which a thermodynamic system participates in equivalent electrical energy storage and a battery is built for energy storage according to embodiment 1 of the present invention.
Fig. 3 is a schematic diagram of a photovoltaic power generation output curve adopted in embodiment 1 of the present invention.
Fig. 4 is a schematic diagram of a load curve of an electrical power consumer used in embodiment 1 of the present invention.
Fig. 5 is a schematic diagram of a time-of-use electricity price curve of an adopted power grid for selling electricity provided in embodiment 1 of the present invention.
Fig. 6 is a schematic diagram of a thermodynamic curve of a cogeneration unit provided in embodiment 1 of the present invention.
Fig. 7 is a schematic view of a light dump curve of the microgrid provided in embodiment 1 of the present invention.
Fig. 8 is a schematic diagram of a variation curve of the stored electricity amount of the electric power energy storage device in the standard mode according to embodiment 1 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
Referring to fig. 2, the thermodynamic system participates in equivalent energy storage and a microgrid structure for battery energy storage is built; it should be noted that the premise of providing the cloud energy storage service by the thermodynamic system is that the original operating condition, that is, the normal heat of the heat supply load, is not affected, so that the thermodynamic system needs to meet the heat supply load first, and then provides the equivalent energy storage service for the new energy power station. In this embodiment, a photovoltaic electric field is used for distributed renewable energy power generation in the microgrid for specific description.
As shown in fig. 2, the microgrid operator reasonably regulates and controls the thermodynamic system, so that the equivalent energy storage utility of the thermodynamic system is exerted, the electric quantity of the abandoned renewable energy of the photovoltaic electric field is stored, the stored electric energy is sold on the internet in a proper time period, and the consumption of the renewable energy and the system income are increased. Meanwhile, the thermodynamic system shares equivalent energy storage for the microgrid operator, so that the energy storage configuration and use cost of the microgrid operator can be reduced.
Referring to fig. 1, fig. 1 is a schematic flow chart of a microgrid shared energy storage planning and revenue allocation method according to an embodiment of the present invention.
The micro-grid shared energy storage planning and profit allocation method provided by the embodiment of the invention comprises the following steps:
1) The method comprises the steps of considering multi-type energy storage resource operation constraints, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model considering cloud energy storage service of a thermodynamic system, wherein the structure of the microgrid system considered in the embodiment 1 is shown in fig. 2; further, the microgrid shared energy storage planning model and the system operation optimization model are solved to obtain an optimal microgrid energy storage planning scheme and an optimal microgrid system operation scheme, so that investment and operation economy of the microgrid energy system are optimized. In one implementation of this embodiment, the solution described above is performed using the commercial optimization software IBM ILOG CPLEX.
1.1 In an implementation manner of this embodiment, the objective function of the microgrid shared energy storage planning model is as follows:
in the above formula, C represents the total cost of the microgrid, C e Representing the total electricity cost of the microgrid, C b Representing investment costs and operation of microgrid configured batteriesLine cost, C DHS Represents the total heat cost of the microgrid, T represents the set of all periods of a typical day, v g,t Grid electricity price, P, representing time period t l,t Representing the load user power at time t, P pv,t Representing the actual photovoltaic power generation power, P, after light rejection for a time period t bg,t Battery discharge power, P, representing time period t bc,t Battery charging power, k, representing time period t CHP Representing the price of fuel, alpha, of a cogeneration unit p Representing the amount of fuel consumed by the cogeneration unit per unit of electric power, p CHP,t Cogeneration unit generated power, f, representing time period t b,cap Represents the cost per energy storage capacity of the configured battery, E b Denotes the capacity, P, of the microgrid-configured cell b Power of a micro-grid configuration battery is shown, r represents a discount rate, T b Indicates the service life of the battery, v b Represents the operating cost per unit time and power of the battery, alpha DHS Represents the amount of fuel consumed by the cogeneration unit per unit of thermal power, H CHP,t The heating value of the cogeneration unit for the time period t is represented.
The multi-type energy storage resource operation constraint comprises: the method comprises the following steps of operation constraint of a cogeneration unit, pipeline and water temperature constraint of a heating node of a thermodynamic system, actual photovoltaic power generation power and microgrid power constraint and energy storage system operation constraint.
