CN115994656A - Virtual power plant economic dispatching method considering excitation demand response under time-of-use electricity price - Google Patents
Virtual power plant economic dispatching method considering excitation demand response under time-of-use electricity price Download PDFInfo
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Abstract
The invention belongs to the technical field of virtual power plant economic dispatch, and particularly relates to a virtual power plant economic dispatch method for considering excitation demand response under time-of-use electricity price; predicting wind/photovoltaic power generation capacity in the future 24H through historical data by a wind/photovoltaic power generation enterprise, and reporting the predicted power generation capacity to a virtual power plant; step 2, CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company; step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method; the charging requirement of the electric vehicle charging station, the output of the wind-solar power generation system and the energy storage of the energy storage system are considered, the output of the distributed power supply is reasonably predicted, the power requirement of each period is matched, the user requirement response is explored through a market mechanism in an excitation mode, and the aims of energy conservation and emission reduction, peak clipping and valley filling, the maximization of the new energy power generation utilization rate and the optimal economic benefit of the virtual power plant are better achieved.
Description
Technical Field
The invention belongs to the technical field of virtual power plant economic dispatch, and particularly relates to a virtual power plant economic dispatch method for considering excitation demand response under time-of-use electricity price.
Background
With the gradual prominence of non-renewable energy shortage and environmental pollution caused by the traditional power generation technology using fossil energy as a main raw material, a distributed power supply (distributed generator, DG) mainly using renewable energy power generation mode has been widely focused and applied by the advantages of economy, environmental protection, flexibility and the like, but single DG has small capacity, power generation has intermittence and randomness, and the operation of independently adding into the power market cannot be realized. The DG is aggregated into a whole to participate in the operation of the power system and the operation of the power market in the form of a virtual power plant, so that a new idea is provided for solving the problems.
The wind power, photovoltaic, energy storage and electric vehicle charging stations are aggregated through a regulating and controlling center in the CVPP and by means of a communication technology, and the electric vehicle charging stations participate in power grid market trading and power grid operation in an integral mode. In grid operation, peak demand during peak hours will put no little stress on the power generation side, while direct load shedding approaches ignore the benefits and comfort on the user side. The demand response is used as an important resource in the power grid economic dispatching, and the adjustable load concept is introduced into the power grid to participate in the economic dispatching together under the time-of-use electricity price mechanism, so that the peak load shifting and optimization dispatching means can be increased, the benefit of a user side can be improved, and the economical efficiency of the virtual power plant can be improved.
In the traditional virtual power plant scheduling method, the reasonable prediction of the output of the distributed power supply is not considered, and the capability of actively reducing and transferring the power load on the user demand side is not well developed under the actions of a market mechanism and price guidance; therefore, it is necessary to provide a virtual power plant economic dispatching method for considering the excitation demand response under the time-of-use electricity price under the premise of considering the charging demand of an electric vehicle charging station, the output of a wind-solar power generation system and the energy storage of an energy storage system.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a virtual power plant economic dispatching method taking into account excitation demand response under time-of-use electricity price, which considers charging demands of electric vehicle charging stations, output of a wind-solar power generation system and energy storage of an energy storage system, reasonably predicts distributed power output, better matches the power demands of each period, discovers user demand response through a market mechanism in an excitation form, and better achieves the aims of energy conservation and emission reduction, peak clipping and valley filling, maximization of new energy power generation utilization and optimal economic benefit of a virtual power plant.
The purpose of the invention is realized in the following way: the virtual power plant economic dispatching method for considering the excitation demand response under the time-of-use electricity price comprises the following steps:
step 1, predicting wind/light generating capacity in the future 24H through historical data by a wind/light generating enterprise, and reporting the predicted generating capacity to a virtual power plant;
step 2, the CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company;
and step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method.
Step 1, predicting wind/photovoltaic power generation capacity in the future 24H through historical data by a wind/photovoltaic power generation enterprise, and reporting the predicted power generation capacity to a virtual power plant comprises the following steps:
the wind speed and the illumination intensity are predicted by adopting a three-time exponential smoothing method (Holt-windows), and the three-time exponential smoothing prediction method comprises the following formula group:
T j ＝β·(N j -N j-1 )+(1-β)·T j-1 (2)
wherein N is j For the estimated smooth value of the hierarchy, T j As a predicted smoothed value of trend, S j For the estimated smooth value of seasons, j is the current moment point, P j-1 For the load value of the previous point in time, α, β, γ represent the level, trend, and season smoothing factors (0<α、β、γ<1) P is the seasonal period, P j-p The load value of the last seasonal period;
load predictive value P of future q time point j+q The method comprises the following steps:
P j+q ＝(T j +N j ·q)·S j-p+q (4)
wherein P is j+q For the predicted value of the future q time points, q is the number of the predicted time points, p is the seasonal period, S j-p+q The estimated smooth value of the q moment point after the last season period is obtained.
