WO2024093027A1 - Energy distribution method and apparatus - Google Patents

Abstract

Disclosed in the present invention are an energy distribution method and apparatus. The method comprises: performing data division on annual load data of energy prosumers to generate load data of new energy and load data of each prosumer; according to the load data of the new energy and the load data of each producer in combination with a user power consumption demand constraint, calculating individual preference of each prosumer; and establishing a two-stage robust optimization model according to the annual load data of the energy prosumers, performing solving according to the two-stage robust optimization model to obtain a scheduling scheme, and then obtaining an energy distribution result according to the scheduling scheme and the individual preference. Use of embodiments of the present invention can effectively improve energy utilization efficiency.

Description

Energy distribution method and device Technical Field

The present invention relates to the technical field of energy distribution, and in particular to an energy distribution method and device.

Background technique

The existing energy system does not take into account consumers' energy needs and the uncertainty of new energy sources, resulting in the inability to allocate various types of energy according to consumers' energy needs.

From the above, it can be concluded that the existing energy allocation method cannot solve the problem of uncertainty, which makes it impossible to allocate various types of energy according to consumers' energy needs, resulting in low energy utilization efficiency.

Summary of the invention

The embodiments of the present invention provide an energy distribution method and device, which effectively improve energy utilization efficiency.

A first aspect of an embodiment of the present application provides an energy distribution method, comprising:

Divide the annual load data of energy producers and consumers to generate new energy load data and load data of each producer and consumer;

Based on the load data of new energy and each producer and consumer, combined with the user's electricity demand constraints, the individual preferences of each producer and consumer are calculated;

A two-stage robust optimization model is established based on the annual load data of energy producers and consumers. After solving the two-stage robust optimization model to obtain the scheduling plan, the energy allocation result is obtained according to the scheduling plan and individual preferences.

In a possible implementation of the first aspect, a two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

The grid cost, dispatching cost and controllable distributed power cost are calculated based on the annual load data of energy producers and consumers;

A two-stage robust optimization model is established based on the grid cost, dispatching cost and controllable distributed generation cost.

In a possible implementation of the first aspect, the annual load data of energy producers and consumers is divided to generate load data of new energy and load data of each producer and consumer, specifically:

The annual load data of energy producers and consumers are divided into data in the form of energy types to generate load data of new energy sources; the load data of new energy sources are used as the uncertainty set in robust optimization;

The annual load data of energy producers and consumers are divided from the perspective of producers and consumers to generate load data of each producer and consumer; wherein the load data of each producer and consumer is used as the energy demand of each producer and consumer.

In a possible implementation manner of the first aspect, the user power demand constraint is specifically:

Where P _DR (t) is the actual dispatch power of the grid microgrid to the demand response load in the time period t; D _DR is the total power demand of the demand response load in the dispatch period;

is the minimum power demand of the demand response load in period t;

is the maximum electricity demand of the demand response load in period t.

In a possible implementation of the first aspect, the grid cost is calculated based on the annual load data of the energy producer and consumer, specifically:

The annual load data of energy producers and consumers include: conventional load power in the grid, photovoltaic output power in the grid, output power of micro gas turbines and day-ahead electricity price;

The grid cost is calculated based on the conventional load power in the grid, the photovoltaic output power in the grid, the output power of the micro gas turbine and the day-ahead transaction electricity price, which is:

C _M (t) = λ (t) [P _DR (t) + _PL (t) - _PG (t) - _PV (t)] Δt;

Where _CM (t) represents the grid cost; _PL (t) represents the conventional load power in the grid during period t; _PPV (t) represents the photovoltaic output power in the grid during period t; _PG (t) represents the output power of the micro gas turbine during period t; λ(t) represents the day-ahead transaction price of the distribution network; Δt is the scheduling step, which is 1h.

