CN115509134A - Building group distributed optimal scheduling method considering building characteristics and electric energy transaction - Google Patents
Building group distributed optimal scheduling method considering building characteristics and electric energy transaction Download PDFInfo
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Abstract
The invention provides a building group distributed optimal scheduling method considering building characteristics and electric energy transaction, which comprises the following steps: building various buildings are subjected to fine modeling, and under the condition that building enclosing structures, functional characteristics, personnel behaviors and distributed energy power generation are considered, different building multi-target operation optimization models with the requirements on economy and comfort as targets are established; on the overall coordination and sharing level among different buildings, a novel energy trading platform and a trading strategy are made according to building grade division and distributed energy generating capacity; and feeding back the transaction electricity price after the transaction between the buildings to each building for a new round of self-optimization, and finally completing the energy coordination and sharing between various buildings through the repeated iteration of the self-optimization of the buildings and the quotation of the transaction platform.
Description
Technical Field
The invention belongs to the technical field of comprehensive energy of power systems, and particularly relates to a building group distributed optimal scheduling method considering building characteristics and electric energy transaction.
Background
Different from the real-time matching of the electric energy supply and consumption processes, the indoor temperature change of the building of the enclosure type building has the thermal delay characteristic, the building can be regarded as thermal virtual energy storage equipment due to the characteristic of the building, the thermal energy storage characteristic can realize the translation of the heat energy demand on a time scale, and the peak clipping and valley filling and the reduction of the system operation cost are greatly facilitated. Because the output of the new energy equipment has certain fluctuation, if the distributed energy is directly connected with the main network, the accuracy of the generated power prediction data of the distributed energy directly influences the stable operation of the main network system. The distributed energy is combined with the building, the building can adjust the operation of the building according to the power generation condition of the distributed energy, and the influence of the fluctuation on the main network system is greatly reduced.
After the building virtual energy storage characteristics are considered, a building energy consumption model is further refined, an applicable energy trading platform is constructed, and the method has important significance for fully exploiting the potential of building construction in peak clipping and valley filling and renewable energy consumption under the background of the rapid development of new energy and the multi-energy complementary energy Internet.
Disclosure of Invention
The invention aims to solve the problem that the existing research on electric energy transaction and sharing between micro grids cannot reflect the operating characteristics of buildings in the micro grids, and most buildings do not have the capacity of independently participating in market competition among the micro grids due to small transaction amount compared with the micro grids with larger market foundation and larger transaction amount, so that the method takes the buildings as the main body, provides a building group distributed optimal scheduling method considering the building characteristics and electric energy transaction, realizes the building self optimization process through the method, constructs a point-to-point energy transaction platform and strategy on the basis of the building self optimization process, further promotes the complementation and interaction of resources among the buildings, achieves the nearby balance of supply and demand power, and improves the consumption capacity of the system on new energy.
In order to achieve the purpose, the invention adopts the following technical scheme:
a building group distributed optimal scheduling method considering building characteristics and electric energy transaction,
(1) Classifying the buildings according to different use functions of the buildings, and establishing corresponding thermodynamic models;
(2) Establishing a corresponding building operation optimization model with economy and comfort as targets according to the functional characteristics and personnel characteristics of the building;
(3) Constructing a corresponding energy trading platform and a corresponding trading strategy according to the operation optimization result of each building, the electric power market characteristics formed among the buildings and the power generation condition of the distributed energy;
(4) And returning the result after the inter-building transaction to the operation optimization model of the building per se for iteration so as to achieve the optimal optimization.
The step (1) of classifying the buildings is to divide the buildings into residential buildings, commercial buildings and special buildings according to different functions, and the thermodynamic model is as follows:
in the formula: h SUN Representing the heat transferred by solar thermal radiation, I SUN The thermal radiation power of the sun represents the heat received by an object per square meter in unit time when the light is vertically irradiated; f win The sum of the area of the building external window; SC is the shading coefficient of the outer window, and the value of the value is related to whether the outer window has a shading board or not and the glass material of the outer window; h rand Represents the heating power of an indoor heat source, mainly the heating power of a human body and electric equipment, N peo Means the sum of the number of persons in the room at that moment, Q peo The heat dissipation is uniform for all people; p is equi Means the total power value of all indoor equipment, epsilon e The heat dissipation proportion of the equipment; h HVAC Represents the cooling/heating power of the air conditioning system, in the method summer cooling is taken as an example, and is therefore represented in the formula as cooling power; k wall The heat transfer coefficient of heat transferred between the outer wall of a building and the outside is expressed, and the heat transferred per second when the difference between indoor temperature and outdoor temperature is 1 ℃ in steady heat transfer is meant; k win The heat transfer coefficient of the external window of the building is similar to the heat transfer coefficient of the external wall of the building; f wall And F win Respectively the external wall area and the external window area of the building; t is room Indicating the room temperature, T out Indicating the outdoor temperature, T room.t Indicating the indoor temperature, T, of the current time period room.t+1 Represents the indoor temperature of the next period; ρ is a unit of a gradient air Is the air density in the room, C air Is the specific heat capacity of air, V room Is the indoor air capacity.