Step 2CVPP determines the load adjustable capacity of the electric automobile according to the load baseline of the electric automobile, integrates the prediction of the new energy power generation capacity and gives a next day scheduling strategy, and sends the scheduling strategy to each contracted user and the power grid company, wherein the step 2CVPP comprises the following steps:
and taking the optimal economic benefit of the virtual power plant as a target, and taking the demand response excitation compensation cost, the operation and maintenance cost of an energy storage battery and wind-light power generation, the electricity selling benefit of the virtual power plant and the subsidy benefit obtained from a power grid into consideration, and establishing a virtual power plant scheduling optimization model according to the predicted force and constraint conditions of wind/light power generation enterprises, wherein the constraint conditions comprise constraint of wind-light power generation operation, constraint of maximum electric quantity of electricity purchase, selling and energy storage to the power grid, constraint conditions of an energy storage system, power balance constraint, constraint of excitation demand response load transfer and constraint of excitation demand response load reduction.
The demand response incentive compensation cost is as follows:
wherein C is t Compensating costs for demand response incentives;
and->
Load reduction and load transfer costs to be paid to users by the virtual power plant in the t period respectively; n (N) T All time period sets for bid decision optimization are set to 24 time periods, and each time period is 1h; />
Reducing the load amount for the r-th load and the load reduced at the time t; />
Compensating the price for the t period specified by the r-th curtailment contract; />
The initial cost which is actually required to be paid by the contract at the time t is reduced for the r load; reducing contract State index->
Representing the state of contract r execution by binary numbers;
the operation and maintenance cost of the energy storage battery is as follows:
wherein:
operating and maintaining cost for the energy storage battery in the t-th period; beta is the running cost coefficient of the energy storage battery; />
The energy storage battery plans to charge and discharge electric quantity within unit time t; alpha is the maintenance cost coefficient of the energy storage battery; />
Rated power of the energy storage battery;
operation maintenance cost of wind-solar power generation:
wherein:
the maintenance cost of the wind turbine generator and the photovoltaic turbine generator is the t-th period; lambda (lambda) w Maintaining coefficients for the operation of the fan; />
Generating energy of the wind turbine generator set in the t-th period; lambda (lambda) pv Maintaining coefficients for operation of photovoltaic power generation; />
For the t-th period of lightGenerating capacity of the photovoltaic power generation;
the electricity selling benefit of the virtual power plant is as follows:
wherein:
the electricity purchasing benefit of the virtual power plant in the t-th period; />
Purchasing and selling electric quantity to a large power grid in a t period for the virtual power plant; />
Purchasing electricity price for the virtual power plant in the period t; />
Generating energy of the wind turbine generator set in the t-th period; />
Generating capacity of photovoltaic power generation for the t-th period;
demand response subsidies obtained by the virtual power plant from the power grid:
wherein: f (f) t e Benefit is subsidized for the demand response of the virtual power plant from the power grid in the t-th period; l (L) t lc Reducing the load for the t period; p is p t r Executing the subsidy price of the demand response for the virtual power plant in the t period; l (L) t ls1 Performing load transfer for the virtual power plant during a t-th period; l (L) t ls2 Performing load transfer to the amount of power of the ith period for the virtual power plant; p (P) i r The subsidy price of the demand response is executed for the i-th period.
The virtual power plant dispatching optimization model is built according to the demand response excitation compensation cost, the operation and maintenance cost of the energy storage battery and wind-solar power generation, the virtual power plant electricity selling benefit and the subsidy benefit obtained from the power grid, and an objective function of the virtual power plant economic optimal dispatching model is built according to the following formula.
