CN112531703B - Optimization method for providing multiple markets and local services for multi-energy virtual power plant - Google Patents
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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
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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
- H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
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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
- 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
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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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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- Y02E40/30—Reactive power compensation
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Abstract
The invention discloses an optimization method for providing multi-market and local service for a multi-energy virtual power plant, and belongs to the technical field of power demand response. The invention mainly comprises the following steps: (1) The first-level optimization, converting the nonlinear and non-convexity 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) Secondary optimization, adopting 24 hours as a period, and re-scrolling and optimizing once every 30 minutes based on a scene by using real-time information; (3) And (3) three-stage optimization, namely adopting a second-order cone SOC convex relaxation based on an optimal power flow OFP equation for rolling optimization every 5 minutes, wherein the period is 30 minutes. The litigation method is based on a model of multi-market and local service scenes and rolling optimization, the VPP is modeled under the condition of considering uncertainty, and the optimized result relieves local network constraint and provides reactive support for a power grid.
Description
Technical Field
The invention discloses an optimization method for providing multi-market and local service for 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. Belongs to the technical field of power demand response.
Background
In recent years, the integration degree of Distributed Energy (DER) has been increasing, and renewable energy power generation has also been increasing. Many DERs currently incorporated into power distribution networks are too small to participate in the power market and thus represent unregulated power generation. Without proper control, these DERs can cause voltage and power flow problems and cannot be fully utilized due to network tension. An emerging method of DER aggregation that enables them to participate in the power market is through Virtual Power Plants (VPPs). By controlling and coordinating the DERs, the VPPs can participate in the power market and provide grid services. But this places high demands on the scheduling capabilities of the virtual power plant.
Disclosure of Invention
Technical problems: the invention aims to overcome the defects of the background technology, and provides an optimization method for providing multi-market and local service for a multi-energy virtual power plant, wherein the VPP is modeled under the condition of taking uncertainty into consideration based on a model of multi-market and local service scene and rolling optimization, and the optimized result relieves local network constraint and provides reactive support for a power grid.
The technical scheme is as follows: in order to achieve the above object, the present 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) The first-level optimization, converting the nonlinear and non-convexity 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 VPP multi-market and local service scenes;
(2) Secondary optimization, adopting 24 hours as a period, and re-scrolling and optimizing once every 30 minutes based on VPP multi-market and local service scenes by using real-time information;
(3) And (3) three-stage optimization, namely adopting a second-order cone SOC convex relaxation based on an optimal power flow OFP equation for rolling optimization every 5 minutes, wherein the period is 30 minutes.
Specifically, the step (1) includes the following steps:
(1-1) cost function analysis of optimization problem
The cost function of the first order optimization problem is as follows:
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being defined by pi s Given the corresponding components for weighting costs, S HL Is the number of scenes considered in the primary optimization stage. For each scene s, the corresponding running cost of the device is that
The cost of the device for load reduction is +.>
The cost/benefit of purchasing/selling active power from the grid is +.>
Just->
Representing cost, minus->
Indicating revenue. At the same time consider revenue +_ for providing reactive support of the grid for the upstream network>
Frequency control auxiliary service FCAS provisioning->
And sales hydrogen energy->
The reactive revenue section allows the VPP to inject/absorb reactive power when such support is required by the network operator by adjusting the reactive operating point of the device and taking into account the active/reactive power constraints of the device and the network. Linearization of OPF equations is used to trade computational ease for model accuracy, but as much as possibleIt is important to be able to maintain accuracy. Therefore, assumptions used in the linearization of the OPF equation must be carefully considered. Most OPF linearization methods consider transmission line level networks where the line reactance is much greater than its resistance. This allows the voltage amplitude of the bus bar |v k I is approximately 1pu, ignoring reactive power flow and modeling active power flow as a linear function of the voltage angle difference between two adjacent bus bars. In a distribution network, these assumptions are invalid. In order to capture reactive power more accurately, a model that considers both line conductance and susceptance, voltage magnitude and voltage phase angle should be employed.