1.2 Specifically, the cogeneration unit operating constraints are:
in the above formula, P CHP,t Represents the electric power of the cogeneration unit during a time period t, H CHP,t Represents the thermal power of the cogeneration unit in a time period t, r CHP The expression of the coefficient, alpha, reflecting the correlation between the electrical power and the thermal power of the cogeneration unit CHP,p Indicating the fuel consumption, alpha, per unit of electric power of the cogeneration unit CHP,DHS The fuel consumption of the unit thermal power of the cogeneration unit,F CHP represents the lower limit of the fuel consumption of the cogeneration unit,
represents the upper limit of the fuel consumption of the cogeneration unit,H CHP represents the lower limit of the heat power of the cogeneration unit and is greater or less>
Represents the upper limit of the thermal power of the cogeneration unit,P CHP represents the lower limit of the electric power of the cogeneration unit and is combined with the device>
Represents the upper limit of the electric power of the cogeneration unit.
1.3 In particular, the pipelines and water temperature constraints of the heating nodes of the thermodynamic system are:
in the above equation, K represents the kth heating network node, where 0 represents the heat source node, K represents the set of all heating network nodes,
indicates the water supply temperature in time period t of the node k>
Represents the return water temperature of the node k in the time period t>
Represents the equivalent thermal insulation coefficient, tau, of the heat supply pipeline at the node k k Heat supply pipe transmission delay representing node k, c amd Denotes the ambient temperature, m k Representing the mass flow through the heating pipeline at node k, c w Represents the specific heat capacity of the water>
Representing the thermal load of node k during time period t,c sup represents the lower limit of the water temperature of the supplied water and is combined with the water tank>
The upper limit of the temperature of the supplied water is shown,c ret indicates the lower limit of the backwater water temperature and is combined with the water tank>
And the upper limit of the temperature of the backwater water is shown.
1.4 Actual photovoltaic power generation power and microgrid power constraints are:
in the above formula, p pv0,t The photovoltaic power generation power at the time of the light rejection is not considered for the time period t.
1.5 In particular, the energy storage system operating constraints are:
in the above formula, u bc,t The state variable indicates whether the energy storage system works in a charging state, the charging working condition time value is 1, and the other working condition states are 0; u. u bg,t The state variable indicates whether the energy storage system works in a discharge state, the discharge working condition time value is 1, and the other working condition states are 0;P bc representing the lower limit of the energy storage charging power;
representing an upper energy storage charging power limit; s. the b,t Representing the stored electric energy of the stored energy in a time period t; eta bc Representing the charging efficiency of the energy storage system; eta bg Indicating the discharge efficiency of the energy storage system;S b representing the lower limit of the stored electric quantity of the energy storage system; />
Representing the upper limit of the storage capacity of the energy storage system; s. the b,start Representing the amount of electricity stored by the energy storage system during the optimization initial period; s b,end Indicating that the amount of power stored by the energy storage system is optimized in the last period of time.
1.6 Introducing auxiliary variables by a large M method to carry out linearization treatment on the nonlinear constraint condition in the constraint condition 1.5), wherein the obtained expression after the linearization treatment is as follows:
in the above formula, M is a sufficiently large normal number.
2) And the optimal energy storage planning scheme and the system optimization operation scheme of the micro-grid are integrated, a micro-grid operation model without energy storage and with energy storage built by part of operation main bodies independently and energy storage built by the operation main bodies together in different alliance modes is established and solved, and the optimal energy storage planning scheme and the system optimization operation scheme of each alliance and the income calculation result of each operation main body are obtained. The method comprises the following specific steps:
2.1 I = { p, l, DHS, b }, where p represents a microgrid provider, l represents a load user, DHS represents a thermal system, and b represents a battery. For any federation
And defining the characteristic value r (S) of the alliance S as the smallest possible net income obtained when S and I-S are played under any alliance of I-S, wherein if the net income is negative, the net income actually represents the cost, and the characteristic value of the alliance is simply called the alliance value.