The constraint of wind-solar power generation operation is as follows:
0≤P t w ≤P t wB (13)
0≤P t pv ≤P t pvB (14)
wherein: p (P) t w The effective generating capacity of the wind turbine generator set in the t-th period; p (P) t pv The effective power generation amount of the photovoltaic power station in the t-th period;
the maximum electric quantity constraint of electricity purchase and selling and energy storage to the power grid is as follows:
P G_min ≤P t G ≤P G_max (15)
P Ba_min ≤P t Ba ≤P Ba_max (16)
wherein: p (P) t G The virtual power plant purchases electricity to the power grid for the t-th period; p (P) G_min The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; p (P) G_max The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; p (P) t Ba The electric quantity stored or discharged by the energy storage system in the t time period; p (P) Ba min Minimum electric quantity stored or released for the energy storage system; p (P) Ba_max Maximum amount of electricity stored or released for the energy storage system;
the energy storage system constraint conditions are as follows:
SOC _min ≤SOC t ≤SOC _max (18)
wherein: SOC (State of Charge) t The state of charge of the energy storage system is the t-th period; e (E) Ba The method comprises the steps of storing electric quantity for an energy storage system initially; SOC (State of Charge) _min Setting the minimum charge state of the energy storage system to 0.2 to prevent the energy storage system from overdischarging; SOC (State of Charge) _max Setting the maximum charge state of the energy storage system to be 0.9 to prevent the energy storage system from being overcharged;
power balance constraint:
in order to ensure the regulated and stable operation of the virtual power plant, the sum of the output of the renewable energy power generation, the energy storage power supply and the power supply of the power grid in the system and the sum of the access loads of the user side are balanced, and the system is characterized in that
Wherein:
accessing an aggregated load quantity of a virtual power plant to an electric automobile user;
the incentive demand is responsive to load transfer constraints:
wherein: p (P) LS0 Initial cost for load transfer;
transferring a cost coefficient for demand response load; m is m t ls And n t ls A start index and an end index of the load transfer contract at the t period are respectively indicated by 1 and 0, and the load transfer starts or ends at the t period; l (L) t ls1 And L is equal to t ls2 The load amounts before and after load transfer in the t period are respectively; d (D) min ls An upper and lower limit for the duration of the load transfer; t (T) ls1 A period of time during which the load is transferable;
equation (20) is an initial cost constraint; formula (21) is a transfer load conservation constraint; equation (22) represents a maximum transfer time and a minimum transfer time constraint, respectively; equation (23) represents load transfer execution state constraints and their inability to start and end at the same time, respectively; the formula (24) represents that the load can be only from T ls1 The period shifts to other periods;
excitation demand response load shedding constraints:
P LC0 ≥φ×m lc (25)
formulas (25) to (28) are the same as those of (20), (22) and (23); p in the formula LC0 Initial cost for load shedding; phi is a cost coefficient of reducing the demand response load; m is m t lc And n t lc A start index and an end index of the load shedding contract at the t period are respectively represented, and the load shedding starts or ends at the t period are respectively represented by 1 and 0; k (k) min And k max The minimum and maximum times of load shedding are performed respectively.
The operation strategy of the virtual power plant comprises the following steps: the power generation side virtual power plant comprises an uncontrollable DG, a large power grid and an energy storage system; the energy storage system has the characteristic of transferring electric quantity, after time-of-use electricity price is implemented, the VPP transfers the low-price electric quantity of the power distribution network to the high price of the power distribution network through the energy storage system to sell, so that benefits are obtained; the implementation of the time-sharing electricity price stimulates the VPP to adjust the power generation plan, so that the power generation cost is reduced, and the power distribution network takes part in peak clipping and valley filling to obtain better benefits;
uncontrollable DG operating strategy: wind power generation and photovoltaic power generation are used as new energy power grid connection, and VPP is preferably utilized within the capacity range;
on the basis of the prior utilization of new energy power, the operation strategy of the electric power market transaction under the condition that the large power grid gives consideration to power supply on the power generation side and time-of-use electricity price is as follows: 1) The electricity purchasing cost is lower than electricity selling price, and VPP purchases electricity to the power grid; 2) The electricity purchasing cost is higher than the electricity selling price, and VPP sells electricity to the power grid; 3) When uncontrollable DG generates more power and the energy storage system cannot be completely consumed, VPP sells electricity to the power grid;
energy storage system operation strategy:
1) The battery stores energy, purchases electricity and stores energy when the time-sharing electricity price is low, sells electricity when the time-sharing electricity price is high, obtains economic benefit and plays a role in peak clipping and valley filling;
2) When uncontrollable DG power generation meets load requirements and is available, the energy storage system stores electric energy to improve the capacity of the system for new energy power generation.
The invention has the beneficial effects that: according to the virtual power plant economic dispatching method considering the excitation demand response under the time-of-use electricity price, the wind/light power generation amount in the future 24H is predicted through historical data by a wind/light power generation enterprise, and the predicted power generation amount is reported to the virtual power plant; step 2, CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company; step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method; the charging requirement of the electric vehicle charging station, the output of the wind-solar power generation system and the energy storage of the energy storage system are considered, the output of the distributed power supply is reasonably predicted, the power requirement of each period is better matched, the user requirement response is explored through a market mechanism in an excitation mode, and the aims of energy conservation, emission reduction, peak clipping, valley filling, maximization of the new energy power generation utilization rate and optimal economic benefit of the virtual power plant are better achieved.
Drawings
FIG. 1 is a flow chart of the virtual power plant economic dispatch method taking into account the excitation demand response under the time-of-use electricity price of the invention.
FIG. 2 is a schematic flow chart of a virtual power plant scheduling optimization model solving in consideration of excitation demand response under time-of-use electricity price.
FIG. 3 is a schematic diagram of a demand response load curve.
FIG. 4 is a schematic diagram of the operating state of each unit of the virtual power plant.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
According to the invention, wind power, photovoltaic, energy storage and electric vehicle charging stations are aggregated through a regulating and controlling center in the CVPP and by utilizing a communication technology, and the integrated electric vehicle charging stations participate in electric network market trading and electric network operation.
As shown in FIG. 1, the virtual power plant economic dispatch method for considering the excitation demand response under the time-of-use electricity price comprises the following steps:
step 1, predicting wind/light generating capacity in the future 24H through historical data by a wind/light generating enterprise, and reporting the predicted generating capacity to a virtual power plant;
step 2, the CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company;
and step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method.
