New energy consumption optimization scheduling method for source-load-storage cooperative operation inside and outside heat supply period

Abstract

The invention discloses a new energy consumption optimization scheduling method for source-load-storage cooperative operation inside and outside a heat supply period, and belongs to the field of source-load-storage optimization scheduling research. The objective of the invention is to solve the problems of poor system adjustment flexibility and low wind power consumption efficiency caused by lack of comprehensive coordination control of various factors of a power grid in the prior art. Establishing a non-heat-supply objective function and a heat-supply objective function, selecting a corresponding non-heat-supply objective function or heat-supply objective function according to whether a current time period is a heat-supply period, establishing an electric power balance constraint, a wind-light output constraint, a thermal power output constraint and an energy storage constraint, and constraining the corresponding non-heat-supply objective function or heat-supply objective function; and optimizing and solving the constrained non-heat supply target function or heat supply target function to obtain the wind power generation power, the photovoltaic power generation power, the active power of the thermal power generating unit, the wind power matched energy storage reserve capacity and the photovoltaic matched energy storage reserve capacity in the current time period, thereby realizing new energy consumption optimization scheduling. The method is used for realizing new energy consumption optimization scheduling.

Description

Technical Field

[0001] The present invention belongs to the field of research on optimal scheduling of power sources, loads, and energy storage. Background Art

[0002] New energy power generation continues to integrate into the power system. Fluctuating renewable energy sources such as wind and light pose many difficulties to the flexible scheduling of the power system. Relying solely on the regulation of generator sets, it is difficult to achieve reliable economic scheduling. To cope with the uncertainty of new energy output, it is necessary to build a coordinated dynamic economic scheduling model for the wind-solar-energy storage system. At the same time, the problem of clean energy consumption has become increasingly prominent, and the grid security and stability control have encountered new challenges. The traditional dispatching operation control mode can no longer meet the new demands of the grid. In the new power market environment, relying solely on multi-source coordination to balance power can no longer meet the requirements of grid peak regulation, frequency modulation, and various power generation and electricity consumption customers.

[0003] Energy storage technology is of great significance for improving the grid regulation ability, enhancing the grid flexibility, and meeting various power generation and consumption demands. Battery energy storage can alleviate the power fluctuation of wind power and strongly support the peak shaving and valley filling of the grid; the large-capacity electric heat storage in power plants can greatly improve the flexible regulation level of thermal power units, and the flexible electric heating mode of distributed heat storage can fully store heat during low-load periods and enhance the grid's low-load peak regulation ability. However, energy storage technology can only release its maximum efficiency when it is in a coordinated control operation state of the grid. Current research results mostly focus on the coordinated operation strategies of two or three elements in the grid's "source-load-energy storage", such as the coordination between energy storage and the grid's automatic generation control, and the coordination between wind farms and the grid's automatic generation control. However, there is a lack of comprehensive coordinated control of various elements in the grid, and the multi-source and multi-operation domain coordination scenarios of thermal power, hydropower, energy storage, wind farms, photovoltaic power plants, nuclear power, and the grid cannot be fully demonstrated. There are also deficiencies in practical operation methods, resulting in poor system regulation flexibility and low wind power consumption efficiency. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems of poor system regulation flexibility and low wind power consumption efficiency caused by the lack of comprehensive coordinated control of various elements in the grid, and to propose an optimized scheduling method for new energy consumption with coordinated operation of power sources, loads, and energy storage during and outside the heating period.

[0005] The optimized scheduling method for new energy consumption with coordinated operation of power sources, loads, and energy storage during and outside the heating period includes the following steps:

[0006] Step 1: During the non-heating period, establish a non-heating objective function with the minimum sum of the penalty costs caused by wind and light curtailment and the operating costs of thermal power units as the goal.

[0007] During the heating period, a heating objective function is established with the goal of minimizing the sum of the penalty costs caused by wind and light curtailment, the operating costs of thermal power units, the penalty costs for deviations from the thermal power generation plan considering the flexibility of heating load, and the penalty costs for temperature deviation from the upper limit at the initial moment of the scheduling period.