In a possible implementation of the first aspect, the dispatch cost is calculated based on the annual load data of the energy producer and consumer, specifically:

The annual load data of energy prosumers include: the unit dispatch cost of demand response load and the expected power consumption of demand response load;

The dispatch cost is calculated based on the unit dispatch cost of the demand response load and the expected power consumption of the demand response load, which is:

Where, C _DR (t) represents the dispatch cost; K _DR is the unit dispatch cost of the demand response load;

It represents the expected power consumption of the demand response load during the period t.

In a possible implementation of the first aspect, the controllable distributed power source cost is calculated based on the annual load data of the energy producer and consumer, specifically:

The annual load data of energy prosumers include: the output power of micro gas turbines;

The cost of controllable distributed power supply is calculated based on the output power of the micro gas turbine, which is:

C _G (t) = [aP _G (t) + b] Δt;

Where _CG (t) represents the cost of controllable distributed power generation; _CG (t) represents the power generation cost of the micro gas turbine in period t; a and b are cost coefficients; _PG (t) represents the output power of the micro gas turbine in period t.

In a possible implementation of the first aspect, a two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost, specifically:

Among them, _CG (t) represents the cost of controllable distributed generation; _CDR (t) represents the dispatching cost; _CM (t) represents the grid cost.

A second aspect of an embodiment of the present application provides an energy distribution device, including: a division module, a calculation module and a solution module;

Among them, the division module is used to divide the annual load data of energy producers and consumers, and generate the load data of new energy and the load data of each producer and consumer;

The calculation module is used to calculate the individual preferences of each prosumer based on the load data of the new energy and the load data of each prosumer, combined with the user's electricity demand constraints;

The solution module is used to establish a two-stage robust optimization model based on the annual load data of energy producers and consumers. After solving the two-stage robust optimization model to obtain the scheduling plan, the energy allocation result is obtained according to the scheduling plan and individual preferences.

In a possible implementation of the second aspect, a two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

The grid cost, dispatching cost and controllable distributed power source cost are calculated based on the annual load data of energy producers and consumers;

A two-stage robust optimization model is established based on the grid cost, dispatching cost and controllable distributed generation cost.

Compared with the prior art, an embodiment of the present invention provides an energy allocation method and device, the method comprising: dividing the annual load data of energy producers and consumers to generate load data of new energy and load data of each producer and consumer; calculating the individual preference of each producer and consumer based on the load data of new energy and the load data of each producer and consumer, combined with the user's electricity demand constraints; establishing a two-stage robust optimization model based on the annual load data of energy producers and consumers, solving the two-stage robust optimization model to obtain a scheduling plan, and then obtaining an energy allocation result based on the scheduling plan and individual preferences.

Its beneficial effect is that: the embodiment of the present invention divides the annual load data of energy producers and consumers, generates the load data of new energy and the load data of each producer and consumer, calculates the individual preferences of each producer and consumer based on the load data of new energy and the load data of each producer and consumer, solves the scheduling plan according to the two-stage robust optimization model, and obtains the energy allocation result according to the scheduling plan and the individual preferences. In the process of calculating the energy allocation result, the embodiment of the present invention considers the load data of new energy used as the uncertain set in the robust optimization and the load data of each producer and consumer used as the energy requirements of each producer and consumer, and fully considers the energy demand and power generation uncertainty of each energy producer and consumer, so the calculated energy allocation result can effectively improve the energy utilization efficiency.

Furthermore, the embodiments of the present invention are comprehensive, flexible, and practical, and can be easily promoted.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a schematic flow chart of an energy distribution method provided by an embodiment of the present invention;

FIG. 2 is a schematic diagram of the structure of an energy distribution device provided in one embodiment of the present invention.

Detailed ways

The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

1 is a flow chart of an energy allocation method provided by an embodiment of the present invention, including S101-S103:

S101: Divide the annual load data of energy producers and consumers to generate load data of new energy and load data of each producer and consumer.

In this embodiment, the annual load data of energy producers and consumers is divided to generate the load data of new energy and the load data of each producer and consumer, specifically:

The annual load data of the energy producer and consumer is divided into data in the form of energy types to generate the load data of the new energy; wherein the load data of the new energy is used as an uncertainty set in robust optimization;

The annual load data of the energy prosumer is divided from the perspective of the prosumer to generate the load data of each prosumer; wherein the load data of each prosumer is used as the energy demand of each prosumer.