Wind power and photovoltaic output conditions are greatly influenced by external environments, so that it is very important to predict the change conditions of the external environments. The wind power station mainly predicts the wind speed change condition of the wind power station, and the photovoltaic power station mainly predicts the illumination change condition in the photovoltaic power station, which is realized by a proper prediction method.
Step 1, predicting wind/photovoltaic power generation capacity in the future 24H through historical data by a wind/photovoltaic power generation enterprise, and reporting the predicted power generation capacity to a virtual power plant comprises the following steps:
the wind speed and the illumination intensity are predicted by adopting a three-time exponential smoothing method (Holt-windows), and the three-time exponential smoothing method can give consideration to the layering, the trending and the seasonality of the time sequence and can predict the time sequence with nonlinear variation trend; the three-order exponential smoothing prediction method formula set is as follows:
T j ＝β·(N j -N j-1 )+(1-β)·T j-1 (2)
wherein N is j For the estimated smooth value of the hierarchy, T j As a predicted smoothed value of trend, S j For the estimated smooth value of seasons, j is the current moment point, P j-1 For the load value of the previous point in time, α, β, γ represent the level, trend, and season smoothing factors (0<α、β、γ<1) P is the seasonal period, P j-p The load value of the last seasonal period;
load predictive value P of future q time point j+q The method comprises the following steps:
P j+q ＝(T j +N j ·q)·S j-p+q (4)
wherein P is j+q For the predicted value of the future q time points, q is the number of the predicted time points, p is the seasonal period, S j-p+q For the last oneThe estimated smoothed value of the q time points after the seasonal period.
Through carrying out the aggregate analysis to hybrid electric automobile according to its market share proportion, find that electric automobile's real-time power distribution is roughly similar with resident's travel time, and resident's of working at night concentrate and return home, and charge power and charge electric quantity fast increase has further increased the peak valley difference of using electricity, and electric automobile load has can cut down and transfer characteristic, consequently considers the demand response and is favorable to realizing peak clipping and filling out the valley, helps virtual power plant to obtain bigger benefit.
The demand response utilizes the demand side resource as an alternative resource of the supply side electric energy, the effect of the demand side in the market is increased through the price signal and the incentive mechanism, the user base line load can quantitatively evaluate the reduction degree of the user load in various electric power demand response projects, the demand response is an important precondition for compensating and settling the user, and an evaluation basis is provided for the demand response projects based on the incentive.
The invention provides a decision optimization scheme combining baseline load prediction by a three-time smooth exponential method and two demand responses based on load reduction and load transfer: performing excitation settlement according to the execution conditions of the base line load and the decision result, making a demand response scheme with the maximization of self-income as a target, and enabling the virtual power plant to replace a user to sign a contract with the electric power market to specify the time and capacity of load reduction and load transfer; the user reduces the electricity consumption or changes the electricity consumption time within the period specified by the contract, thereby obtaining corresponding economic incentives.
Step 2CVPP determines the load adjustable capacity of the electric automobile according to the load baseline of the electric automobile, integrates the prediction of the new energy power generation capacity and gives a next day scheduling strategy, and sends the scheduling strategy to each contracted user and the power grid company, wherein the step 2CVPP comprises the following steps:
and taking the optimal economic benefit of the virtual power plant as a target, and taking the demand response excitation compensation cost, the operation and maintenance cost of an energy storage battery and wind-light power generation, the electricity selling benefit of the virtual power plant and the subsidy benefit obtained from a power grid into consideration, and establishing a virtual power plant scheduling optimization model according to the predicted force and constraint conditions of wind/light power generation enterprises, wherein the constraint conditions comprise constraint of wind-light power generation operation, constraint of maximum electric quantity of electricity purchase, selling and energy storage to the power grid, constraint conditions of an energy storage system, power balance constraint, constraint of excitation demand response load transfer and constraint of excitation demand response load reduction.