(1-2) Optimization of Power Flow (OPF) equation linearization model analysis
Linearizing an OPF equation in the primary optimization, wherein the active and reactive power flow equations are as follows:
p kh，s (t)＝G kh (|V k，s (t)|-|V h，s (t)|)-B kh (θ k，s (t)-θ h，s (t)) (2)
q kh，s (t)＝-B kh (|V k，s (t)|-|V h，s (t)|)-G kh (θ k，s (t)-θ h，s (t)) (3)
in p kh，s (t) and q kh，s (t) is the active and reactive flows between nodes k and h, respectively.
The general model of each device operation is as follows:
in E k，i Is the energy storage capacity of the device; x is x k，i，s Is a standardized energy storage stage;
is the load and generator efficiency; e-shaped article k，i，s，t Is available orThe active power required depends on the sign of the active power; omega k，i，s (t) represents E k，i，s，t The number of the cutting; v k，i Representing energy storage losses;
Δt is the time step;
(1-3) device-dependent constraint analysis
The active and reactive power provided by the device is limited by:
equations (8) - (10) define the active power injected by the device and limit each time step α, respectively k，i Planning time domain beta k，i Is reduced by a reduced amount.
0≤α k，i |∈ k，i，s，t |-|ω k，i，s (t)| (9)
By ensuring the required reactive power, provided that the load power factor remains unchanged during load shedding
The load power factor is ensured to be constant according to the proportion reduction of the active power. The following formula is shown:
the device may also be limited in its ability to climb a slope. 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.
The equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity H cap Standardized hydrogen storage h s 。
Represents sales of hydrogen energy market, +.>
It indicates whether the device is a hydrogen energy device.
Equation (13) (14) shows that this constraint limits the bids that can be placed by the emergency frequency control auxiliary service market devices, which takes into account the device's maximum climbing capacity and auxiliary service response time.
The frequency control auxiliary service herein refers specifically to an emergency frequency control auxiliary service used when a significant change in frequency occurs, and the response time is the time required for the provider to request the frequency control auxiliary service to reach its bidding power output. The following constraints are introduced to ensure that these devices have sufficient energy margin to provide relevant services.
Specifically, the step (2) includes the following steps:
the second level optimization is mainly used for dealing with economic dispatch problems, so that the method uses SOC convex relaxation of the optimal power flow equation. This gives a more accurate model of the flow equation, including modeling the net losses. In order to maintain convexity, the criterion in equation (5) is changed to convex quadratic constraint. For example, the capacitor bank operates, and the SOC convex relaxation is utilized by v k As state variables, where v k ＝|V k | 2 . The voltage dependence of the reactive power output of the capacitor is available from equation (17). Equation (18) limits the operating state of the reactive power operated plant if the plant can only be operated within a fixed power factor range.
Phi in k，i And
is the minimum and maximum allowed apparent power phase angle. When optimizing active and reactive power together, it is important to optimize the interaction between the variables. Since the schedule is already set in the first level of optimization, the on-off state of the device is a parameter rather than a decision variable, so both are convex constraints. Apparent power flow constraints can also be modeled accurately in convex optimization by convex quadratic constraints, where
The secondary optimization provides a set of operating points for the first time span and storage profile of all scenarios.
Specifically, the step (3) includes 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 is implemented in three-level optimization scene number S LL The minimum value is calculated, and a penalty factor is added in consideration of the deviation of the secondary optimization problem
Specifically, the formula (20) is as follows:
the three-level optimization has a planning horizon of only 30 minutes, but the penalty factor may prevent the three-level optimization problem from deviating too far from the daily optimal solution unless shutdown constraints conflict, or if there is sufficient additional revenue. Because the formula utilizes the SOC relaxation of the OPF equation, the working point determined by three-stage optimization operates through a complete non-convex alternating current power flow, thereby obtaining a technically feasible solution in a practical system. The three-level optimization provides a set of market offers that the VPP can complete in all scenarios.
The beneficial effects are that: aiming at the problems that the uncertainty of the demand and the intermittence of the DER require the OPF to solve in a smaller time interval, the invention provides an optimization method for providing multi-market and local service for a multi-energy virtual power plant, and based on a model of multi-market and local service scene and rolling optimization, the VPP is modeled under the condition of considering the uncertainty, and the optimized result relieves local network constraint and provides reactive support for a power grid.
