CN112531703A - Optimization method for providing multi-market and local service by multi-energy virtual power plant - Google Patents

Optimization method for providing multi-market and local service by multi-energy virtual power plant Download PDF

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CN112531703A
CN112531703A CN202011457371.4A CN202011457371A CN112531703A CN 112531703 A CN112531703 A CN 112531703A CN 202011457371 A CN202011457371 A CN 202011457371A CN 112531703 A CN112531703 A CN 112531703A
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optimization
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CN112531703B (en
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赵建立
郑庆荣
盛明
高志刚
汤卓凡
陆颖杰
张娟
郭雁
王静
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State Grid Shanghai Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/30—Reactive power compensation

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Abstract

The invention discloses an optimization method for providing multi-market and local service by a multi-energy virtual power plant, and belongs to the technical field of power demand response. The invention mainly comprises the following steps: (1) first-level optimization, namely converting the nonlinear and non-convex problems into mixed integer linear programming problems by using a linearization technology, and performing rolling optimization by using real-time information every 24 hours based on a scene; (2) performing secondary optimization, namely performing rolling optimization once again by using real-time information every 30 minutes based on a scene by using a 24-hour period; (3) and (3) three-stage optimization, wherein rolling optimization is carried out every 5 minutes by adopting second-order cone (SOC) convex relaxation based on an optimal power flow (OFP) equation with a period of 30 minutes. The litigation method is based on a multi-market and local service scene and a rolling optimization model, modeling is carried out on the VPP under the condition that uncertainty is considered, local network constraint is relieved as a result after optimization, and reactive support is provided for a power grid.

Description

Optimization method for providing multi-market and local service by multi-energy virtual power plant
Technical Field
The invention discloses an optimization method for providing multi-market and local service by a multi-energy virtual power plant, which solves the problem of uncertainty based on a model of VPP multi-market and local service scene and rolling optimization. Belonging to the technical field of power demand response.
Background
In recent years, the degree of integration of Distributed Energy (DER) has been increasing, and renewable energy power generation has also been increasing. Many of the DERs currently incorporated into power distribution networks are too small to participate in the power market and therefore represent unregulated power generation. Without proper control, these DERs may cause voltage and power flow problems and may not take full advantage of network strain. An emerging approach to DER aggregation that enables them to participate in the power market is through Virtual Power Plants (VPP). Through control and coordination of the DER, the VPP can participate in the electricity market and provide grid services. But this puts high demands on the scheduling capacity of the virtual power plant.
Disclosure of Invention
The technical problem is as follows: the invention aims to make up the defects of the background technology and provides an optimization method for providing multi-market and local services for a multi-energy virtual power plant.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
an optimization method for providing multi-market and local service by a multi-energy virtual power plant is characterized by comprising the following steps: the method comprises the following steps:
(1) first-level optimization, namely converting a nonlinear and non-convex problem into a mixed integer linear programming problem by using a linearization technology, and performing rolling optimization by using real-time information every 24 hours based on VPP multi-market and local service scenes;
(2) performing secondary optimization, namely performing rolling optimization once again by using real-time information every 30 minutes based on VPP multi-market and local service scenes by adopting a 24-hour period;
(3) and (3) three-stage optimization, wherein rolling optimization is carried out every 5 minutes by adopting second-order cone (SOC) convex relaxation based on an optimal power flow (OFP) equation with a period of 30 minutes.
Specifically, the step (1) comprises the following steps:
(1-1) cost function analysis of optimization problem
The cost function of the first-order optimization problem is shown in the following graph:
Figure BDA0002829376350000011
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being represented by pisGiving, for weighting the corresponding component of the cost, SHLIs the number of scenes considered in the primary optimization stage. For each scene s, the corresponding operating cost of the plant is
Figure BDA0002829376350000012
The cost of the equipment for reducing the load is
Figure BDA0002829376350000013
The cost/benefit of buying/selling real power from the grid is
Figure BDA0002829376350000021
Is just
Figure BDA0002829376350000022
Represents cost, minus
Figure BDA0002829376350000023
Indicating revenue. While accounting for revenue in providing reactive support of the grid to upstream networks
Figure BDA0002829376350000024
Frequency Control Assisted Service (FCAS) provisioning
Figure BDA0002829376350000025
And selling hydrogen energy
Figure BDA0002829376350000026
The reactive revenue component allows the VPP to inject/absorb reactive power when a network operator needs such support by adjusting the reactive operating point of the equipment and taking into account the equipment and network active/reactive power constraints. The linearity of the OPF equation is used to trade computational tractability for model accuracy, but it is important to maintain accuracy as much as possible. Therefore, the assumptions used in the OPF equation linearization process must be carefully considered. Most OPF linearization methods consider transmission line level networks, where the line reactance is much larger than its resistance. This allows the voltage amplitude (| V) of the buskI) is approximately 1pu, ignoring the reactive power flow and modeling the active power flow as a linear function of the voltage angle difference between two adjacent busbars ("direct current power flow"). In the distribution network, these assumptions are invalid. To capture the reactive power more accurately, a model should be employed that takes into account both line conductance and susceptance, voltage magnitude and voltage phase angle.