After the difference of the building thermodynamic models and the difference of personnel characteristics caused by building classification are considered at the same time, building operation optimization models of the building operation optimization models are built by taking economy and comfort as targets, unified optimization targets and standards are built among different buildings, and specific objective functions are as follows:
in the above formula C Trade.t The economic cost of system operation is represented, and the economic cost means the electricity trading cost of the building, the power distribution network and other buildings; c Ma.t Representing the use and maintenance cost of each device, and mainly considering the maintenance cost of the HVAC system and the photovoltaic power generation system in the method; c Tem.t Penalty costs for affecting user temperature comfort; c Net.b And C Net.s Respectively representing the price of electricity sold and purchased in the current time period of the distribution network, P Net.b And P Net.s Respectively representing the electricity purchasing and electricity selling electric quantity of the building at the power distribution network in the current time period, wherein only one electricity purchasing state and one electricity selling state exist in the same time period; delta. For the preparation of a coating HVAC 、δ pv Respectively representing the use and maintenance cost of unit power in unit time period of the HVAC system and the photovoltaic power generation system; p HVAC 、P pv Representing the power of the HVAC system and the photovoltaic power generation system, respectively; gamma is a temperature penalty factor, which can be regarded as the sensitivity of the user to the temperature comfort, T set For a set indoor optimum temperature, the greater the deviation from the set temperature, the greater the temperature penalty cost.
The transaction strategy in the step (3) is as follows: in the trading process, each building mainly provides 3 types of information, namely trading quotation, trading electric quantity and photovoltaic power generation amount, a trading market compares and judges according to the quotation information of each building, when the highest quotation of a power purchasing party is higher than the lowest quotation of a power selling party, a trading condition is met, if the same quotation occurs in the building at the moment, the building is distinguished according to the building grade, and the building with high priority grade is traded preferentially; the building prioritization mainly refers to the following two factors: according to building function division, a special building with important load is divided into the highest level and a residential building is divided into the lowest level; aiming at both parties purchasing and selling electricity, if the proportion of the electricity to be traded of the building to the total electricity to be traded is higher, the building purchases and sells electricity more stably, the priority level is higher, and if the proportions of the electricity to be traded of the building to the total electricity to be traded are the same, the building level with large electricity generation amount of distributed energy resources is determined to be higher; and after the two parties meet the trading conditions, the trading price is the average value of the quoted prices of the two parties, the trading electric quantity is the party with less electric quantity to be traded, the self information to be traded is respectively updated after the trading is completed, and the next round of trading is carried out.
And (4) returning the result after the inter-building transaction to the operation optimization model of the building to perform iteration so as to achieve the optimal optimization, wherein the specific process is as follows:
after the transaction center finishes the transaction, each building obtains the final electricity transaction cost and the operation cost of the system of the building
C Trade.t ＝C Net.b P Net.b -C Net.s P Net.s
In the formula, C Net.b And C Net.s Respectively representing the electricity selling and purchasing prices, P, of the current time slot of the trading market Net.b And P Net.s Respectively representing the electricity purchasing and electricity selling quantities of the building in the current time period in the power distribution network, iterating the traded electricity quantity trading price back to the building operation optimization for re-optimization and subsequent market trading, judging that iteration is completed if the iteration frequency reaches a set maximum value K or the total operation cost difference of the system after two iterations is less than 5%, and enabling the building to reach the optimal solution of considering the building virtual energy storage characteristic and the P2P transaction operation optimization.