S102: Calculate the individual preferences of each prosumer based on the load data of new energy sources and the load data of each prosumer and in combination with the power demand constraints of users.

Among them, the energy demand of each producer and consumer is divided from the perspective of the producer and consumer based on the load data of the energy producers and consumers participating in the stratification and grading, and the load data of each producer and consumer is used as the energy requirement of each producer and consumer; the individual preferences of each producer and consumer are taken into account according to the constraints on user electricity demand.

In this embodiment, the user power demand constraint is specifically:

Where P _DR (t) is the actual dispatch power of the grid microgrid to the demand response load in the time period t; D _DR is the total power demand of the demand response load in the dispatch period;

is the minimum power demand of the demand response load in period t;

is the maximum electricity demand of the demand response load in period t.

S103: A two-stage robust optimization model is established based on the annual load data of energy producers and consumers. After the scheduling plan is obtained by solving the two-stage robust optimization model, the energy allocation result is obtained according to the scheduling plan and individual preferences.

In this embodiment, the two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

The grid cost, dispatching cost and controllable distributed power source cost are calculated based on the annual load data of energy producers and consumers;

The two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost.

In a specific embodiment, the grid cost is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy producer and consumer include: conventional load power in the power grid, photovoltaic output power in the power grid, output power of micro gas turbines and day-ahead transaction electricity price;

The grid cost is calculated based on the conventional load power in the grid, the photovoltaic output power in the grid, the output power of the micro gas turbine and the day-ahead transaction electricity price, specifically:

C _M (t) = λ (t) [P _DR (t) + _PL (t) - _PG (t) - _PV (t)] Δt;

Wherein, _CM (t) represents the grid cost; _PL (t) represents the conventional load power in the grid during period t; _PPV (t) represents the photovoltaic output power in the grid during period t; _PG (t) represents the output power of the micro gas turbine during period t; λ(t) represents the day-ahead transaction price of the distribution network; Δt is the scheduling step, which is 1h.

In a specific embodiment, the scheduling cost is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy prosumer includes: the unit dispatch cost of the demand response load and the expected power consumption of the demand response load;

The dispatch cost is calculated according to the unit dispatch cost of the demand response load and the expected power consumption of the demand response load, specifically:

Wherein, C _DR (t) represents the dispatching cost; K _DR is the unit dispatching cost of the demand response load;

It represents the expected power consumption of the demand response load during the period t.

In a specific embodiment, the cost of controllable distributed power source is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy prosumer includes: the output power of the micro gas turbine;

The controllable distributed power source cost is calculated according to the output power of the micro gas turbine, specifically:

C _G (t) = [aP _G (t) + b] Δt;

Wherein, _CG (t) represents the cost of the controllable distributed power source; _CG (t) represents the power generation cost of the micro gas turbine in time period t; a and b are cost coefficients; _PG (t) represents the output power of the micro gas turbine in time period t.

In a specific embodiment, the two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost, specifically:

Wherein, _CG (t) represents the cost of the controllable distributed power source; _CDR (t) represents the dispatching cost; _CM (t) represents the grid cost.

Furthermore, since the load data of renewable energy is used as the uncertainty set in robust optimization, the uncertainty set is:

Among them, u _PV (t) is the uncertainty of each renewable energy output introduced after considering the uncertainty; u _L (t) is the load power uncertainty variable introduced after considering the uncertainty;

The maximum fluctuation deviation allowed for photovoltaic output;

The maximum fluctuation deviation allowed for load power;

and

All are positive numbers.

The load data of new energy sources is used as the uncertainty set in robust optimization. The economically optimal scheduling plan is obtained according to the uncertainty set when the uncertain variables in the uncertainty set change in the worst scenario. Then the inner and outer layers are optimized, and the iterative decomposition is performed according to the corresponding optimization variables. Then, the decomposed sub-problems are transformed according to the strong duality theory and merged with the outer max problem. The optimal scheduling plan can be obtained by solving the merged constraints.