(1-2) analysis of linearized model of Optimized Power Flow (OPF) equation
And (3) linearizing an OPF equation in the first-level optimization, wherein an active power flow equation and a reactive power flow equation are shown as follows:
pkh,s(t)＝Gkh(|Vk,s(t)|-|Vh,s(t)|)-Bkh(θk,s(t)-θh,s(t)) (2)
qkh,s(t)＝-Bkh(|Vk,s(t)|-|Vh,s(t)|)-Gkh(θk,s(t)-θh,s(t)) (3)
in the formula pkh,s(t) and qkh,s(t) is the active and reactive flow between nodes k and h, respectively.
A general model of the operation of each plant is as follows:
Figure BDA0002829376350000027
in the formula Ek,iIs the energy storage capacity of the device; x is the number ofk,i,sIs a standardized energy storage level;
Figure BDA0002829376350000028
is the efficiency of the load and the generator; e is the same ask,i,s,tThe active power available or required, depending on the sign thereof; omegak,i,s(t) represents ∈k,i,s,tThe number of the reduction; v. ofk,iRepresents an energy storage loss; Δ t is the time step;
(1-3) device dependent constraint analysis
The active and reactive power provided by the equipment is limited by:
Figure BDA0002829376350000029
Figure BDA00028293763500000210
Figure BDA00028293763500000211
equations (8) - (10) define the active power injected by the device and limit the step α at each time respectivelyk,iAnd planning time domain betak,iThe amount of the steel can be reduced.
Figure BDA0002829376350000031
0≤αk,i|∈k,i,s,t|-|ωk,i,s(t)| (9)
Figure BDA0002829376350000032
By ensuring the required reactive power, assuming that the load power factor remains constant during load shedding
Figure BDA0002829376350000033
The load power factor is ensured to be constant according to the proportional reduction of the active power. As shown in the following formula:
Figure BDA0002829376350000034
the device may also be limited by the ability to climb a hill. Node power injection is the sum of the power injected by the devices at the node, equal to the net power flowing into/out of the node.
Figure BDA0002829376350000035
The equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity HcapStandardized storage of hydrogen hs。
Figure BDA0002829376350000036
Represents the sales volume of the hydrogen energy market,
Figure BDA0002829376350000037
it indicates whether the equipment is a hydrogen energy equipment.
Equation (13) (14) indicates that the constraints limit the bids that can be made by the emergency frequency control auxiliary service market equipment, taking into account the maximum climbing capacity of the equipment and the auxiliary service response time.
Figure BDA0002829376350000038
Figure BDA0002829376350000039
The frequency control assistance service herein refers specifically to an emergency frequency control assistance service used when the frequency is significantly changed, and the response time is the time required for the provider to request the frequency control assistance service to reach its bid power output. The following constraints are introduced to ensure that these devices have sufficient energy margin to provide the relevant services.
Figure BDA00028293763500000310
Figure BDA00028293763500000311
Specifically, the step (2) includes the following steps:
the second-level optimization is mainly used for processing the economic dispatching problem, so that the SOC convex relaxation of the optimal power flow equation is used. This gives a more accurate model of the power flow equation, including modeling the loss. To maintain convexity, the criterion in equation (5) is exchanged for convex quadratic constraint. For example, operation of capacitor bank, SOC convex relaxation using vkAs state variables, where vk＝|Vk|2. The voltage dependence of the reactive power output of the capacitor is given by equation (17). Equation (18) limits the plant operating conditions for reactive power operation if the plant can only operate within a fixed power factor range.