The beneficial effects obtained by the invention are as follows:
the method provides a building group distributed optimization scheduling model considering building characteristics and electric energy transaction, establishes a building operation optimization model taking economy and comfort as targets, and stimulates and guides each building to realize building group energy sharing under various uncertain conditions through a novel continuous auction transaction mechanism considering market relation and transaction risk. The novel continuous auction trading mechanism provided by the method can complete market bidding according to market relations and building operation conditions, each building quoted price is updated in real time according to the whole trading condition, and the novel continuous auction trading mechanism has good stability and dynamic characteristics. The method has the advantages that each building is guided to complete iterative optimization through updating of transaction prices, and the load adjusting capacity of each building is further mined while the economy of the building is improved; energy sharing among buildings is realized, and meanwhile, the energy sharing capability and the distributed energy consumption capability of building groups are improved.
Drawings
Fig. 1 is a block diagram of a building complex system.
Fig. 2 is a diagram of a thermodynamic model of a building.
Fig. 3 is a diagram of the change of the special building optimized operation temperature.
Fig. 4 is a P2P transaction flow.
Fig. 5 is a transaction case of 9-point transaction at the transaction center.
Fig. 6 is a transaction scenario for the transaction center 15.
FIG. 7 is a building iterative optimization flow diagram.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, there will now be described in detail, with reference to the accompanying drawings, a non-limiting detailed description of the present invention. The building group distributed optimal scheduling method considering building characteristics and electric energy transactions comprises the following steps:
1. according to different using functions of the buildings, the buildings are classified, and corresponding thermodynamic models are established.
Building a building group energy management framework:
as shown in fig. 1, considering a building group consisting of a plurality of buildings, each interconnected with a communication network by electric power, the method mainly divides the buildings into three categories of commercial buildings, residential buildings and special buildings. Each building has distributed energy sites including Photovoltaic (PV) and Wind Power (WP), and Photovoltaic Power is mainly considered in the method. Each building is provided with an Energy Management System (EMS) which is responsible for integrating the operation condition in the building and the distributed Energy generation condition and then completing the operation optimization of the building. After the building finishes self operation optimization, information such as trading electric quantity, trading quotation and distributed energy generating capacity is submitted to a point-to-point (P2P) trading market, electric quantity trading with other buildings is finally finished in the P2P trading market, and if surplus or shortage of electric quantity still exists after trading is finished, the building is uniformly traded with a power distribution network.
Building thermodynamic model building:
the following conclusion can be obtained by research and analysis aiming at various buildings in the city, at present, the modern buildings in the city are generally integrated buildings mainly comprising large-area glass curtain walls, any one layer or a plurality of layers in the buildings share the same temperature state, therefore, a thermodynamic model adopts a classical model considering solar radiation heat gain, external window radiation heat radiation, external wall radiation heat radiation and indoor various heat sources heat gain as shown in figure 2, the maximum characteristic of the thermodynamic model is that the whole building is regarded as a whole to well reflect the heat gain and heat radiation conditions of the whole system, different types of buildings have different heat storage characteristics due to different materials and areas of external walls, internal walls, floor slabs, roofs and skylights, and therefore, the structural area F of various buildings is mainly considered in the thermodynamic model wall 、F win And heat transfer coefficient K wall 、K win The difference in (a).
2. And establishing a corresponding building operation optimization model by taking economy and comfort as targets according to the functional characteristics and personnel characteristics of the building.
The method is characterized in that a quantitative mathematical relation among indoor temperature, building thermal load, refrigerating power and external temperature is constructed from the aspect of energy conservation by utilizing the characteristic that building heat conduction has time delay, namely the heat storage characteristic, and based on a thermal balance equation of the building, so that a virtual energy storage system for heat in the building is constructed. Meanwhile, a virtual energy storage system of the building is integrated into a building operation optimization model, and the temperature comfort level and the penalty function thereof are brought into an optimization target, so that the optimization management of building virtual energy storage charging and discharging is realized, and the charging and discharging process is expressed by the indoor temperature change of the building. The operation cost of the building can be reduced to a certain extent by adding the virtual energy storage system of the building.