The demand response incentive compensation cost is as follows:
wherein C is t Compensating costs for demand response incentives; c (C) t LC And C t LS Load reduction and load transfer costs to be paid to users by the virtual power plant in the t period respectively; n (N) T All time period sets for bid decision optimization are set to 24 time periods, and each time period is 1h; q rt LC Reducing the load amount for the r-th load and the load reduced at the time t; p is p rt LC Compensating the price for the t period specified by the r-th curtailment contract; p is p rt LC0 The initial cost which is actually required to be paid by the contract at the time t is reduced for the r load; reducing contract State index I rt LC Representing the state of contract r execution by binary numbers;
the operation and maintenance cost of the energy storage battery is as follows:
wherein: c (C) t dy Operating and maintaining cost for the energy storage battery in the t-th period; beta is the running cost coefficient of the energy storage battery; p (P) t Ba The planned charge and discharge electric quantity of the energy storage battery in unit time tThe method comprises the steps of carrying out a first treatment on the surface of the Alpha is the maintenance cost coefficient of the energy storage battery; e (E) e Ba Rated power of the energy storage battery;
operation maintenance cost of wind-solar power generation:
wherein:
the maintenance cost of the wind turbine generator and the photovoltaic turbine generator is the t-th period; lambda (lambda) w Maintaining coefficients for the operation of the fan; />
Generating energy of the wind turbine generator set in the t-th period; lambda (lambda) pv Maintaining coefficients for operation of photovoltaic power generation; />
Generating capacity of photovoltaic power generation for the t-th period;
the electricity selling benefit of the virtual power plant is as follows:
wherein:
the electricity purchasing benefit of the virtual power plant in the t-th period; />
Purchasing and selling electric quantity to a large power grid in a t period for the virtual power plant; />
Purchasing electricity price for the virtual power plant in the period t; />
Generating energy of the wind turbine generator set in the t-th period; />
Generating capacity of photovoltaic power generation for the t-th period;
demand response subsidies obtained by the virtual power plant from the power grid:
wherein:
benefit is subsidized for the demand response of the virtual power plant from the power grid in the t-th period; />
Reducing the load for the t period; />
Executing the subsidy price of the demand response for the virtual power plant in the t period; />
Performing load transfer for the virtual power plant during a t-th period; />
Performing load transfer to the amount of power of the ith period for the virtual power plant; />
The subsidy price of the demand response is executed for the i-th period.
The virtual power plant dispatching optimization model is built according to the demand response excitation compensation cost, the operation and maintenance cost of the energy storage battery and wind-solar power generation, the virtual power plant electricity selling benefit and the subsidy benefit obtained from the power grid, and an objective function of the virtual power plant economic optimal dispatching model is built according to the following formula.
The constraint of wind-solar power generation operation is as follows:
wherein:
the effective generating capacity of the wind turbine generator set in the t-th period; />
The effective power generation amount of the photovoltaic power station in the t-th period;
the maximum electric quantity constraint of electricity purchase and selling and energy storage to the power grid is as follows:
wherein:
the virtual power plant purchases electricity to the power grid for the t-th period; p (P) G_min The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; p (P) G_max The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; />
Stored or discharged electricity for a time period t energy storage systemAn amount of; p (P) Ba_min Minimum electric quantity stored or released for the energy storage system; p (P) Ba_max Maximum amount of electricity stored or released for the energy storage system;
the energy storage system constraint conditions are as follows:
SOC _min ≤SOC t ≤SOC _max (18)
wherein: SOC (State of Charge) t The state of charge of the energy storage system is the t-th period; e (E) Ba The method comprises the steps of storing electric quantity for an energy storage system initially; SOC (State of Charge) _min Setting the minimum charge state of the energy storage system to 0.2 to prevent the energy storage system from overdischarging; SOC (State of Charge) _max Setting the maximum charge state of the energy storage system to be 0.9 to prevent the energy storage system from being overcharged;
power balance constraint:
in order to ensure the regulated and stable operation of the virtual power plant, the sum of the output of the renewable energy power generation, the energy storage power supply and the power supply of the power grid in the system and the sum of the access loads of the user side are balanced, and the system is characterized in that
P t L ＝P t pv +P t w +P t G +P t Ba (19)
Wherein: p (P) t L Accessing an aggregated load quantity of a virtual power plant to an electric automobile user;
the incentive demand is responsive to load transfer constraints:
wherein: p (P) LS0 Initial cost for load transfer;
transferring a cost coefficient for demand response load; m is m t ls And n t ls A start index and an end index of the load transfer contract at the t period are respectively indicated by 1 and 0, and the load transfer starts or ends at the t period; l (L) t ls1 And L is equal to t ls2 The load amounts before and after load transfer in the t period are respectively; d (D) min ls An upper and lower limit for the duration of the load transfer; t (T) ls1 A period of time during which the load is transferable;
equation (20) is an initial cost constraint; formula (21) is a transfer load conservation constraint; equation (22) represents a maximum transfer time and a minimum transfer time constraint, respectively; equation (23) represents load transfer execution state constraints and their inability to start and end at the same time, respectively; the formula (24) represents that the load can be only from T ls1 The period shifts to other periods;
excitation demand response load shedding constraints:
P LC0 ≥φ×m lc (25)
formulas (25) to (28) are the same as those of (20), (22) and (23); p in the formula LC0 Initial cost for load shedding; phi is a cost coefficient of reducing the demand response load; m is m t lc And n t lc A start index and an end index of the load shedding contract at the t period are respectively represented, and the load shedding starts or ends at the t period are respectively represented by 1 and 0; k (k) min And k max The minimum and maximum times of load shedding are performed respectively.