Further, it should be noted that the net profit of each operator is defined as follows: the net income of the microgrid operator is the cost of the cogeneration unit subtracted from the electricity selling result; the net profit of the load user is a negative value of the sum of the electricity purchasing cost and the heat consumption cost, wherein the heat consumption cost is a fixed value; the net benefit of the thermodynamic system is that the heat cost of a load user is subtracted by the heating cost of the cogeneration unit, wherein the net benefit is 0 when the thermodynamic system meets the load demand and operates in a mode of maximizing the net benefit; the net gain of a battery is the negative of the sum of its investment cost and the cost of operating and maintaining.
2.2 In the embodiment of the present invention, consideration is given to alliance income of microgrid operators, load users, thermodynamic systems and battery energy storage bodies with CHP units and photovoltaic power generation capabilities in different alliance modes, including:
single member alliance: r (p), r (l), r (DHS), r (b);
two-member alliance:
r({p,l}),r({p,DHS}),r({p,b}),r({l,DHS}),r({l,b}),r({DHS,b})；
three-member alliance: r ({ p, l, DHS }), r ({ p, l, b }), r ({ l, DHS, b });
four-member alliance: r ({ p, l, DHS, b });
the microgrid operation model without energy storage, with part of operation bodies independently put in energy storage and the operation bodies jointly put in energy storage under different alliance modes comprises the following eight modes:
mode 1: a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
mode 2: a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator puts in a battery;
mode 3: the cogeneration unit and the thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user does not put in service a battery, and a microgrid operator puts in service a battery;
mode 4: a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator share a battery;
mode 5: a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
mode 6: a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator does not put in a battery;
mode 7: a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user does not put in a battery, and a microgrid operator puts in a battery;
mode 8: a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator build a battery together.
2.3 And when calculating the union value of each union, the mode corresponding relation is as follows:
TABLE 1 mode correspondence
Taking the calculation of r (p) as an example, when S = { p }, I-S = { l, DHS, b }, where S will obtain the minimum net benefit when I-S joins { l, DHS, b }, so the calculation of r (S) should correspond to the minimum net benefit mode, i.e., mode 2.
2.4 In different modes), the income calculation mode of each operation subject is as follows:
both modes 1 to 4 were optimized in two steps:
the first step is optimization for optimizing heat supply of the cogeneration unit, because the cogeneration unit and the thermodynamic system do not participate in equivalent electricity energy storage, the objective function is the lowest heat supply cost of the thermodynamic system;
the second optimization is as follows:
for mode 1: optimizing two steps, namely optimizing the power output and photovoltaic light abandoning of a cogeneration unit of a microgrid operator by taking the income maximization of the microgrid operator as a target; and optimizing the capacity power configuration and the operation setting of the battery by taking the maximum battery benefit as a target.
For mode 2: a double-layer optimization, wherein the price of electricity sold by a microgrid operator to a load user is in real time proportional to the price of electricity of the power grid, and the price proportion of electricity is optimized by taking the maximum income of the microgrid operator as an outer layer;
the inner layer is optimized in two steps, wherein in the first step, the battery capacity, the power configuration and the operation setting are optimized by taking the load users and the battery profit as well as the maximization as targets; and the second step of optimization is that the maximum income of a microgrid operator is the target cogeneration unit power generation and photovoltaic light abandonment.
For mode 3: the method aims at the maximization of the income of a microgrid operator and a battery, and optimizes the power output of a cogeneration unit, photovoltaic light abandonment, battery capacity, power configuration and operation setting.
For mode 4: the method aims at maximizing the sum of benefits of a microgrid operator, a load user and a battery, and optimizes the electricity output, photovoltaic light abandonment, battery capacity, power configuration and operation setting of the cogeneration unit.
The optimization of modes 5 to 8 is as follows:
for mode 5: optimizing two steps, namely optimizing power output, heat output and photovoltaic light abandon of a cogeneration unit by taking the maximization of the sum of benefits of a microgrid operator and a thermodynamic system as a target; optimizing the configuration of battery capacity and power and operation setting by taking the maximization of battery profit as a target;
for mode 6: a double-layer optimization, wherein the condition that the selling price of the electricity to the load users by the microgrid operator is proportional to the electricity price of the power grid in real time is assumed, and the setting of the electricity price proportion is optimized by the outer layer aiming at maximizing the sum of the income of the microgrid operator and the thermodynamic system; the inner layer is optimized in two steps, wherein in the first step, the battery capacity, the power configuration and the operation setting are optimized by taking the load users and the battery profit as well as the maximization as targets; and the second step of optimization is that the maximum sum of the benefits of the microgrid operator and the thermodynamic system is the electricity output, heat output and photovoltaic light abandon of the target cogeneration unit.