Meanwhile, after considering the difference of building thermodynamic models and the difference of personnel characteristics caused by building classification, building operation optimization models of the building operation optimization models are constructed by taking economy and comfort as targets, unified optimization targets and standards are established among different buildings, and specific objective functions are as follows:
in the above formula C Trade.t The economic cost of system operation is represented, and the economic cost means the electricity trading cost of the building, the power distribution network and other buildings; c Ma.t Representing the use and maintenance cost of each device, wherein the maintenance cost of an HVAC system and a photovoltaic power generation system is mainly considered in the method; c Tem.t Penalty costs for affecting user temperature comfort; c Net.b And C Net.s Respectively representing the price of electricity sold and purchased in the current time period of the distribution network, P Net.b And P Net.s Respectively representing the electricity purchasing and electricity selling electric quantity of the building at the power distribution network in the current time period, wherein only one electricity purchasing state and one electricity selling state exist in the same time period; delta HVAC 、δ pv Respectively representing the use and maintenance cost of unit power in a unit time period of the HVAC system and the photovoltaic power generation system; p is HVAC 、P pv Representing the power of the HVAC system and the photovoltaic power generation system, respectively; gamma is a temperature penalty factor, which can be regarded as the sensitivity of the user to the temperature comfort, T set For a set indoor optimum temperature, the greater the deviation from the set temperature, the greater the temperature penalty cost.
The operation optimization result of the special building is shown in fig. 3, and the special building mainly has the following differences in operation optimization compared with other two types of buildings: the electrical load and the thermal load in the special building are higher, the number of personnel is more, and the influence caused by uncertainty of the personnel is more serious compared with other two types of buildings; the glass curtain wall of the special building has less area, the virtual energy storage characteristic of the building is better, the heat transmitted by solar heat radiation is less, and the influence caused by the uncertainty of illumination intensity is less; the method is characterized in that the special building has higher temperature range requirement during operation, the building is required to be kept within the temperature requirement range within 24 hours, and the optimal temperature of people in the special building is set to be 24 degrees in the method, so that the building is allowed to float up and down for 2.5 degrees during operation. As can be seen from FIG. 3, the operation of a special building meets the temperature condition and keeps a fluctuation state, and the factors influencing the temperature fluctuation are mainly the power price of the power distribution network and the temperature penalty factor. The characteristics of the building as a virtual energy storage system are greatly different from those of a traditional electricity storage system, and compared with the high charging and discharging frequency of the electricity storage system, the virtual energy storage system of the building is faster in self energy dissipation and is greatly influenced by the outside, such as illumination and outdoor temperature, so that the charging and discharging frequency is lower, and the charging and discharging frequency is changed 7 times in 24 hours as shown in fig. 3. The essential of the operation optimization considering the building virtual energy storage is that the electricity prices at different moments are utilized, the refrigeration is carried out in advance when the electricity prices are low, and the refrigeration electricity consumption is reduced when the electricity prices are high, so that the operation cost is saved.
3. And constructing a corresponding energy trading platform and a trading strategy according to the operation optimization result of each building, the electric power market characteristics formed among the buildings and the power generation condition of the distributed energy.
The P2P trading platform adopts a distributed trading platform as shown in fig. 1, each building is an individual and provides related quotation information to a P2P trading center, and the trading quotation of the building is continuously modified according to market information to finally achieve trading, and the flow of the P2P trading is shown in fig. 4.
In the transaction process, each building mainly provides 3 types of information including transaction quotations, transaction electric quantity and photovoltaic power generation quantity. The trading market compares and judges according to the quotation information of each building, when the highest quotation of the electricity purchasing party is higher than the lowest quotation of the electricity selling party, the trading condition is met, if the same quotation occurs to the building at the moment, the building is distinguished according to the building grade, and the building with the high priority grade is traded preferentially. The building prioritization mainly refers to the following two factors: according to building function division, a special building with important load is divided into the highest level and a residential building is divided into the lowest level; for both electricity purchasing and electricity selling parties, if the proportion of the electricity to be traded of the building to the total electricity to be traded is higher, the electricity purchasing and electricity selling of the building are more stable, the priority level is higher, and if the proportion of the electricity to be traded of the building to the total electricity to be traded is the same, the building with high electricity generation amount of the distributed energy is determined to be higher. And after the two parties meet the trading conditions, the trading price is the average value of the quoted prices of the two parties, the trading electric quantity is the party with less electric quantity to be traded, the self information to be traded is respectively updated after the trading is completed, and the next round of trading is carried out.
When most buildings still have target transaction amount which is not completed by the buildings after the transaction of the buildings is completed or the transaction turns reach the maximum times, closing the transaction platform, and clearing the residual electricity quantity to be transacted for the buildings which complete the transaction target and the power distribution network.