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Fig. 1 is a general flow chart of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings.
FIG. 1 shows an optimization method for providing multiple markets and local services for a multi-energy virtual power plant, which solves the uncertainty problem based on multiple markets and local service scenes and a rolling optimization model. The steps are specifically described below.
Step one: and (3) primary optimization, namely converting the nonlinear and non-convexity 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 the VPP multi-market and local service scene.
(1-1) cost function analysis of optimization problem
The cost function of the first order optimization problem is shown in the following diagram:
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being defined by pi s Given the corresponding components for weighting costs, S HL Is the number of scenes considered in the primary optimization stage. For each scene s, the corresponding running cost of the device is that
The cost of the device for load reduction is +.>
The cost/benefit of purchasing/selling active power from the grid is +.>
Just->
Representing cost, minus->
Indicating revenue. At the same time consider revenue +_ for providing reactive support of the grid for the upstream network>
Frequency control auxiliary service FCAS provisioning->
And sales hydrogen energy->
The reactive revenue section allows the VPP to inject/absorb reactive power when such support is required by the network operator by adjusting the reactive operating point of the device and taking into account the active/reactive power constraints of the device and the network. Linearization of the OPF equation is used to trade computational ease for model accuracy, but it is important to maintain accuracy as much as possible. Therefore, assumptions used in the linearization of the OPF equation must be carefully considered. Most OPF linearization methods consider transmission line level networks where the line reactance is much greater than its resistance. This allows the voltage amplitude of the bus bar |v k I is approximately 1pu, ignoring reactive power flow and modeling active power flow as a linear function of the voltage angle difference between two adjacent bus bars. In a distribution network, these assumptions are invalid. In order to capture reactive power more accurately, a model that considers both line conductance and susceptance, voltage magnitude and voltage phase angle should be employed.
(1-2) optimization of Power flow OPF equation linearization model analysis
Linearizing an OPF equation in the primary optimization, wherein the active and reactive power flow equations are as follows:
p kh，s (t)＝G kh (|V k，s (t)|-|V h，s (t)|)-B kh (θ k，s (t)-θ h，s (t)) (2)
q kh，s (t)＝-B kh (|V k，s (t)|-|V h，s (t)|)-G kh (θ k，s (t)-θ h，s (t)) (3)
in p kh，s (t) and q kh，s (t) is the active and reactive flows between nodes k and h, respectively.
The general model of each device operation is as follows:
in E k，i Is the energy storage capacity of the device; x is x k，i，s Is a standardized energy storage stage;
is the load and generator efficiency; e-shaped article k，i，s，t Active power, whether available or required, depends on the sign of the active power; omega k，i，s (t) represents E k，i，s，t The number of the cutting; v k，i Representing energy storage losses; Δt is the time step;
(1-3) device-dependent constraint analysis
The active and reactive power provided by the device is limited by:
equations (8) - (10) define the active power injected by the device and limit each time step α, respectively k，i Planning time domain beta k，i Can be reduced in (2)Amount of the components.
0≤α k，i ∈ k，i，s，t |-|ω k，i，s (t)| (9)
By ensuring the required reactive power, provided that the load power factor remains unchanged during load shedding
The load power factor is ensured to be constant according to the proportion reduction of the active power. The following formula is shown:
the device may also be limited in its ability to climb a slope. 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.
The equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity H cap Standardized hydrogen storage h s 。
Represents sales of hydrogen energy market, +.>
It indicates whether the device is a hydrogen energy device.
Equation (13) (14) shows that this constraint limits the bids that can be placed by the emergency frequency control auxiliary service market devices, which takes into account the device's maximum climbing capacity and auxiliary service response time.
The frequency control auxiliary service herein refers specifically to an emergency frequency control auxiliary service used when a significant change in frequency occurs, and the response time is the time required for the provider to request the frequency control auxiliary service to reach its bidding power output. The following constraints are introduced to ensure that these devices have sufficient energy margin to provide relevant services.