For mode 7: the method aims at maximizing the income sum of a microgrid operator, a thermodynamic system and a battery, and optimizes the power output, the heat output and the photovoltaic light abandonment of the cogeneration unit, the capacity power configuration and the operation setting of the battery.
For mode 8: the method is characterized in that the electricity output, the heat output and the photovoltaic light abandon of a cogeneration unit, and the capacity power configuration and the operation setting of a battery are optimized by taking the maximization of the income sum of a microgrid operator, a load user, a thermodynamic system and the battery as a target.
Each mode respectively corresponds to the operation constraint of a cogeneration unit, the pipeline and water temperature constraint of a heating node of a thermodynamic system, the actual photovoltaic power generation power and microgrid power constraint and the operation constraint of an energy storage system. In addition, only the microgrid operator puts in service the battery in the modes 3 and 7, so that a constraint needs to be added to the battery, and the microgrid operator is prohibited from buying electricity from the power grid and storing the electricity into the battery, wherein the constraint is as follows:
P b`,t ＝P CHP,t +P pv,t -P l,t
2.5 In modes 2 and 6), the two-layer optimization solution is as follows:
because the outer layer independent variable is only a single variable, a simple fixed step traversal method is used, so that the independent variable traverses from 0 to 1 according to a fixed step, and a relatively ideal result can be obtained.
3) Calculating the income distribution result of each operation subject by using a shape value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance, wherein the expression is as follows:
in the above formula, phi i (r) represents net income distributed by an operation subject I, r(s) represents a characteristic value of the alliance, the characteristic value represents the minimum net income which can be obtained when s and I-s are in game under any alliance of the I-s, if the net income is negative, the net income actually represents cost, s represents the alliance, I represents an operation subject set, s \ I represents a set after the operation subject I is deleted from the alliance s, | s | represents the number of members contained in the alliance s, n represents a set of all the operation subjects in the shared energy storage planning model, and n = | I |.
In conclusion, the method provided by the invention takes the thermodynamic system as a power system cloud energy storage service provider, can fully explore the equivalent electricity energy storage capacity of the thermodynamic system, and simultaneously cooperates with the conventional electricity energy storage, thereby maximizing the energy storage effect, reducing the energy storage use cost and promoting the on-site consumption of the distributed renewable energy of the microgrid.
The above scheme is exemplified below:
the embodiment adopts a rolling optimization scheme, namely, the data of two days is optimized at one time.
The original photovoltaic power generation data when photovoltaic curtailment is not considered in the embodiment is shown in fig. 3, the load data in the embodiment is shown in fig. 4, and the grid electricity price in the embodiment is shown in fig. 5.
In an implementation manner of this embodiment, the maximum heating power of the cogeneration unit is 5MW, the maximum power supply is 4.5MW, the unit power cost of the battery energy storage is 160 ten thousand yuan/MW, and the unit capacity cost is 120 ten thousand yuan/MWh. In addition, the operation and maintenance cost is 10 yuan/MW/h, the charging and discharging efficiency is 95%, the operation life is 10 years, and the discount rate is 8%.
In this embodiment, the established model is solved by IBM ILOG CPLEX 12.10.0 commercial optimization software, and the battery planning result and the light and electricity abandoning and heat cost result obtained by the solution are shown in the following table:
table 2. Energy storage planning scheme and photovoltaic light abandoning and electricity using heat cost result in mode 8
The output result of the optimized cogeneration unit is shown in fig. 6, the photovoltaic waste light is shown in fig. 7, and the battery charge and discharge is shown in fig. 8.