Fig. 5 and fig. 6 are specific transaction situations, fig. 5 is a transaction situation at a 9 o 'clock of the transaction center, fig. 6 is a transaction situation at a 15 o' clock of the transaction center, in the figures, a black line indicates that a blue line of a power selling party indicates a power purchasing party, and when the highest power price of the power purchasing party is higher than the lowest power price of the power selling party, an intersection point occurs between the two lines, which indicates that the transaction is successful. By comparing the difference between the transaction quotation curves of the electricity purchasing and selling parties in fig. 5 and 6, the electricity purchasing and selling parties are in the market at 9 o' clock in the transaction center, and the total electricity selling quantity is greater than the electricity purchasing quantity, so that the electricity purchasing party can purchase electric energy at a lower price after multiple rounds of transaction quotations, but the final electricity selling price of the electricity purchasing party is superior to the clearing price of the power distribution network. The transaction center 15 is a seller market, the total electricity purchasing quantity is greater than the electricity selling quantity, the electricity selling party can sell electricity at a higher price after multiple rounds of transaction quotations, the situation that the price of the electricity selling party first rises and then falls in fig. 6 mainly comes from the combined action of the pessimism coefficient and the risk coefficient, the influence of the pessimism coefficient is greater than that of the risk coefficient along with the transaction, and therefore the price of the electricity selling party is gradually reduced for achieving the final transaction. Through the analysis, the transaction platform and the transaction strategy provided by the method can be verified to be capable of effectively operating.
4. And returning the result after the inter-building transaction to the operation optimization model of the building per se for iteration so as to achieve the optimal optimization.
After the transaction center completes the transaction, each building obtains the final electricity transaction cost and the operation cost of the system of the building, as shown in the following formula, the economic cost of the system operation in the initial building operation optimization is the transaction cost of the building and the power distribution network, and P Net.b Selling electricity for the distribution network, P Net.s The electricity purchasing price of the power distribution network is that electricity purchasing and selling only operate in one state in the same time period.
C Trade.t ＝C Net.b P Net.b -C Net.s P Net.s (2)
The above manner and the system cost after the transaction are completed have a large deviation, so in the method, the electricity transaction price after the transaction is iterated back to the building operation optimization for re-optimization and subsequent market transaction, and a specific flow is shown in fig. 7.
Considering the influence of the calculation time length, if the iteration times reach a set maximum value K or the difference of the total operation cost of the system after two iterations is less than 5%, the iteration is judged to be completed, and at the moment, the building reaches the optimal solution of self operation optimization after the building virtual energy storage characteristic and the P2P transaction are considered.
Claims (5)
1. A building group distributed optimal scheduling method considering building characteristics and electric energy transaction is characterized by comprising the following steps:
(1) Classifying the buildings according to different use functions of the buildings, and establishing corresponding thermodynamic models;
(2) Establishing a corresponding building operation optimization model with economy and comfort as targets according to the functional characteristics and personnel characteristics of the building;
(3) Constructing a corresponding energy trading platform and a trading strategy according to the operation optimization result of each building, the electric power market characteristics formed among the buildings and the power generation condition of the distributed energy;
(4) And returning the result after the inter-building transaction to the operation optimization model of the building for iteration so as to achieve the optimal optimization.
2. The distributed building group optimized dispatching method considering building characteristics and electric energy transaction as claimed in claim 1, wherein the step (1) of classifying the buildings is to classify the buildings into residential buildings, commercial buildings and special buildings according to different functions, and the thermodynamic model is as follows:
in the formula: h SUN Representing the heat transferred by solar thermal radiation, I SUN The thermal radiation power of the sun represents the heat received by an object per square meter in unit time when the light is vertically irradiated; f win The sum of the area of the building external window; SC is the outer window shading coefficient, and the value of the SC is related to whether the outer window has a shading plate or not and the glass material of the outer window; h rand Represents the heating power of an indoor heat source, mainly the heating power of a human body and electric equipment, N peo Means the sum of the number of persons in the room at that moment, Q peo The average heat dissipation of people; p equi Means the total power value of all indoor equipment, epsilon e The heat dissipation proportion of the equipment; h HVAC Represents the cooling/heating power of the air conditioning system, in the method summer cooling is taken as an example, and is therefore represented in the formula as cooling power; k is wall The heat transfer coefficient of heat transferred between the outer wall of a building and the outside is expressed, and the heat transferred per second when the difference between indoor temperature and outdoor temperature is 1 ℃ in steady heat transfer is meant; k win The heat transfer coefficient of the external window of the building is similar to the heat transfer coefficient of the external wall of the building; f wall And F win Respectively the external wall area and the external window area of the building；T room Indicating the room temperature, T out Indicating the outdoor temperature, T room.t Indicating the indoor temperature, T, of the current time period room.t+1 Represents the indoor temperature of the next time period; rho air Is the air density in the room, C air Is the specific heat capacity of air, V room Is the indoor air capacity.