The operation strategy of the virtual power plant comprises the following steps: the power generation side virtual power plant comprises an uncontrollable DG, a large power grid and an energy storage system; the energy storage system has the characteristic of transferring electric quantity, after time-of-use electricity price is implemented, the VPP transfers the low-price electric quantity of the power distribution network to the high price of the power distribution network through the energy storage system to sell, so that benefits are obtained; the implementation of the time-sharing electricity price stimulates the VPP to adjust the power generation plan, so that the power generation cost is reduced, and the power distribution network takes part in peak clipping and valley filling to obtain better benefits;
uncontrollable DG operating strategy: wind power generation and photovoltaic power generation are used as new energy power grid connection, and VPP is preferably utilized within the capacity range;
on the basis of the prior utilization of new energy power, the operation strategy of the electric power market transaction under the condition that the large power grid gives consideration to power supply on the power generation side and time-of-use electricity price is as follows: 1) The electricity purchasing cost is lower than electricity selling price, and VPP purchases electricity to the power grid; 2) The electricity purchasing cost is higher than the electricity selling price, and VPP sells electricity to the power grid; 3) When uncontrollable DG generates more power and the energy storage system cannot be completely consumed, VPP sells electricity to the power grid;
energy storage system operation strategy:
1) The battery stores energy, purchases electricity and stores energy when the time-sharing electricity price is low, sells electricity when the time-sharing electricity price is high, obtains economic benefit and plays a role in peak clipping and valley filling;
2) When uncontrollable DG power generation meets load requirements and is available, the energy storage system stores electric energy to improve the capacity of the system for new energy power generation.
According to the solving method of the economic optimization scheduling model of the virtual power plant, as shown in fig. 2, three-time exponential smoothing method and JupyterNotook software are adopted to generate wind/light prediction output for 24 hours in a typical day based on historical data, and the wind/light prediction output is loaded into a base line load of electric automobile charging in a certain area of 24 hours to evaluate the load adjustable capacity of the electric automobile charging; finally, with the maximum economic benefit of the virtual power plant as a target, a virtual power plant dispatching optimization model is established by taking predicted force, excitation demand response and the like as constraint conditions, the model is a Mixed integer linear programming model (Mixed-Integer Linear Programming, MILP), decision variables are indexes of wind-solar distributed power supply, energy storage equipment, power grid output and two types of excitation demand response, and a solver Cplex and a Yalmip tool box are used in Matlab for solving.
Based on Jupyter Notebook and Matlab software, carrying out data prediction and constructing a virtual power plant scheduling optimization model; solving the model, as shown in fig. 3 and 4, the result shows that the load curve after response is better than the load peak value before response by 26%, and the peak-valley difference is only 61% of the original load; when the scheduling strategy is used for realizing economic optimal scheduling of the virtual power plant, the peak clipping and valley filling can be effectively realized, the running stability of the power grid can be improved, and the utilization rate of new energy power generation can be improved; FIG. 3 is a demand response load graph, and FIG. 4 is a state diagram of the operation of each unit of the virtual power plant.
In summary, according to the virtual power plant economic dispatching method considering the excitation demand response under the time-of-use electricity price, the wind/light power generation amount in the future 24H is predicted through historical data by a wind/light power generation enterprise through step 1, and the predicted power generation amount is reported to the virtual power plant; step 2, CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company; step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method; the charging requirement of the electric vehicle charging station, the output of the wind-solar power generation system and the energy storage of the energy storage system are considered, the output of the distributed power supply is reasonably predicted, the power requirement of each period is better matched, the user requirement response is explored through a market mechanism in an excitation mode, and the aims of energy conservation, emission reduction, peak clipping, valley filling, maximization of the new energy power generation utilization rate and optimal economic benefit of the virtual power plant are better achieved.
Claims (7)
1. The virtual power plant economic dispatching method for considering the excitation demand response under the time-of-use electricity price is characterized by comprising the following steps of:
step 1, predicting wind/light generating capacity in the future 24H through historical data by a wind/light generating enterprise, and reporting the predicted generating capacity to a virtual power plant;
step 2, the CVPP determines the load adjustable capacity of the electric vehicle according to a load baseline of the electric vehicle, integrates the prediction of the new energy generating capacity, gives a scheduling strategy of the next day, and sends the scheduling strategy to each contracted user and a power grid company;
and step 3, scheduling by each contracted enterprise according to an economic optimization scheduling method.
2. The method for economic dispatch of a virtual power plant in consideration of excitation demand response according to claim 1, wherein the step 1 of predicting wind/photovoltaic power generation amount in the future 24H by a wind/photovoltaic power generation enterprise through historical data and reporting the predicted power generation amount to the virtual power plant comprises:
the wind speed and the illumination intensity are predicted by adopting a three-time exponential smoothing method (Holt-windows), and the three-time exponential smoothing prediction method comprises the following formula group:
T j ＝β·(N j -N j-1 )+(1-β)·T j-1 (2)
wherein N is j For the estimated smooth value of the hierarchy, T j As a predicted smoothed value of trend, S j For the estimated smooth value of seasons, j is the current moment point, P j-1 For the load value of the previous point in time, α, β, γ represent the level, trend, and season smoothing factors (0<α、β、γ<1)，P is the seasonal period, P j-p The load value of the last seasonal period;
load predictive value P of future q time point j+q The method comprises the following steps:
P j+q ＝(T j +N j ·q)·S j-p+q (4)
wherein P is j+q For the predicted value of the future q time points, q is the number of the predicted time points, p is the seasonal period, S j-p+q The estimated smooth value of the q moment point after the last season period is obtained.