[0008] Step 2: According to whether the current period is the heating period, select the corresponding non-heating objective function or heating objective function, and establish power balance constraints, wind and light power output constraints, thermal power output constraints, and energy storage constraints to constrain the corresponding non-heating objective function or heating objective function. Optimize and solve the constrained non-heating objective function or heating objective function to obtain the wind power generation power, photovoltaic power generation power, active power of thermal power units, reserve capacity of wind power supporting energy storage, and reserve capacity of photovoltaic supporting energy storage at the current period, so as to realize the optimal dispatching of new energy consumption.

[0009] Preferably, in Step 1, the non-heating objective function:

[0010] min(C fg.t +C h.t ) Formula 1,

[0011] In the formula, C fg.t is the penalty cost caused by wind and light curtailment at time t. n1 and n2 are the numbers of restricted wind turbines and photovoltaics respectively, and c1 and c2 are the economic cost coefficients of wind and light curtailment. are the rated powers of wind power and photovoltaic power generation respectively, P fi.t , P gi.t are the wind power and photovoltaic power generation powers at time t respectively. α(t) and β(t) are the reserve capacity reserve coefficients of energy storage at time t. S f , S g are the reserve capacities of wind power and photovoltaic supporting energy storage respectively. f fi (t), f gi (t) are the probability density functions of wind power and photovoltaic power generation respectively. t2 and t1 are the upper and lower limits of time respectively; C h.t is the operating cost of thermal power units at time t. n3 is the number of thermal power units. is the fuel cost. a i , b i , c i are all fuel cost coefficients. P hi.t is the active power of thermal power unit i at time t. h0P hi.t is the environmental cost. h0 is the operating cost of desulfurization and denitrification per unit of power supply. c0P hi.t is the combustion support cost. c0 is the unit power cost when the unit uses oil injection or plasma combustion support. d i P hi.t +ei is the loss cost of rotor fatigue loss and creep loss during the deep regulation stage, d i and e i are both unit power loss coefficients, f i is the operation and maintenance cost per unit time.

[0012] Preferably, in step 1, the heating objective function:

[0013] min(C fg.t +C h.t +C 2rt.i +C wt.i ) Formula 2,

[0014] In the formula, C 2rt.i is the penalty cost for the deviation of the thermoelectric plan when considering the flexibility of the heating load, is the planned active power of the heating unit when considering the flexibility of the heating load, P ri.t is the actual active power of the heating unit; C wt.i is the penalty cost when the temperature at the initial moment of the scheduling period deviates from the upper temperature limit. n4 is the number of users, h i is the penalty coefficient for the deviation of the indoor temperature of user i, θ i.T is the indoor temperature of user i at the start of each scheduling period, θ i.max is the upper limit of the indoor heating temperature of user i.

[0015] Preferably, in step 2, the power balance constraint is expressed as:

[0016] P hi.t +P fi.t +P gi.t +P ri.t =P load Formula 3,

[0017] In the formula, P load is the system load at time t.

[0018] Preferably, in step 2, the wind and solar power output constraints are expressed as:

[0019]

[0020] In the formula, P f.max 、P g.max are the maximum power of the wind turbine and photovoltaic power generation.

[0021] Preferably, in step 2, the thermal power output constraint is expressed as:

[0022] P hi.min ≤P hi.t ≤P hi.max Formula 5,

[0023] Wherein, P hi.min and P hi.max are the minimum and maximum power generations of the thermal power unit respectively.

[0024] Preferably, in step 2, the energy storage constraint is expressed as:

[0025]

[0026] Wherein, S f.min and S f.max are the minimum and maximum capacities of the energy storage supporting the wind power respectively, and S g.min and S g.max are the minimum and maximum capacities of the energy storage supporting the photovoltaic power respectively.

[0027] The beneficial effects of the present invention are as follows:

[0028] The present invention proposes a "source-network-load-storage" collaborative optimization operation method for the heating period, aiming to promote the consumption of wind power in the integrated energy system. The integrated energy system includes heating, wind power, photovoltaic power, thermal power and energy storage devices. By means of the coupling of the power grid and the heat grid, fuel, environment, combustion support, loss and operation and maintenance costs are taken into consideration, and then a "source-network-load-storage" collaborative optimization operation model with the system economic operation as the goal is constructed. In this way, the coupling between electric and thermal energies is strengthened, the wind power consumption space is significantly expanded, the system regulation flexibility is greatly improved, the wind power consumption efficiency is effectively increased, the power purchase and heating costs of the integrated energy system are reduced, the operation cost is decreased, and the overall economic benefit is improved.