Figure BDA0002829376350000041
Figure BDA0002829376350000042
In the formulak,iAnd
Figure BDA0002829376350000043
is the minimum and maximum allowable apparent power phase angle. When jointly optimizing active and reactive power, it is important to optimize the interaction between the variables. Since the plan has been set in the first-level optimization, the on-off state of the device is a parameter rather than a decision variable, so both are convex constraints. The apparent power flow constraint can also be accurately modeled in convex optimization through convex quadratic constraint, wherein
Figure BDA0002829376350000044
Indicating the apparent power limit.
Figure BDA0002829376350000045
The secondary optimization provides a set of operating points for the first time span and storage profile of all scenarios.
Specifically, the step (3) comprises the following steps:
three-level optimization and two-level optimization based on VPP multi-market and local service scenes are similar, except that the cost function of the VPP multi-market and local service scenes optimizes the scene number S in the three levelsLLCalculating the minimum value, considering the deviation of the second-level optimization problem, and adding a penalty factor
Figure BDA0002829376350000046
Specifically, as shown in formula (23):
Figure BDA0002829376350000047
the tertiary optimization has a 30 minute planning horizon, but the penalty factor may prevent the tertiary optimization problem from deviating too far from the daily optimal solution unless the shutdown constraints conflict, or if there is sufficient additional revenue. As the formula utilizes the SOC relaxation of the OPF equation, the working point determined by the three-level optimization runs through a complete non-convex alternating current flow, thereby obtaining a feasible solution on the technology in the actual system. Three-level optimization provides a set of market quotes that a VPP can complete in all scenarios.
Has the advantages that: the invention provides an optimization method for providing multi-market and local service for a multi-energy virtual power plant aiming at the problem that the uncertainty of the demand and the intermittence of DER require OPF to be solved in a smaller time interval.
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FIG. 1 is a general flow diagram of the present invention.
Detailed Description
The invention will be further explained below with reference to the drawings.
Fig. 1 shows an optimization method for providing multi-market and local services for a multi-energy virtual power plant, which is based on a multi-market and local service scenario and a rolling optimization model to solve the uncertainty problem. The respective steps are specifically described below.
The method comprises the following steps: and (3) first-level optimization, wherein a nonlinear and non-convex problem is converted into a mixed integer linear programming problem by using a linearization technology, and the real-time information is used for performing rolling optimization every 24 hours based on VPP multi-market and local service scenes.
(1-1) cost function analysis of optimization problem
The cost function of the first-order optimization problem is shown in the following graph:
Figure BDA0002829376350000051
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being represented by pisGiving, for weighting the corresponding component of the cost, SHLIs the number of scenes considered in the primary optimization stage. For each scene s, device phaseThe running cost is
Figure BDA0002829376350000052
The cost of the equipment for reducing the load is
Figure BDA0002829376350000053
The cost/benefit of buying/selling real power from the grid is
Figure BDA0002829376350000054
Is just
Figure BDA0002829376350000055
Represents cost, minus
Figure BDA0002829376350000056
Indicating revenue. While accounting for revenue in providing reactive support of the grid to upstream networks
Figure BDA0002829376350000057
Frequency Control Assisted Service (FCAS) provisioning
Figure BDA0002829376350000058
And selling hydrogen energy
Figure BDA0002829376350000059
The reactive revenue component allows the VPP to inject/absorb reactive power when a network operator needs such support by adjusting the reactive operating point of the equipment and taking into account the equipment and network active/reactive power constraints. The linearity of the OPF equation is used to trade computational tractability for model accuracy, but it is important to maintain accuracy as much as possible. Therefore, the assumptions used in the OPF equation linearization process must be carefully considered. Most OPF linearization methods consider transmission line level networks, where the line reactance is much larger than its resistance. This allows the voltage amplitude (| V) of the buskI) is approximately 1pu, ignoring the reactive power flow and modeling the active power flow as a linear function of the voltage angle difference between two adjacent busbars ("direct current power flow"). In the distribution network, these assumptions are invalid. Is composed ofTo capture the reactive power more accurately, a model should be used that considers both line conductance and susceptance, voltage magnitude and voltage phase angle.