The purpose of this model is to find the most economically optimal scheduling solution when the uncertain variable u changes towards the worst scenario in the uncertainty set U, which has the following form:

Among them, the minimization of the outer layer is the first stage problem, and the optimization variable is x; the maximum minimization of the inner layer is the second stage problem, and the optimization variables are u and y. The minimization problem here means minimizing the running cost; the expressions of x and y are as follows:

Ω(x,u) represents the feasible domain of optimizing variable y given a set of (x,u). The specific expression is as follows:

Where γ, λ, ν and π represent the dual variables corresponding to the constraints in the minimization problem of the second stage.

Using C&CG to decompose the purpose form, the main problem form is:

Where k is the current iteration number; y _l is the solution to the subproblem after the l-th iteration; u ^* _l is the value of the uncertain variable u in the worst scenario obtained after the l-th iteration.

The decomposed sub-problems are in the form of:

max _u∈U min _{y∈Ω(x，u)} c ^T y;

According to the strong duality theory, it is converted into the max form and merged with the outer max problem to obtain the following dual problem:

And by solving the above constraints, the optimal scheduling solution is obtained.

Furthermore, the optimal dispatching scheme includes: energy cost, flexible resource reserve cost and energy demand cost. The energy cost, flexible resource reserve cost and energy demand cost are allocated to the participants of the corresponding hierarchical dispatching plan (i.e., combined with individual preferences), and the final hierarchical dispatching result (i.e., energy allocation result) can be obtained. According to the above model and its dual variables, the price of flexible resources, the price of electricity and the marginal price of user electricity welfare can be calculated; according to the optimal dispatching scheme, combined with the individual preferences of each producer and consumer, the final hierarchical dispatching result is obtained.

The specific energy cost is: C _G (t) + C _DR (t) + C _M (t);

The cost of flexible resource reserve is: λ(t)[P _L (t)-P _PV (t)]Δt;

The energy demand cost is:

To further illustrate the energy distribution device, please refer to FIG. 2 , which is a schematic diagram of the structure of an energy distribution device provided by an embodiment of the present invention, including: a division module, a calculation module and a solution module;

The division module is used to divide the annual load data of energy producers and consumers, and generate the load data of new energy and the load data of each producer and consumer;

The calculation module is used to calculate the individual preferences of each prosumer based on the load data of the new energy source and the load data of each prosumer, combined with the user's electricity demand constraints;

The solution module is used to establish a two-stage robust optimization model based on the annual load data of energy producers and consumers, and after solving the two-stage robust optimization model to obtain a scheduling plan, obtain an energy allocation result based on the scheduling plan and the individual preferences.

In this embodiment, the two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

The grid cost, dispatching cost and controllable distributed power source cost are calculated based on the annual load data of energy producers and consumers;

The two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost.

In this embodiment, the annual load data of energy producers and consumers is divided to generate the load data of new energy and the load data of each producer and consumer, specifically:

The annual load data of the energy producer and consumer is divided into data in the form of energy types to generate the load data of the new energy; wherein the load data of the new energy is used as an uncertainty set in robust optimization;

The annual load data of the energy prosumer is divided from the perspective of the prosumer to generate the load data of each prosumer; wherein the load data of each prosumer is used as the energy demand of each prosumer.

In this embodiment, the user power demand constraint is specifically:

Where P _DR (t) is the actual dispatch power of the grid microgrid to the demand response load in the time period t; D _DR is the total power demand of the demand response load in the dispatch period;

is the minimum power demand of the demand response load in period t;

is the maximum electricity demand of the demand response load in period t.