[0029] Aiming at the northern winter heating period, combined with the requirements of high proportion of clean energy power generation, winter clean energy heating, and the wide application of battery energy storage technology, through the research on the coordinated dispatching of multiple sources and multiple operation domains of the power grid, a high proportion of clean energy consumption method based on automatic generation control is proposed with the purpose of reducing the wind and photovoltaic power abandonment in the power grid, and the operation boundary of the energy storage is more accurately defined. The model pursues the optimal total operation cost of the system, takes into account the carbon emission cost, the investment and operation and maintenance costs of the energy storage device, etc., and can solve the parameters in each time period to achieve real-time dispatching. Solving and analyzing this model can provide a scientific decision-making basis for power system operators, help the new power system towards clean and low-carbon, and lay a solid foundation for building a high proportion of new energy supply and consumption system. Description of the Drawings

[0030] Figure 1 is the flow chart of the new energy consumption optimization dispatching method for the collaborative operation of source, load and storage inside and outside the heating period;

[0031] Figure 2 is the schematic diagram of the optimized configuration of the heating energy storage;

[0032] Figure 3 It is a scheduling architecture diagram for multi-time scale scheduling coordination;

[0033] Figure 4 It is a flow chart for solving the collaborative scheduling of source, load and storage every other day during the heating period. Specific implementation manner

[0034] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0035] It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0036] Next, the present invention will be further described in conjunction with the accompanying drawings and specific embodiments, but it is not a limitation of the present invention.

[0037] Embodiment:

[0038] A new energy consumption optimization scheduling method for the collaborative operation of source, load and storage inside and outside the heating period, the method includes the following contents:

[0039] Step 1. In the non-heating period, establish a non-heating objective function with the minimum sum of the penalty cost generated by wind power curtailment and photovoltaic power curtailment and the operating cost of thermal power units as the goal;

[0040] In the heating period, establish a heating objective function with the minimum sum of the penalty cost generated by wind power curtailment and photovoltaic power curtailment, the operating cost of thermal power units, the penalty cost of the deviation of the thermoelectric plan when considering the flexibility of the heating load, and the penalty cost when the temperature at the initial moment of the scheduling period deviates from the temperature upper limit as the goal;

[0041] Step 2. According to whether the current period is the heating period, select the corresponding non-heating objective function or heating objective function, and establish power balance constraints, wind and light output constraints, thermal power output constraints and energy storage constraints to constrain the corresponding non-heating objective function or heating objective function, and optimize and solve the constrained non-heating objective function or heating objective function to obtain the wind power generation power, photovoltaic power generation power, active power of thermal power units, reserve capacity of wind power supporting energy storage and reserve capacity of photovoltaic supporting energy storage at the current period, so as to realize the new energy consumption optimization scheduling.

[0042] Specifically, Figure 3 The output of the wind and light units refers to the wind power generation power and the photovoltaic power generation power, and the heating output of the thermal power unit refers to the active power of the thermal power unit.

[0043] Further limited, in step 1, the non-heating objective function:

[0044] min(C fg.t +C h.t ) Formula 1,

[0045] wherein, C fg.t is the penalty cost generated by wind curtailment and PV curtailment at time t, n1 and n2 are the numbers of restricted wind turbines and PVs respectively, c1 and c2 are the economic cost coefficients of wind curtailment and PV curtailment respectively, are the rated powers of wind power and PV power generation respectively, P fi.t , P gi.t are the power generation powers of wind power and PV at time t respectively, α(t) and β(t) are the reserve coefficient of energy storage reserve capacity at time t, S f , S g are the reserve capacities of energy storage supporting wind power and PV respectively, f fi (t), f gi (t) are the probability density functions of wind power and PV power generation respectively, t2 and t1 are the upper and lower limits of time respectively; C h.t is the operating cost of thermal power units at time t, n3 is the number of thermal power units, is the fuel cost, a i , b i , c i are all fuel cost coefficients, P hi.t is the active power of thermal power unit i at time t, h0P hi.t is the environmental cost, h0 is the operating cost of desulfurization and denitration per unit power supply, c0P hi.t is the combustion support cost, c0 is the unit power cost when the unit uses oil injection or plasma combustion support, d i P hi.t +e i is the loss cost of rotor fatigue loss and creep loss in the deep load regulation stage, d i and e i are both unit power loss coefficients, f i is the operation and maintenance cost per unit time.