(1-2) analysis of linearized model of Optimized Power Flow (OPF) equation
And (3) linearizing an OPF equation in the first-level optimization, wherein an active power flow equation and a reactive power flow equation are shown as follows:
pkh,s(t)＝Gkh(|Vk,s(t)|-|Vh,s(t)|)-Bkh(θk,s(t)-θh,s(t)) (2)
qkh,s(t)＝-Bkh(|Vk,s(t)|-|Vh,s(t)|)-Gkh(θk,s(t)-θh,s(t)) (3)
in the formula pkh,s(t) and qkh,s(t) is the active and reactive flow between nodes k and h, respectively.
A general model of the operation of each plant is as follows:
Figure BDA00028293763500000510
in the formula Ek,iIs the energy storage capacity of the device; x is the number ofk,i,sIs a standardized energy storage level;
Figure BDA00028293763500000511
is the efficiency of the load and the generator; e is the same ask,i,s,tThe active power available or required, depending on the sign thereof; omegak,i,s(t) represents ∈k,i,s,tThe number of the reduction; v. ofk,iRepresents an energy storage loss; Δ t is the time step;
(1-3) device dependent constraint analysis
The active and reactive power provided by the equipment is limited by:
Figure BDA0002829376350000061
Figure BDA0002829376350000062
Figure BDA0002829376350000063
equations (8) - (10) define the active power injected by the device and limit the step α at each time respectivelyk,iAnd planning time domain betak,iThe amount of the steel can be reduced.
Figure BDA0002829376350000064
0≤αk,i|∈k,i,s,t|-|ωk,i,s(t)| (9)
Figure BDA0002829376350000065
By ensuring the required reactive power, assuming that the load power factor remains constant during load shedding
Figure BDA0002829376350000066
The load power factor is ensured to be constant according to the proportional reduction of the active power. As shown in the following formula:
Figure BDA0002829376350000067
the device may also be limited by the ability to climb a hill. Node power injection is the sum of the power injected by the devices at the node, equal to the net power flowing into/out of the node.
Figure BDA0002829376350000068
The equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity HcapStandardized storage of hydrogen hs。
Figure BDA0002829376350000069
Represents the sales volume of the hydrogen energy market,
Figure BDA00028293763500000610
it indicates whether the equipment is a hydrogen energy equipment.
Equation (13) (14) indicates that the constraints limit the bids that can be made by the emergency frequency control auxiliary service market equipment, taking into account the maximum climbing capacity of the equipment and the auxiliary service response time.
Figure BDA00028293763500000611
Figure BDA0002829376350000071
The frequency control assistance service herein refers specifically to an emergency frequency control assistance service used when the frequency is significantly changed, and the response time is the time required for the provider to request the frequency control assistance service to reach its bid power output. The following constraints are introduced to ensure that these devices have sufficient energy margin to provide the relevant services.
Figure BDA0002829376350000072
Figure BDA0002829376350000073
Step two: and secondary optimization, namely, adopting 24 hours as a period, and performing rolling optimization once again by using real-time information every 30 minutes based on VPP multi-market and local service scenes.
The second-level optimization is mainly used for processing the economic dispatching problem, so that the SOC convex relaxation of the optimal power flow equation is used. This gives a more accurate model of the power flow equation, including modeling the loss. To maintainConvexity, the criterion in equation (5) is replaced by convex quadratic constraint. For example, operation of capacitor bank, SOC convex relaxation using vkAs state variables, where vk＝|Vk|2. The voltage dependence of the reactive power output of the capacitor is given by equation (17). Equation (18) limits the plant operating conditions for reactive power operation if the plant can only operate within a fixed power factor range.
Figure BDA0002829376350000074
Figure BDA0002829376350000075
In the formulak,iAnd
Figure BDA0002829376350000076
is the minimum and maximum allowable apparent power phase angle. When jointly optimizing active and reactive power, it is important to optimize the interaction between the variables. Since the plan has been set in the first-level optimization, the on-off state of the device is a parameter rather than a decision variable, so both are convex constraints. The apparent power flow constraint can also be accurately modeled in convex optimization through convex quadratic constraint, wherein
Figure BDA0002829376350000077
Indicating the apparent power limit.
Figure BDA0002829376350000078
The secondary optimization provides a set of operating points for the first time span and storage profile of all scenarios.
Step three: and (3) three-stage optimization, wherein rolling optimization is carried out every 5 minutes by adopting second-order cone (SOC) convex relaxation based on an optimal power flow (OFP) equation with a period of 30 minutes.