In this embodiment, the grid cost is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy producer and consumer include: conventional load power in the power grid, photovoltaic output power in the power grid, output power of micro gas turbines and day-ahead transaction electricity price;

The grid cost is calculated based on the conventional load power in the grid, the photovoltaic output power in the grid, the output power of the micro gas turbine and the day-ahead transaction electricity price, specifically:

C _M (t) = λ (t) [P _DR (t) + _PL (t) - _PG (t) - _PV (t)] Δt;

Wherein, _CM (t) represents the grid cost; _PL (t) represents the conventional load power in the grid during period t; _PPV (t) represents the photovoltaic output power in the grid during period t; _PG (t) represents the output power of the micro gas turbine during period t; λ(t) represents the day-ahead transaction price of the distribution network; Δt is the scheduling step, which is 1h.

In this embodiment, the dispatching cost is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy prosumer includes: the unit dispatch cost of the demand response load and the expected power consumption of the demand response load;

The dispatch cost is calculated according to the unit dispatch cost of the demand response load and the expected power consumption of the demand response load, specifically:

Wherein, C _DR (t) represents the dispatching cost; K _DR is the unit dispatching cost of the demand response load;

It represents the expected power consumption of the demand response load during the period t.

In this embodiment, the controllable distributed power cost is calculated based on the annual load data of energy producers and consumers, specifically:

The annual load data of the energy prosumer includes: the output power of the micro gas turbine;

The controllable distributed power source cost is calculated according to the output power of the micro gas turbine, specifically:

C _G (t) = [aP _G (t) + b] Δt;

Wherein, _CG (t) represents the cost of the controllable distributed power source; _CG (t) represents the power generation cost of the micro gas turbine in time period t; a and b are cost coefficients; _PG (t) represents the output power of the micro gas turbine in time period t.

In this embodiment, the two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost, specifically:

Wherein, _CG (t) represents the cost of the controllable distributed power source; _CDR (t) represents the dispatching cost; _CM (t) represents the grid cost.

The embodiment of the present invention divides the annual load data of energy producers and consumers through a division module to generate load data of new energy and load data of each producer and consumer; the calculation module calculates the individual preference of each producer and consumer based on the load data of new energy and the load data of each producer and consumer, combined with the user's electricity demand constraints; the solution module establishes a two-stage robust optimization model based on the annual load data of energy producers and consumers, and after solving the two-stage robust optimization model to obtain a scheduling plan, the energy allocation result is obtained according to the scheduling plan and individual preferences.

The embodiment of the present invention divides the annual load data of energy producers and consumers, generates the load data of new energy and the load data of each producer and consumer, calculates the individual preferences of each producer and consumer based on the load data of new energy and the load data of each producer and consumer, solves the scheduling scheme based on the two-stage robust optimization model, and obtains the energy allocation result based on the scheduling scheme and the individual preferences. In the process of calculating the energy allocation result, the embodiment of the present invention considers the load data of new energy used as the uncertain set in the robust optimization and the load data of each producer and consumer used as the energy requirements of each producer and consumer, and fully considers the energy demand and power generation uncertainty of each energy producer and consumer, so the calculated energy allocation result can effectively improve the energy utilization efficiency.

Furthermore, the embodiments of the present invention are comprehensive, flexible, and practical, and can be easily promoted.

The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims (10)

1. An energy distribution method, characterized by comprising:

   Divide the annual load data of energy producers and consumers to generate new energy load data and load data of each producer and consumer;

   According to the load data of the new energy and the load data of each prosumer, combined with the user's electricity demand constraints, the individual preferences of each prosumer are calculated;

   A two-stage robust optimization model is established based on the annual load data of energy producers and consumers. After a scheduling plan is obtained by solving the two-stage robust optimization model, an energy allocation result is obtained based on the scheduling plan and the individual preferences.
2. The energy allocation method according to claim 1 is characterized in that the two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

   The grid cost, dispatching cost and controllable distributed power cost are calculated based on the annual load data of energy producers and consumers;

   The two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost.
3. According to claim 2, an energy distribution method is characterized in that the annual load data of energy producers and consumers is divided into data to generate the load data of new energy and the load data of each producer and consumer, specifically:

   The annual load data of the energy producer and consumer is divided into data in the form of energy types to generate the load data of the new energy; wherein the load data of the new energy is used as an uncertainty set in robust optimization;

   The annual load data of the energy prosumer is divided from the perspective of the prosumer to generate the load data of each prosumer; wherein the load data of each prosumer is used as the energy demand of each prosumer.
4. The energy allocation method according to claim 3 is characterized in that the user power demand constraint is specifically:

   

   

   Where P _DR (t) is the actual dispatch power of the grid microgrid to the demand response load in the time period t; D _DR is the total power demand of the demand response load in the dispatch period;

   

   is the minimum power demand of the demand response load in period t;

   

   is the maximum electricity demand of the demand response load in period t.
5. The energy distribution method according to claim 4 is characterized in that the grid cost is calculated based on the annual load data of energy producers and consumers, specifically:

   The annual load data of the energy producer and consumer include: conventional load power in the power grid, photovoltaic output power in the power grid, output power of micro gas turbines and day-ahead transaction electricity price;

   The grid cost is calculated based on the conventional load power in the grid, the photovoltaic output power in the grid, the output power of the micro gas turbine and the day-ahead transaction electricity price, specifically:

   C _M (t) = λ (t) [P _DR (t) + _PL (t) - _PG (t) - _PV (t)] Δt;

   Wherein, _CM (t) represents the grid cost; _PL (t) represents the conventional load power in the grid during time period t; _PPV (t) represents the photovoltaic output power in the grid during time period t; _PG (t) represents the output power of the micro gas turbine during time period t; λ(t) represents the day-ahead transaction price of the distribution network; Δt is the scheduling step, which is 1h.
6. The energy distribution method according to claim 5 is characterized in that the scheduling cost is calculated based on the annual load data of energy producers and consumers, specifically:

   The annual load data of the energy prosumer includes: the unit dispatch cost of the demand response load and the expected power consumption of the demand response load;

   The dispatch cost is calculated according to the unit dispatch cost of the demand response load and the expected power consumption of the demand response load, which is specifically:

   

   Wherein, C _DR (t) represents the dispatching cost; K _DR is the unit dispatching cost of the demand response load;

   

   It represents the expected power consumption of the demand response load during the period t.
7. According to the energy distribution method of claim 6, it is characterized in that the cost of controllable distributed power supply is calculated based on the annual load data of energy producers and consumers, specifically:

   The annual load data of the energy prosumer includes: the output power of the micro gas turbine;

   The controllable distributed power source cost is calculated according to the output power of the micro gas turbine, specifically:

   C _G (t) = [aP _G (t) + b] Δt;

   Wherein, _CG (t) represents the cost of the controllable distributed power source; _CG (t) represents the power generation cost of the micro gas turbine in time period t; a and b are cost coefficients; _PG (t) represents the output power of the micro gas turbine in time period t.
8. An energy distribution method according to claim 7, characterized in that the two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost, specifically:

   

   Wherein, _CG (t) represents the cost of the controllable distributed power source; _CDR (t) represents the dispatching cost; _CM (t) represents the grid cost.
9. An energy distribution device, characterized in that it comprises: a division module, a calculation module and a solution module;

   The division module is used to divide the annual load data of energy producers and consumers, and generate the load data of new energy and the load data of each producer and consumer;

   The calculation module is used to calculate the individual preferences of each prosumer based on the load data of the new energy source and the load data of each prosumer, combined with the user's electricity demand constraints;

   The solution module is used to establish a two-stage robust optimization model based on the annual load data of energy producers and consumers, and after solving the two-stage robust optimization model to obtain a scheduling plan, obtain an energy allocation result based on the scheduling plan and the individual preferences.
10. The energy distribution device according to claim 9 is characterized in that the two-stage robust optimization model is established based on the annual load data of energy producers and consumers, specifically:

    The grid cost, dispatching cost and controllable distributed power cost are calculated based on the annual load data of energy producers and consumers;

    The two-stage robust optimization model is established according to the grid cost, the dispatching cost and the controllable distributed power source cost.

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