[0046] Specifically, in the peak shaving operation mode during the non-heating period, thermal power units have the ability to enter deep load regulation, increasing the flexibility capacity of the system. The optimization goal not only needs to consider the accommodation of new energy, but also needs to ensure the operation economy of the system. Therefore, Formula 1 is established.

[0047] To increase the grid connection penetration rate of wind and PV power generation, the penalty costs of wind curtailment and PV curtailment are introduced to meet the high proportion accommodation requirements of renewable energy. The wind curtailment and PV curtailment costs for the period from t1 to t2 are C fg.t. α(t) and β(t) are the reserve coefficient of the energy storage reserve capacity at time t, which decrease as the state of charge of the energy storage increases and are between [0, 1].

[0048] At present, when a large amount of new energy is connected to the power grid, it is very difficult for thermal power units to smooth out the fluctuations of wind and solar power only by conventional peak shaving means. If the deviation between the flexibility demand of the power system and the daily generation output plan of the thermal power unit is within the deviation range permitted by the real-time generation plan, and the output is also within the scope of the conventional technical output, there is no need to sacrifice the economic benefits of the operation of the thermal power unit to improve the system acceptance level. However, once the deviation exceeds the deviation range of the generation plan, or the real-time output plan deviates from the conventional technical output range of the thermal power unit, this means that the system flexibility regulation ability is lacking, and the thermal power unit must be put into the deep peak shaving mode to enhance the acceptance ability. When the unit enters the peak shaving state, the operating costs are mainly reflected in the costs of fuel, environment, combustion support, loss, and operation and maintenance. So there is C h.t .

[0049] Further defined, in step 1, the heating objective function:

[0050] min(C fg.t +C h.t +C 2rt.i +C wt.i ) Formula 2,

[0051] In the formula, C 2rt.i is the penalty cost for the deviation of the thermal power plan when considering the flexibility of the heating load, is the planned active power of the heating unit when considering the flexibility of the heating load, P ri.t is the actual active power of the heating unit; C wt.i is the penalty cost when the temperature at the initial moment of the scheduling period deviates from the upper temperature limit. n4 is the number of users, h i is the penalty coefficient for the deviation of the indoor temperature of user i, θ i.T is the indoor temperature of user i at the start of each scheduling period, θ i.max is the upper limit of the indoor heating temperature of user i.

[0052] Specifically, during the heating period, some units will give priority to operating according to the given thermal power plan, and only allow a range of adjustments to this plan when there is wind curtailment. The objective function is as shown in Formula 2. Formula 2 combines the penalty costs of wind and light curtailment with the operating costs of thermal power units at full load, and can comprehensively reflect the overall operating costs when a large-scale new energy is connected to the system, which is more in line with the actual production and operation situation of the energy base. Under the condition of considering both safety and economy, it can provide a decision-making basis for the operating mode of thermal power units. At the same time, considering the flexibility of the heating load during the heating period, when there is wind curtailment, the heating output can be flexibly adjusted manually to meet the new energy consumption demand.

[0053] During the heating season, some units also need to undertake heating tasks in addition to power generation. Under the "heat determines power" mode, these units will give priority to operating according to the given thermal power plan, and only allow a range of adjustments to this plan under special working conditions. Therefore, a penalty term for the deviation of the thermal power plan of heating units is introduced.

[0054]

[0055] In the formula, C rt.i is the penalty cost for the deviation of the thermal power plan of heating units, n4 is the number of heating units, gi is the penalty coefficient for the deviation of the plan of unit i, is the planned active power of the heating unit, P ri.t is the actual active power of the heating unit.

[0056] During the winter heating stage, the accommodation of wind power encounters obstacles. The key lies in the "heat determines power" operating mode of heating units, which restricts the peak shaving ability. Exploring the potential of urban heating pipe networks is an effective way to improve the accommodation of wind power. Urban heating pipe networks are large in scale, have good heat preservation effects, and have strong thermal inertia. On the basis of ensuring that heating is not affected, by virtue of the heat storage characteristics and thermal inertia of the heating pipe network, the heat load of heating units can be shifted, and their heat and power outputs can be appropriately reduced to achieve the optimal operation and control of thermoelectric coupling, strengthen the peak shaving efficiency of heating units, and create space for the accommodation of wind power. After all, heating users care about the appropriate indoor temperature. As long as the indoor temperature is within a reasonable fluctuation range, the heat load demand of users is considered to be met. Therefore, the heating load of the unit has a certain flexible adjustment space.