Three-level optimization based on sceneSimilar to the second-level optimization, except that the cost function optimizes the scene number S at the third levelLLCalculating the minimum value, considering the deviation of the second-level optimization problem, and adding a penalty factor
Figure BDA0002829376350000079
Specifically, as shown in formula (23):
Figure BDA00028293763500000710
the tertiary optimization has a 30 minute planning horizon, but the penalty factor may prevent the tertiary optimization problem from deviating too far from the daily optimal solution unless the shutdown constraints conflict, or if there is sufficient additional revenue. As the formula utilizes the SOC relaxation of the OPF equation, the working point determined by the three-level optimization runs through a complete non-convex alternating current flow, thereby obtaining a feasible solution on the technology in the actual system. Three-level optimization provides a set of market quotes that a VPP can complete in all scenarios.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (4)

1. An optimization method for providing multi-market and local service by a multi-energy virtual power plant is characterized by comprising the following steps: the method comprises the following steps:
(1) first-level optimization, namely converting a nonlinear and non-convex problem into a mixed integer linear programming problem by using a linearization technology, and performing rolling optimization by using real-time information every 24 hours based on VPP multi-market and local service scenes;
(2) performing secondary optimization, namely performing rolling optimization once again by using real-time information every 30 minutes based on VPP multi-market and local service scenes by adopting a 24-hour period;
(3) and (3) three-stage optimization, wherein rolling optimization is carried out every 5 minutes by adopting second-order cone (SOC) convex relaxation based on an optimal power flow (OFP) equation with a period of 30 minutes.
2. The method of claim 1, wherein the method comprises: the step (1) is carried out according to the following steps:
(1-1) cost function analysis of optimization problem
The cost function of the first-order optimization problem is shown in the following graph:
Figure FDA0002829376340000011
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being represented by pisGiving, for weighting the corresponding component of the cost, SHLIs the number of scenes considered in the primary optimization stage; for each scene s, the corresponding operating cost of the plant is
Figure FDA0002829376340000012
The cost of the equipment for reducing the load is
Figure FDA0002829376340000013
The cost/benefit of buying/selling real power from the grid is
Figure FDA0002829376340000014
Is just
Figure FDA0002829376340000015
Represents cost, minus
Figure FDA0002829376340000016
Representing revenue; while accounting for revenue in providing reactive support of the grid to upstream networks
Figure FDA0002829376340000017
Frequency Control Assisted Service (FCAS) provisioning
Figure FDA0002829376340000018
And selling hydrogen energy
Figure FDA0002829376340000019
The reactive revenue section allows the VPP to inject/absorb reactive power when the network operator needs such support by adjusting the reactive operating points of the equipment and considering the active/reactive power constraints of the equipment and the network; using linearization of OPF equations trades computational tractability for model accuracy, but it is important to maintain accuracy as much as possible; therefore, the assumptions used in the OPF equation linearization process must be carefully considered; most OPF linearization methods consider transmission line level networks, where the line reactance is much larger than its resistance; this allows the voltage amplitude (| V) of the busk|)) is approximately 1pu, ignoring the reactive power flow and modeling the active power flow as a linear function of the voltage angle difference between two adjacent busbars ("direct current power flow"); in the distribution network, these assumptions are invalid; in order to capture the reactive power more accurately, a model which simultaneously considers the line conductance and susceptance, the voltage amplitude and the voltage phase angle is adopted;
(1-2) analysis of linearized model of Optimized Power Flow (OPF) equation
And (3) linearizing an OPF equation in the first-level optimization, wherein an active power flow equation and a reactive power flow equation are shown as follows:
pkh,s(t)＝Gkh(|Vk,s(t)|-|Vh,s(t)|)-Bkh(θk,s(t)-θh,s(t)) (2)
qkh,s(t)＝-Bkh(|Vk,s(t)|-|Vh,s(t)|)-Gkh(θk,s(t)-θh,s(t)) (3)
in the formula pkh,s(t) and qkh,s(t) is the active and reactive flows between nodes k and h, respectively;
a general model of the operation of each plant is as follows:
Figure FDA0002829376340000021
in the formula Ek,iIs the energy storage capacity of the device; x is the number ofk,i,sIs a standardized energy storage level;
Figure FDA0002829376340000022
is the efficiency of the load and the generator; e is the same ask,i,s,tThe active power available or required, depending on the sign thereof; omegak,i,s(t) represents ∈k,i,s,tThe number of the reduction; v. ofk,iRepresents an energy storage loss; Δ t is the time step;
