CN115603317A - Virtual power plant optimal scheduling method based on two-stage risk constraint - Google Patents
Virtual power plant optimal scheduling method based on two-stage risk constraint Download PDFInfo
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Abstract
The invention belongs to the technical field of electric power, and discloses a virtual power plant optimal scheduling method based on two-stage risk constraint, which is characterized by comprising the steps of determining a virtual power plant structure and a scheduling strategy; establishing a VPP two-stage scheduling model considering risk constraint; and carrying out simulation analysis on the virtual power plant topology, and solving the models of the first step, the strategy and the second step by adopting simulation analysis software. The invention has the following main beneficial effects: the energy and standby service scheduling of the VPP is optimized, the risk of the virtual power plant caused by uncertainty is quantified, and the risk of a VPP operator under different conditions is avoided in advance.
Description
Technical Field
The invention belongs to the technical field of electric power, and discloses a virtual power plant optimal scheduling method based on two-stage risk constraint.
Background
With the proposal of a dual-carbon target, the permeability of renewable energy in a power grid is continuously increased to reduce and alleviate the energy crisis and reduce carbon emission, and the energy-saving and emission-reduction technology of a power system becomes a research hotspot, for example, in document 1: zhoushenli, liuyukun, yuyuquan, shu and Ling, etc. virtual power plant technology development trend [ J ] Guangxi electric power, 2021, 44 (1): 1 to 6; document 2: wanyan leaf, zhao Li navigation, chang Wei light, and the like, a virtual power plant energy storage system energy collaborative optimization regulation and control method based on model prediction control [ J ] intelligent power, 2021, 49 (7): 16-22; document 3: huien, wang, etc. thermoelectric virtual power plant two-stage optimization scheduling [ J ] of a heat storage tank-containing smart power, 2019, 47 (11): 79 to 85 portions of; document 4: wujing, distributed resource aggregation virtual power plant multidimensional trade optimization model research [ D ]. North china university of electric power (beijing), 2021; document 5: poplar-nail, zhao junhua, wenfu bolt, etc. virtual power plant bidding strategy containing electric cars and wind-electric machines [ J ] power system automation, 2014, 38 (13): 92-102. A Virtual Power Plant (VPP) integrates information of a plurality of distributed power sources, an energy storage system, and different types of customers through an intelligent information system, and performs reasonable energy allocation to effectively realize efficient energy utilization in coupling between renewable energy power generation and demand-side management, as described in reference 6: yuangui, jia xingchao, cheng liang, etc. virtual power plant source-load coordinated multi-objective optimized scheduling [ J ] solar science, 2021, 42 (5): 105-112; document 7: virtual power plant multi-time scale optimization scheduling based on fuzzy probability strategy real-time feedback [ J ] electrotechnical technical report, 2021, 36 (7): 1446-1455; document 8: li ling hao, cuo yan, zhanhao, etc. virtual power plant risk aversion model and benefit allocation method in electricity market [ J ] electricity construction, 2021, 42 (1): 67-75 parts of; document 9: korean Shi Yong, zhang Yuhua, lisoldier, a cold, heat and electricity virtual power plant based on multi-scene technology is optimized and scheduled in two stages [ J ] electricity measurement and instrument, 2020, 45 (5): 47-54. Because the system faces multiple uncertainties such as renewable energy output, load prediction error and energy price, the risk is brought to VPP scheduling decision when the system participates in the power market, and the research on the virtual power plant optimal scheduling strategy under risk constraint has important significance for improving the stability of the power system and low-carbon development.