3. The virtual power plant economic dispatching method for considering the excitation demand response under the time-of-use electricity price as claimed in claim 1, wherein the step 2CVPP determines the load adjustable capacity of the electric vehicle according to the load baseline of the electric vehicle and integrates the prediction of the new energy power generation capacity to give a dispatching strategy of the next day, and the dispatching to each contracted user and the power grid company comprises the following steps:
and taking the optimal economic benefit of the virtual power plant as a target, and taking the demand response excitation compensation cost, the operation and maintenance cost of an energy storage battery and wind-light power generation, the electricity selling benefit of the virtual power plant and the subsidy benefit obtained from a power grid into consideration, and establishing a virtual power plant scheduling optimization model according to the predicted force and constraint conditions of wind/light power generation enterprises, wherein the constraint conditions comprise constraint of wind-light power generation operation, constraint of maximum electric quantity of electricity purchase, selling and energy storage to the power grid, constraint conditions of an energy storage system, power balance constraint, constraint of excitation demand response load transfer and constraint of excitation demand response load reduction.
4. A virtual power plant economic dispatch method for accounting for incentive demand response at time of day as recited in claim 3, wherein said demand response incentive compensation cost is of the formula:
wherein C is t Compensating costs for demand response incentives; c (C) t LC And C t LS Load reduction and load transfer costs to be paid to users by the virtual power plant in the t period respectively; n (N) T All time period sets for bid decision optimization are set to 24 time periods, and each time period is 1h; q rt LC Reducing the load amount for the r-th load and the load reduced at the time t; p is p rt LC Compensating the price for the t period specified by the r-th curtailment contract; p is p rt LC0 The initial cost which is actually required to be paid by the contract at the time t is reduced for the r load; reducing contract State index I rt LC Representing the state of contract r execution by binary numbers;
the operation and maintenance cost of the energy storage battery is as follows:
wherein: c (C) t dy Operating and maintaining cost for the energy storage battery in the t-th period; beta is the running cost coefficient of the energy storage battery; p (P) t Ba The energy storage battery plans to charge and discharge electric quantity within unit time t; alpha is the maintenance cost coefficient of the energy storage battery; e (E) e Ba Rated power of the energy storage battery;
operation maintenance cost of wind-solar power generation:
wherein: c (C) t sw The maintenance cost of the wind turbine generator and the photovoltaic turbine generator is the t-th period; lambda (lambda) w Maintenance for operation of fansCoefficients; p (P) t wB Generating energy of the wind turbine generator set in the t-th period; lambda (lambda) pv Maintaining coefficients for operation of photovoltaic power generation; p (P) t pvB Generating capacity of photovoltaic power generation for the t-th period;
the electricity selling benefit of the virtual power plant is as follows:
wherein: f (f) t s The electricity purchasing benefit of the virtual power plant in the t-th period; p (P) t G Purchasing and selling electric quantity to a large power grid in a t period for the virtual power plant; c (C) t gs Purchasing electricity price for the virtual power plant in the period t; p (P) t w Generating energy of the wind turbine generator set in the t-th period; p (P) t pv Generating capacity of photovoltaic power generation for the t-th period;
demand response subsidies obtained by the virtual power plant from the power grid:
wherein: f (f) t e Benefit is subsidized for the demand response of the virtual power plant from the power grid in the t-th period; l (L) t lc Reducing the load for the t period; p is p t r Executing the subsidy price of the demand response for the virtual power plant in the t period; l (L) t ls1 Performing load transfer for the virtual power plant during a t-th period; l (L) t ls2 Performing load transfer to the amount of power of the ith period for the virtual power plant; p (P) i r The subsidy price of the demand response is executed for the i-th period.
5. A virtual power plant economic dispatch method for accounting for excitation demand response at time-of-use electricity prices as recited in claim 3, wherein: the virtual power plant dispatching optimization model is built according to the demand response excitation compensation cost, the operation and maintenance cost of the energy storage battery and wind-solar power generation, the virtual power plant electricity selling benefit and the subsidy benefit obtained from the power grid, and an objective function of the virtual power plant economic optimal dispatching model is built according to the following formula.