The energy storage configuration and operation optimization results obtained by solving the modes 1 to 7 are as follows:
TABLE 3 energy storage configuration and operation optimization results
Therefore, energy storage facilities are additionally built in the microgrid, and a thermodynamic system is incorporated to serve as equivalent electric energy storage, so that the photovoltaic light abandonment amount is reduced, and the heat net cost for electricity utilization of the microgrid is reduced.
In addition, according to the operation optimization result calculated in each mode, the union value shown in the following table can be obtained:
TABLE 4. Alliance values
Further, the Shapley value method is adopted to calculate the benefit distribution result of each operator, and the calculation results are as follows:
TABLE 5 benefit Allocation for each operator
According to the benefit allocation amounts of the operation bodies shown in the table, the final load user expenditure amount is 192878 yuan, the microgrid operator should obtain the net income 104204 yuan, the thermal system should obtain the net income 620 yuan, and the battery should obtain the net income 28347 yuan.
In conclusion, the method provided by the invention provides a profit allocation scheme among benefit agents in the alliance when the microgrid operator, the load user, the thermal system and the battery alliance are adopted, so that the fairness and the rationality of the cloud energy storage mode are ensured, and the provided multi-energy cloud energy storage mode can stimulate the benefit agents of the microgrid to participate.
Example 2
Different from embodiment 1, an embodiment of the present invention provides a microgrid shared energy storage planning and revenue allocation system, which is applied to the method according to embodiment 1, and includes:
the model construction model is used for calculating the operation constraints of various types of energy storage resources, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model for calculating the cloud energy storage service of the thermodynamic system;
the first processing module is used for solving the microgrid shared energy storage planning model and the system operation optimization model to obtain an optimal microgrid energy storage planning scheme and a system operation optimization scheme;
the second processing module is used for integrating the optimal energy storage planning scheme and the system optimization operation scheme of the microgrid, establishing microgrid operation models which have no energy storage and are used for separately putting part of operation bodies into energy storage and jointly building energy storage by the operation bodies in different alliance modes and solving the microgrid operation models to obtain the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation body of each alliance;
and the calculation module is used for calculating the income distribution result of each operation subject by adopting a shapey value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance.
Example 3
In distinction from the foregoing embodiments, an embodiment of the present invention provides a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the method for planning and allocating energy shared by micro grid and revenue is implemented as in embodiment 1.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The microgrid shared energy storage planning and revenue allocation method is characterized by comprising the following steps of:
considering multi-type energy storage resource operation constraints, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model considering the cloud energy storage service of the thermodynamic system;
solving the microgrid shared energy storage planning model and the system operation optimization model to obtain an optimal microgrid energy storage planning scheme and a system operation optimization scheme;
integrating the optimal energy storage planning scheme and the system optimization operation scheme of the microgrid, establishing microgrid operation models which have no energy storage and are used for separately putting part of operation subjects into energy storage and jointly building energy storage by the operation subjects in different alliance modes and solving the microgrid operation models to obtain the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance;
and calculating the income distribution result of each operation subject by adopting a shape value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance.
2. The microgrid shared energy storage planning and revenue distribution method of claim 1, wherein the operation bodies comprise microgrid operators, load users, thermal systems and battery energy storage.
3. The microgrid shared energy storage planning and revenue allocation method of claim 2, wherein an objective function of the microgrid shared energy storage planning model is as follows:
in the above formula, C represents the total cost of the microgrid, C e Representing the total electricity cost, C, of the microgrid b Represents the investment cost and the operation cost of the micro-grid configuration battery, C DHS Represents the total heat cost of the microgrid, T represents the set of all periods of a typical day, v g,t Grid electricity price, P, representing time period t l，t Representing the load user power at time t, P pv，t Actual photovoltaic power generation power after light rejection, P, taken into account, representing time period t bg，t Battery discharge power, P, representing time period t bc,t Battery charging power, k, representing time period t CHP Representing the price of fuel, alpha, of a cogeneration unit p Represents the amount of fuel consumed by the cogeneration unit per unit of electric power, P CHP,t Cogeneration unit generated power, f, representing time period t b,cap Representing the cost per unit energy storage capacity of the battery, E b Representing the capacity, P, of a microgrid-configured battery b Power of a micro-grid configuration battery is shown, r represents a discount rate, T b Indicates the service life of the battery, v b Represents the operating cost per unit time and power of the battery, alpha DHS Represents the amount of fuel consumed by the cogeneration unit per unit thermal power, H CHP，t The heating value of the cogeneration unit for the time period t is represented.