[0057] Therefore, the penalty term for the deviation of the thermal power plan of heating units is improved to construct a new optimization model C 2rt.i . This model aims to track the set power and comprehensively considers the constraints such as the electro-thermal coupling of units, heat losses in heating pipe networks, time-delay characteristics, and the thermal inertia of heating users.

[0058] Among them, takes values according to the following principle: when there is a wind curtailment period, the value is the target electric power modified manually, and the value range is To ensure the minimum heating output when the user requires the lowest indoor temperature, To ensure the maximum heating output when there is no wind curtailment; when there is no wind curtailment period, It is the planned heating output.

[0059] In addition, to ensure the heating needs of users and prevent the indoor temperature of users from remaining at the lowest temperature required by heating users, the penalty cost C for the deviation of the indoor temperature in the user area is introduced wt.i , at the initial moment of each scheduling period. The indoor temperature in each heating user area is the upper limit of the indoor temperature in the heating user area.

[0060] Further defined, in step 2, the power balance constraint is expressed as:

[0061] P hi.t +P fi.t +P gi.t +P ri.t =P load Formula 3,

[0062] In the formula, P load is the system load at time t.

[0063] Further defined, in step 2, the wind and solar power output constraints are expressed as:

[0064]

[0065] In the formula, P f.max 、P g.max are the maximum power of the wind turbine and photovoltaic power generation.

[0066] Further defined, in step 2, the thermal power output constraint is expressed as:

[0067] P hi.min ≤P hi.t ≤P hi.max Formula 5,

[0068] In the formula, P hi.min 、P hi.max are the minimum and maximum power generation of the thermal power unit.

[0069] Further defined, in step 2, the energy storage constraint is expressed as:

[0070]

[0071] In the formula, S f.min and S f.max are the minimum and maximum capacities of the energy storage supporting the wind power respectively, and S g.min and S g.max are the minimum and maximum capacities of the energy storage supporting the photovoltaic power respectively.

[0072] Specifically,Figure 4 As an example of obtaining the wind power generation power, photovoltaic power generation power, thermal power unit active power, wind power supporting energy storage reserve capacity, and photovoltaic power supporting energy storage reserve capacity once every other day during the heating period, this is to illustrate that this embodiment can obtain the parameters for each period and achieve real-time scheduling; the objective function can analyze the wind power generation power P fi.t , photovoltaic power generation power P gi.t , thermal power unit active power P hi.t , wind power supporting energy storage reserve capacity S f , and photovoltaic power supporting energy storage reserve capacity S g . However, according to actual needs, the required parameters can be selected from them. For example, Figure 4 in, for the day-ahead parameters, only the required thermal power unit active power P hi.t , wind power supporting energy storage reserve capacity S f , and photovoltaic power supporting energy storage reserve capacity S g are selected, and the power generation power P fi.t , photovoltaic power generation power P gi.t are not selected; while for the intra-day parameters, all the required parameters are selected: wind power generation power P fi.t , photovoltaic power generation power P gi.t , thermal power unit active power P hi.t , wind power supporting energy storage reserve capacity S f , and photovoltaic power supporting energy storage reserve capacity S g .

[0073] Although the present invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the present invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed, as long as they do not deviate from the spirit and scope of the present invention as defined by the appended claims. It should be understood that different dependent claims and the features described herein can be combined in a manner different from that described in the original claims. It should also be understood that the features described in connection with a single embodiment can be used in other described embodiments.