Step two: and (3) secondary optimization, wherein the period is 24 hours, and real-time information is used for scrolling and optimizing again every 30 minutes based on the VPP multi-market and local service scene.
The second level optimization is mainly used for dealing with economic dispatch problems, so that the method uses SOC convex relaxation of the optimal power flow equation. This gives a more accurate model of the flow equation, including modeling the net losses. In order to maintain convexity, the criterion in equation (5) is changed to convex quadratic constraint. For example, the capacitor bank operates, and the SOC convex relaxation is utilized by v k As state variables, where v k ＝|V k | 2 . The voltage dependence of the reactive power output of the capacitor is available from equation (17). Equation (18) limits the operating state of the reactive power operated plant if the plant can only be operated within a fixed power factor range.
Phi in k，i And
is the minimum and maximum allowed apparent power phase angle. When optimizing active and reactive power together, it is important to optimize the interaction between the variables. Since the schedule is already set in the first level of optimization, the on-off state of the device is a parameter rather than a decision variable, so both are convex constraints. Apparent power flow constraints can also be modeled accurately in convex optimization by convex quadratic constraints, where
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, namely adopting a second-order cone SOC convex relaxation based on an optimal power flow OFP equation for rolling optimization every 5 minutes, wherein the period is 30 minutes.
The scene-based three-level optimization is similar to the two-level optimization except that the cost function optimizes the scene number S in three levels LL The minimum value is calculated, and a penalty factor is added in consideration of the deviation of the secondary optimization problem
Specifically as shown in (20)
The three-level optimization has a planning horizon of only 30 minutes, but the penalty factor may prevent the three-level optimization problem from deviating too far from the daily optimal solution unless shutdown constraints conflict, or if there is sufficient additional revenue. Because the formula utilizes the SOC relaxation of the OPF equation, the working point determined by three-stage optimization operates through a complete non-convex alternating current power flow, thereby obtaining a technically feasible solution in a practical system. The three-level optimization provides a set of market offers that the VPP can complete in all scenarios.
The foregoing is only a preferred embodiment of the invention, it being 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 present invention, and such modifications and adaptations are intended to be comprehended within the scope of the invention.
Claims (1)
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) The first-level optimization, converting the nonlinear and non-convexity 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 VPP multi-market and local service scenes; 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 as follows:
it is based on the cost of each scene considered, the probability of each scene occurring in the time range t being defined by pi s Given the corresponding components for weighting costs, S HL The number of scenes considered in the primary optimization stage; for each scene s, the corresponding running cost of the device is that
The cost of the device for load reduction is +.>
The cost/benefit of purchasing/selling active power from the grid is +.>
Just->
Representing cost, minus->
Representing revenue; while taking into account revenue for providing reactive power support of the grid for the upstream network
(1-2) optimization of Power flow OPF equation linearization model analysis
Linearizing an OPF equation in the primary optimization, wherein the active and reactive power flow equations are as follows:
p kh，s (t)＝G kh (|V k，s (t)|-|V h，s (t)|)-B kh (θ k，s (t)-θ h，s (t)) (2)
q kh，s (t)＝-B kh (|V k，s (t)-|V h，s (t)|)-G kh (θ k，s (t)-θ h，s (t)) (3)
in p kh，s (t) and q kh，s (t) active and reactive flows between nodes k and h, respectively;
the general model of each device operation is as follows:
in E k，i Is the energy storage capacity of the device; x is x k，i，s Is a standardized energy storage stage;
is the load and generator efficiency; e-shaped article k，i，s，t Active power, whether available or required, depends on the sign of the active power; omega k，i，s (t) represents E k，i，s，t The number of the cutting; v k，i Representing energy storage losses; Δt is the time step;
(1-3) device-dependent constraint analysis
The active and reactive power provided by the device is limited by:
equations (8) - (10) define the active power injected by the device and limit each time step α, respectively k，i Planning time domain beta k，i Is a reducible amount of (2);
0≤α k，i |∈ k，i，s，t |-|ω k，i，s (t) |(9)
by ensuring the required reactive power, provided that the load power factor remains unchanged during load shedding