3. The building group distributed optimization scheduling method considering building characteristics and electric energy transactions as claimed in claim 1, wherein in the step (2), after simultaneously considering building thermodynamic model differences and personnel characteristics differences caused by building classification, building operation optimization models of the building are constructed by taking economy and comfort as targets, and unified optimization targets and standards are established among different buildings, and specific target functions are as follows:
in the above formula C Trade.t The economic cost of the system operation is expressed, and the economic cost means the electricity transaction cost of the building, the power distribution network and other buildings; c Ma.t Representing the use and maintenance cost of each device, and mainly considering the maintenance cost of the HVAC system and the photovoltaic power generation system in the method; c Tem.t Penalty cost for affecting user temperature comfort; c Net.b And C Net.s Respectively representing the electricity selling price and the electricity purchasing price of the current time period of the trading market, and the electricity purchasing price of the power distribution network in the initial optimization, P Net.b And P Net.s Respectively representing the electricity purchasing and electricity selling electric quantity of the building in the current time period, wherein only one electricity purchasing state and one electricity selling state exist in the same time period; delta. For the preparation of a coating HVAC 、δ pv Respectively representing the use and maintenance cost of unit power in unit time period of the HVAC system and the photovoltaic power generation system; p HVAC 、P pv Representing the power of the HVAC system and the photovoltaic power generation system, respectively; gamma is a temperature penalty factor, which can be regarded as the sensitivity of the user to temperature comfort, T set For a set indoor optimum temperature, the greater the deviation from the set temperature, the greater the temperature penaltyThe greater the cost.
4. The building group distributed optimal scheduling method considering building characteristics and electric energy trading of claim 1, wherein the trading strategy of step (3) is as follows: in the trading process, each building mainly provides 3 types of information, namely trading quotation, trading electric quantity and photovoltaic power generation amount, a trading market compares and judges according to the quotation information of each building, when the highest quotation of a power purchasing party is higher than the lowest quotation of a power selling party, a trading condition is met, if the same quotation occurs in the building at the moment, the building is distinguished according to the building grade, and the building with high priority grade is traded preferentially; the building prioritization mainly refers to the following two factors: according to the building function division, a special building with important load is divided into the highest level, and a residential building is divided into the lowest level; for both electricity purchasing and electricity selling parties, if the proportion of the electricity to be traded of the building to the total electricity to be traded is higher, the electricity purchasing and electricity selling of the building are more stable, the priority level is higher, and if the proportions of the electricity to be traded of the building to the total electricity to be traded are the same, the building with high electricity generation amount of the distributed energy is determined to be higher for promoting the consumption of the distributed energy; and after the two parties meet the trading conditions, the trading price is the average value of the quoted prices of the two parties, the trading electric quantity is the party with less electric quantity to be traded, the self information to be traded is respectively updated after the trading is completed, and the next round of trading is carried out.
5. The building group distributed optimization scheduling method considering building characteristics and electric energy transactions as claimed in claim 1, wherein the step (4) returns the result after the inter-building transaction to the operation optimization model of the building itself for iteration so as to achieve the optimal optimization comprises the following specific processes:
after the transaction center finishes the transaction, each building obtains the final electricity transaction cost and the operation cost of the system of the building
C Trade.t ＝C Net.b P Net.b -C Net.s P Net.s
In the formula, C Net.b And C Net.s Respectively representing the electricity selling and purchasing prices, P, of the current time slot of the trading market Net.b And P Net.s Respectively representThe electricity purchasing and selling quantities of electricity of the building in the current time period are iterated back to building operation optimization for secondary optimization and subsequent market trading after the traded electricity trading price in the current time period, if the iteration times reach a set maximum value K or the difference between the total operation cost of the system after two iterations is less than 5%, the iteration is judged to be completed, and at the moment, the building reaches the optimal solution of considering building virtual energy storage characteristics and P2P trading and then self operation optimization.
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| CN116128227A (en) * | 2022-12-30 | 2023-05-16 | 天津大学 | Electric energy distribution method and device |
| CN116128227B (en) * | 2022-12-30 | 2024-02-02 | 天津大学 | Electric energy distribution method and device |
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