Claims

1. A new energy consumption optimization dispatching method for collaborative operation of power sources, loads and energy storages inside and outside the heating period, characterized in that, The method includes the following: Step 1: During the non-heating period, establish a non-heating objective function with the minimum sum of the penalty costs generated by wind and photovoltaic curtailment and the operating costs of thermal power units as the goal. During the heating period, establish a heating objective function with the minimum sum of the penalty costs generated by wind and photovoltaic curtailment, the operating costs of thermal power units, the penalty costs for the deviation of the thermal power generation plan when considering the flexibility of the heating load, and the penalty costs for the deviation of the temperature from the upper limit at the initial moment of the scheduling period as the goal. Step 2: According to whether the current period is the heating period, select the corresponding non-heating objective function or heating objective function, and establish power balance constraints, wind and photovoltaic output constraints, thermal power output constraints, and energy storage constraints to constrain the corresponding non-heating objective function or heating objective function. Optimize and solve the constrained non-heating objective function or heating objective function to obtain the wind power generation power, photovoltaic power generation power, active power of thermal power units, reserve capacity of wind power supporting energy storage, and reserve capacity of photovoltaic power supporting energy storage in the current period, and realize the optimal dispatching of new energy consumption.

2. The new energy consumption optimization dispatching method for coordinated operation of source, load and storage inside and outside the heating period according to claim 1, wherein In Step 1, the non-heating objective function: min(C fg.t +C h.t ) Formula 1 In the formula, C fg.t is the penalty cost caused by wind and PV curtailment at time t. n1 and n2 are the numbers of restricted wind turbines and PVs respectively. c1 and c2 are the economic cost coefficients of wind and PV curtailment respectively. are the rated powers of wind power and PV power generation respectively. P fi.t 、P gi.t are the wind power and PV power generation at time t respectively. α(t) and β(t) are the reserve coefficient of energy storage reserve capacity at time t. S f 、S g are the energy storage reserve capacities of wind power and PV power respectively. f fi (t)、f gi (t) are the probability density functions of wind power and PV power generation respectively. t2 and t1 are the upper and lower limits of time respectively; C h.t is the operating cost of thermal power units at time t. n3 is the number of thermal power units, is the fuel cost. a i 、b i 、c i are all fuel cost coefficients. P hi.t is the active power of thermal power unit i at time t. h0P hi.t is the environmental cost. h0 is the operating cost of desulfurization and denitrification per unit power supply. c0P hi.t is the combustion support cost. c0 is the unit power cost when the unit uses oil injection or plasma combustion support. d i P hi.t +e i is the loss cost of rotor fatigue loss and creep loss in the deep regulation stage. d i and e i are all unit power loss coefficients. f i is the operation and maintenance cost per unit time.

3. The new energy consumption optimization dispatching method for coordinated operation of source, load and storage inside and outside the heating period according to claim 2, characterized in that In Step 1, the heating objective function: min(C fg.t +C h.t +C 2rt.i +C wt.i ) Formula 2 In the formula, C 2rt.i is the penalty cost for the deviation of the thermoelectric plan when considering the flexibility of the heating load, is the planned active power of the heating unit when considering the flexibility of the heating load, and P ri.t is the actual active power of the heating unit; C wt.i is the penalty cost when the temperature at the initial moment of the scheduling period deviates from the upper temperature limit. n4 is the number of users, and h i is the penalty coefficient for the deviation of the indoor temperature of user i, and θ i.T is the indoor temperature of user i at the start of each scheduling period, and θ i.max is the upper limit of the indoor heating temperature of user i.

4. The new energy consumption optimization scheduling method for coordinated operation of source, load and storage inside and outside the heating period according to claim 3, wherein, In Step 2, the power balance constraint is expressed as: P hi.t +P fi.t +P gi.t +P ri.t =P load Formula 3 Where P load is the system load at time t.

5. The new energy consumption optimization dispatching method for collaborative operation of power sources, loads and energy storage during and outside the heating period according to claim 4, characterized in that In Step 2, the wind and photovoltaic output constraints are expressed as: where P f.max , P g.max are the maximum powers of the fan and photovoltaic power generation respectively.

6. The new energy consumption optimization dispatching method for source-load-storage collaborative operation inside and outside the heating period according to claim 4 or 5, characterized in that In Step 2, the thermal power output constraint is expressed as: P hi.min ≤P hi.t ≤P hi.max Formula 5 Wherein, P hi.min and P hi.max are the minimum and maximum power generation of the thermal power unit respectively.

7. The new energy consumption optimization scheduling method for coordinated operation of source, load and storage inside and outside the heating period according to claim 3, characterized in that In Step 2, the energy storage constraint is expressed as: Wherein, S f.min and S f.max are respectively the minimum capacity and the maximum capacity of the energy storage supporting wind power, and S g.min and S g.max are respectively the minimum capacity and the maximum capacity of the energy storage supporting photovoltaic power.

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