The constant load power factor is ensured according to the proportion reduction of the active power; the following formula is shown:
the equipment is also limited by the ability to climb a slope; 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;
the equation is derived from equation (4) and simulates a single concentrated hydrogen energy storage capacity H cap Standardized hydrogen storage h s ；
Represents sales of hydrogen energy market, +.>
Indicating whether the device is a hydrogen energy device;
equation (13) (14) shows that this constraint limits the bids that can be made by the emergency frequency control auxiliary service market device, which takes into account the device maximum climbing capability and auxiliary service response time;
the frequency control auxiliary service here refers to an emergency frequency control auxiliary service used when the frequency is changed significantly, and the response time is the time required for the provider to request the frequency control auxiliary service to reach the bidding power output; introducing the following constraints ensures that these devices have sufficient energy margin to provide relevant services;
(2) Secondary optimization, adopting 24 hours as a period, and re-scrolling and optimizing once every 30 minutes based on VPP multi-market and local service scenes by using real-time information; the step (2) is carried out according to the following steps:
the second-level optimization is mainly used for treating economic dispatch problems, so that the method uses SOC convex relaxation of an optimal power flow equation; this gives a more accurate model of the flow equation, including modeling the net loss; in order to maintain convexity, the standard in formula (5) is changed to convex quadratic constraint; for example, the capacitor bank operates, and the SOC convex relaxation is utilized by v k As state variables, where v k ＝|V k | 2 The method comprises the steps of carrying out a first treatment on the surface of the The voltage dependence of the reactive power output of the capacitor is obtainable by equation (17); equation (18) limits the operating state of the reactive power operated device if the device can only operate within a fixed power factor range;
phi in k，i And
is the minimum and maximum allowed apparent power phase angle; when optimizing active and reactive power together, it is important to optimize the interaction between the variables; since the plan has been set in the first level of 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 modeled accurately in convex optimization by convex quadratic constraint, wherein +.>
Representing an apparent power limit;
the second level of optimization provides a set of operating points for the first time span and storage profile of all scenes;
(3) Three-stage optimization, namely performing rolling optimization every 5 minutes by adopting a Second Order Cone (SOC) convex relaxation based on an Optimal Power Flow (OPF) equation with a period of 30 minutes; 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 is implemented in three-level optimization scene number S LL The minimum value is calculated, and a penalty factor is added in consideration of the deviation of the secondary optimization problem
Specifically, the formula (20) is as follows:
the three-level optimization has a planning range of only 30 minutes, but the penalty factor can prevent the three-level optimization problem from deviating from the daily optimal solution too far unless the shutdown constraint conflicts, or if there is enough additional benefit because the formula utilizes the SOC relaxation of the OPF equation, the working point determined by the three-level optimization operates through a complete non-convex alternating current tide, thereby obtaining a technically feasible solution in a practical system; the three-level optimization provides a set of market offers that the VPP can complete in all scenarios.
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| CN104993522B (en) * | 2015-06-30 | 2018-01-19 | 中国电力科学研究院 | A kind of active distribution network Multiple Time Scales coordination optimization dispatching method based on MPC |
| CN109002913A (en) * | 2018-07-10 | 2018-12-14 | 河海大学 | A kind of more virtual plant joint optimal operation methods |
| CN110416998B (en) * | 2019-07-01 | 2021-01-08 | 华北电力大学 | Regional complex distribution network scheduling control management system based on virtual power plant |
| CN110581571A (en) * | 2019-08-29 | 2019-12-17 | 昆明理工大学 | dynamic optimization scheduling method for active power distribution network |
| CN110620380B (en) * | 2019-09-05 | 2023-06-20 | 深圳供电局有限公司 | Virtual power plant control system |
| CN111080022A (en) * | 2019-12-23 | 2020-04-28 | 国网四川省电力公司经济技术研究院 | Partition distributed coordination optimization method containing multiple benefit agents |
| CN111934360B (en) * | 2020-07-09 | 2021-08-31 | 浙江浙能技术研究院有限公司 | Virtual power plant-energy storage system energy collaborative optimization regulation and control method based on model predictive control |
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