4. The microgrid shared energy storage planning and revenue allocation method of any one of claims 1 to 3, wherein the multi-type energy storage resource operation constraints comprise: the method comprises the following steps of heat and power cogeneration unit operation constraint, pipeline and water temperature constraint of a heating node of a thermodynamic system, actual photovoltaic power generation power and microgrid power constraint and energy storage system operation constraint.
5. The microgrid shared energy storage planning and revenue allocation method of claim 1, further comprising introducing auxiliary variables by using a large M method to linearize nonlinear constraint conditions before solving the microgrid shared energy storage planning model and the system operation optimization model.
6. The microgrid shared energy storage planning and revenue distribution method according to claim 1, wherein the expression for calculating revenue distribution results of each operation subject by using a shapey value method is as follows:
in the above formula, phi i (r) represents net income allocated by an operating subject I, r(s) represents a characteristic value of the alliance, the characteristic value represents the minimum net income which can be obtained when s and I-s are in game under any alliance of the I-s, if the net income is negative, the net income actually represents cost, s represents the alliance, I represents a set of the operating subjects, s { I } represents a set after the operating subject I is deleted from the alliance s, | s | represents the number of members contained in the alliance s, n represents a set of all the operating subjects in the shared energy storage planning model, and n = | I | represents
7. The microgrid shared energy storage planning and revenue allocation method of claim 6, wherein the net revenue of each operator is defined as follows:
the net income of the microgrid operator is the cost of the cogeneration unit subtracted from the electricity selling result;
the net profit of the load user is a negative value of the sum of the electricity purchasing cost and the heat consumption cost, wherein the heat consumption cost is a fixed value;
the net benefit of the thermodynamic system is that the heat cost of a load user is subtracted by the heating cost of the cogeneration unit, wherein the net benefit is 0 when the thermodynamic system meets the load demand and operates in a mode of maximizing the net benefit;
the net gain of a battery is the negative of the sum of its investment cost and its operational maintenance cost.
8. The microgrid shared energy storage planning and revenue allocation method of claim 2, wherein the microgrid operation model of no energy storage, independent energy storage of part of operation bodies and co-established energy storage of operation bodies in different alliance modes comprises:
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator puts in a battery;
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, a load user does not put in service a battery, and a microgrid operator puts in service a battery;
a cogeneration unit and a thermodynamic system in the microgrid do not participate in equivalent electricity energy storage, and a load user and a microgrid operator share a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator do not build a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user puts in a battery, and a microgrid operator does not put in a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, a load user does not put in service a battery, and a microgrid operator puts in service a battery;
a cogeneration unit and a thermodynamic system in the microgrid participate in equivalent electricity energy storage, and a load user and a microgrid operator build a battery together.
9. A microgrid shared energy storage planning and revenue distribution system, applied to the method of any one of claims 1 to 8, comprising:
the model construction model is used for calculating the operation constraints of various types of energy storage resources, taking the minimized microgrid operation cost as a target function, and establishing a microgrid shared energy storage planning model and a system operation optimization model for calculating the cloud energy storage service of the thermodynamic system;
the first processing module is used for solving the microgrid shared energy storage planning model and the system operation optimization model to obtain an optimal microgrid energy storage planning scheme and a system operation optimization scheme;
the second processing module is used for integrating the optimal energy storage planning scheme and the system optimization operation scheme of the microgrid, establishing a microgrid operation model which has no energy storage and is formed by separately putting part of operation bodies into energy storage and jointly building energy storage by the operation bodies in different alliance modes and solving the microgrid operation model to obtain the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation body of each alliance;
and the calculation module is used for calculating the income distribution result of each operation subject by adopting a shape value method based on the optimal energy storage planning scheme, the system optimization operation scheme and the income calculation result of each operation subject of each alliance.
10. A computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the microgrid shared energy storage planning and revenue distribution method according to any one of claims 1 to 8.
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