(1-3) device dependent constraint analysis
The active and reactive power provided by the equipment is limited by:
Figure FDA0002829376340000023
Figure FDA0002829376340000024
Figure FDA0002829376340000025
equations (8) - (10) define the active power injected by the device and limit the step α at each time respectivelyk,iAnd planning time domain betak,iThe amount of the steel can be reduced;
Figure FDA0002829376340000026
0≤αk,i|∈k,i,s,t|-|ωk,i,s(t)| (9)
Figure FDA0002829376340000027
by ensuring the required reactive power, assuming that the load power factor remains constant during load shedding
Figure FDA0002829376340000028
The load power factor is ensured to be constant and unchanged according to the proportional reduction of the active power; as shown in the following formula:
Figure FDA0002829376340000029
the equipment is also limited by the ability to climb; node power injection is the sum of the power injected by the devices at the node, equal to the net power flowing into/out of the node;
Figure FDA00028293763400000210
the equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity HcapStandardized storage of hydrogen hs；
Figure FDA0002829376340000031
Represents the sales volume of the hydrogen energy market,
Figure FDA0002829376340000032
it indicates whether the equipment is a hydrogen energy equipment;
equation (13) (14) indicates that the constraint limits the bids that can be placed by the emergency frequency control auxiliary service market equipment, taking into account the maximum equipment climbing capacity and the auxiliary service response time;
Figure FDA0002829376340000033
Figure FDA0002829376340000034
the frequency control auxiliary service herein refers specifically to an emergency frequency control auxiliary service used when the frequency is significantly changed, and the response time is the time required for the supplier to request the frequency control auxiliary service to reach its bid power output; the following constraints are introduced to ensure that these devices have sufficient energy margin to provide the relevant services;
Figure FDA0002829376340000035
Figure FDA0002829376340000036
3. the method of claim 1, wherein the method comprises: the step (2) is carried out according to the following steps:
the secondary optimization is mainly used for processing the economic dispatching problem, so that the SOC convex relaxation of the optimal power flow equation is used; this gives a more accurate flow equation for the model, including modeling the network loss; in order to maintain convexity, the standard in the formula (5) is replaced by convex quadratic constraint; for example, operation of capacitor bank, SOC convex relaxation using vkAs state variables, where vk＝|Vk|2(ii) a The voltage dependence of the reactive power output of the capacitor is given by equation (17); equation (18) limits the plant operating conditions for reactive power operation if the plant can only operate within a fixed power factor range;
Figure FDA0002829376340000037
Figure FDA0002829376340000038
in the formulak,iAnd
Figure FDA0002829376340000039
is the minimum and maximum allowable apparent power phase angle; when jointly optimizing active and reactive power, it is important to optimize the interaction between the variables; since the plan has been set in the first-level optimization, the on-off state of the device is a parameter rather than a decision variable, so both are convex constraints; the apparent power flow constraint can also be accurately modeled in convex optimization through convex quadratic constraint, wherein
Figure FDA00028293763400000310
Representing an apparent power limit;
Figure FDA00028293763400000311
the secondary optimization provides a set of operating points for the first time span and storage profile of all scenarios.
4. The method of claim 1, wherein the method comprises: the step (3) is carried out according to the following steps:
three-level optimization and two-level optimization based on VPP multi-market and local service scenes are similar, except that the cost function of the VPP multi-market and local service scenes optimizes the scene number S in the three levelsLLCalculating the minimum value, considering the deviation of the second-level optimization problem, and adding a penalty factor
Figure FDA0002829376340000041
Specifically, as shown in formula (23):
Figure FDA0002829376340000042
the three-level optimization is only within a planning range of 30 minutes, but penalty factors can prevent the three-level optimization problem from deviating too far from the daily optimal solution unless shutdown constraint conflicts exist, or if enough extra income exists, because the formula utilizes SOC relaxation of an OPF equation, a working point determined by the three-level optimization runs through a complete non-convex alternating current power flow, so that a feasible solution on the technology in a practical system is obtained; three-level optimization provides a set of market quotes that a VPP can complete in all scenarios.
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