6. A virtual power plant economic dispatch method for accounting for excitation demand response at time of day electricity prices according to claim 3, wherein constraints of wind-solar power generation operation are as follows:
0≤P t w ≤P t wB (13)0≤P t pv ≤P t pvB (14)
wherein: p (P) t w The effective generating capacity of the wind turbine generator set in the t-th period; p (P) t pv The effective power generation amount of the photovoltaic power station in the t-th period;
the maximum electric quantity constraint of electricity purchase and selling and energy storage to the power grid is as follows:
P G_min ≤P t G ≤P G_max (15)
P Ba_min ≤P t Ba ≤P Ba_max (16)
wherein: p (P) t G The virtual power plant purchases electricity to the power grid for the t-th period; p (P) G_min The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; p (P) G_max The minimum electric quantity of electricity purchased and sold from the power grid for the virtual power plant; p (P) t Ba The electric quantity stored or discharged by the energy storage system in the t time period; p (P) Ba_min Minimum electric quantity stored or released for the energy storage system; p (P) Ba_max Maximum amount of electricity stored or released for the energy storage system;
the energy storage system constraint conditions are as follows:
SOC _min ≤SOC t ≤SOC _max (18)
wherein: SOC (State of Charge) t The state of charge of the energy storage system is the t-th period; e (E) Ba The method comprises the steps of storing electric quantity for an energy storage system initially; SOC (State of Charge) _min Setting the minimum charge state of the energy storage system to 0.2 to prevent the energy storage system from overdischarging; SOC (State of Charge) _max Setting the maximum charge state of the energy storage system to be 0.9 to prevent the energy storage system from being overcharged;
power balance constraint:
in order to ensure the regulated and stable operation of the virtual power plant, the sum of the output of the renewable energy power generation, the energy storage power supply and the power supply of the power grid in the system and the sum of the access loads of the user side are balanced, and the system is characterized in that
Wherein: p (P) t L Accessing an aggregated load quantity of a virtual power plant to an electric automobile user;
the incentive demand is responsive to load transfer constraints:
wherein: p (P) LS0 Initial cost for load transfer;
transferring a cost coefficient for demand response load; m is m t ls And n t ls A start index and an end index of the load transfer contract at the t period are respectively indicated by 1 and 0, and the load transfer starts or ends at the t period; l (L) t ls1 And L is equal to t ls2 The load amounts before and after load transfer in the t period are respectively; d (D) min ls An upper and lower limit for the duration of the load transfer; t (T) ls1 A period of time during which the load is transferable;
equation (20) is an initial cost constraint; formula (21) is a transfer load conservation constraint; equation (22) represents a maximum transfer time and a minimum transfer time constraint, respectively; equation (23) represents load transfer execution state constraints and their inability to start and end at the same time, respectively; the formula (24) represents that the load can be only from T ls1 The period shifts to other periods;
excitation demand response load shedding constraints:
P LC0 ≥φ×m lc (25)
formulas (25) to (28) are the same as those of (20), (22) and (23); p in the formula LC0 Initial cost for load shedding; phi is a cost coefficient of reducing the demand response load; m is m t lc And n t lc A start index and an end index of the load shedding contract at the t period are respectively represented, and the load shedding starts or ends at the t period are respectively represented by 1 and 0; k (k) min And k max The minimum and maximum times of load shedding are performed respectively.
7. The virtual power plant economic dispatch method for accounting for excitation demand response at a time-of-use electricity price of claim 1, wherein the virtual power plant operating strategy comprises: the power generation side virtual power plant comprises an uncontrollable DG, a large power grid and an energy storage system; the energy storage system has the characteristic of transferring electric quantity, after time-of-use electricity price is implemented, the VPP transfers the low-price electric quantity of the power distribution network to the high price of the power distribution network through the energy storage system to sell, so that benefits are obtained; the implementation of the time-sharing electricity price stimulates the VPP to adjust the power generation plan, so that the power generation cost is reduced, and the power distribution network takes part in peak clipping and valley filling to obtain better benefits;
uncontrollable DG operating strategy: wind power generation and photovoltaic power generation are used as new energy power grid connection, and VPP is preferably utilized within the capacity range;
on the basis of the prior utilization of new energy power, the operation strategy of the electric power market transaction under the condition that the large power grid gives consideration to power supply on the power generation side and time-of-use electricity price is as follows: 1) The electricity purchasing cost is lower than electricity selling price, and VPP purchases electricity to the power grid; 2) The electricity purchasing cost is higher than the electricity selling price, and VPP sells electricity to the power grid; 3) When uncontrollable DG generates more power and the energy storage system cannot be completely consumed, VPP sells electricity to the power grid;
energy storage system operation strategy:
1) The battery stores energy, purchases electricity and stores energy when the time-sharing electricity price is low, sells electricity when the time-sharing electricity price is high, obtains economic benefit and plays a role in peak clipping and valley filling;
2) When uncontrollable DG power generation meets load requirements and is available, the energy storage system stores electric energy to improve the capacity of the system for new energy power generation.
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| CN116722570A (en) * | 2023-07-27 | 2023-09-08 | 国网浙江省电力有限公司乐清市供电公司 | Optimal operation method and device for power distribution network, electronic equipment and storage medium |
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