For the scheduling problem of the virtual power plant, there have been a lot of research reports at home and abroad, for example, document 10: in na, lisson, take into account the virtual plant optimization scheduling of wind power uncertainty [ J ]. The university of northeast electricity, 2017, 37 (5): 5-12; document 11: electric-gas interconnected integrated energy system low-carbon economic dispatch [ J ] considering carbon capture equipment, electric appliance and energy efficiency management technology, 2020, 23 (4): 106 to 113; document 12: dengjing micro, li hua qiang, wenfengrui, etc. consider the active distribution network two-stage optimization scheduling of virtual power plant market transactions [ J ] power construction, 2021, 42 (9): 22-31; document 13: liuliarmy, rhoning, wutong, etc. consideration of the demand side response virtual power plant optimization schedule based on mixed integer second order cone planning [ J ] solar academy, 2021, 42 (8): 96-104; document 14: zhou army, chenyi, yangyiwei, etc. wind power-garbage incineration virtual power plant with flue gas storage device two-stage optimized scheduling [ J ] Chinese power, 2019, 52 (2): 7-16; document 15: consideration of carbon-containing capture in coordination with electrical-to-gas and virtual power plant optimization scheduling [ J ] grid technology, 2021, 45 (9): 11-20; document 16: pan, zhou Jun, wang Yi, et al. virtual plant two-stage optimization scheduling considering the characteristics of heat supply and utilization [ J ]. Power System and its Automation report, 2021, 33 (6): 8-18; document 17: electric power selling company optimization scheduling and electric power purchasing decision [ J ] power grid technology considering virtual power plant combination strategy, 2020, 44 (6): 2078-2086. In an energy internet environment, user resources mainly participate in a power system in a demand response form to change or reshape a load curve, so as to alleviate challenges brought by uncertain resources such as renewable energy power generation, for example, document 18: poplar Hongdong, yangdi, monsciaceae. Multiple virtual power plant multiple time scale coordinated optimization scheduling [ J ] smart power under high proportion renewable energy infiltration, 2021, 49 (2): 60-68; document 19: panhua, liang Dian, xue Qiangzhong, etc. the economic dispatch of virtual power plant including wind, light, gas and storage based on time-of-use electricity price [ J ] solar energy science and newspaper, 2020, 41 (8) to 115-122. Document 20: hangzhou Xiahi, liujunyong, feng super, etc. the virtual power plant optimization scheduling model [ J ] considering demand response. 1666-1674, the cogeneration units, the distributed power supplies and the demand side resources are integrated, the established VPP scheduling model takes the maximum profit as the target, and the influence of the user on the VPP scheduling under the uncertainty factor is analyzed. Document 21: alahyrari A, ehsan M, mousavazadeh M.A hybrid storage-with Virtual Power Plant (VPP) differentiation in the electric markers: a selected-scheduling optimization consumption price, reusable generation, and electric vehicles uncertainties [ J ]. Journal of Energy Storage,2019, 25: 100812-100820, the action of a distributed mobile power supply of an electric vehicle is considered in a VPP scheduling strategy, the charging and discharging power of the electric vehicle is integrated into a power market, day-ahead Energy and Storage, the arrival and departure time of the electric vehicle, the residual electric quantity when a battery is available and the uncertainty of the number of the electric vehicle are considered, and the efficient utilization of Energy is realized. Document 22: bahrami S, amini M H.A localized tracking algorithm for an electric market with generation uncertainties [ J ]. Applied Energy,2018, 218:520-532, an energy trading framework between VPPs and Independent System Operators (ISO) is built in the power market environment, meanwhile, the uncertainty in renewable energy is solved by adopting conditional risk values, and the risk of serious shortage of generated energy is limited within a certain confidence level. Document 23: zhou Bo, lulin, gao hong, etc. A virtual power plant optimization trading strategy based on two-stage stochastic programming [ J ] power construction, 2018, 39 (9): 70-77, simulating uncertainty of distributed power supply output and electricity price by using a scene method, and providing a two-stage random programming VPP scheduling model to measure transaction risk through conditional risk value. Document 24: kang Y.Optial Energy management for virtual Power plant with renewable generation [ J ]. Energy and Power Engineering,2017,9 (4): 308-320, consider the demand response of the customer, propose VPP's energy management strategy, describing the uncertainty of electricity prices and distributed power supplies, but the uncertainty risk in the energy management problem has not been solved. Document 25: nguyen HT, le L B, wang Z.A doubling string for virtual power plants with the inner radial command exchange mark using the stored program [ J ]. IEEE Transactions on Industry Applications,2018, 54 (4): 3044-3055, an energy price prediction model of the market in the future is established, and the proposed VPP optimization scheduling model effectively solves the uncertainty of energy price and load demand, but the uncertain risk of virtual power plant operator decision is not described, demonstrated and implemented.
Disclosure of Invention
In order to solve the problems, the invention aims to disclose a virtual power plant optimal scheduling method based on two-stage risk constraint, which is realized by adopting the following technical scheme.
A virtual power plant optimal scheduling method based on two-stage risk constraint is characterized by comprising the following steps:
the first step is as follows: determining a virtual power plant structure and a scheduling strategy;
the second step is that: establishing a VPP two-stage scheduling model considering risk constraints: the method comprises the following steps: the method comprises the steps of determining an objective function, establishing a demand response model and establishing constraint conditions, wherein the constraint conditions comprise: power balance constraint, distributed power supply capacity constraint, distributed power supply operation constraint, energy storage device charging/discharging constraint and virtual power plant electric quantity transaction constraint;
the third step: and carrying out simulation analysis on the virtual power plant topology, and solving the models of the first step, the strategy and the second step by adopting simulation analysis software.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the virtual power plant structure comprises a schedulable distributed power unit, a wind generating set, an energy storage device and users participating in demand response.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that a scheduling strategy is carried out in two stages, in the first stage, a VPP submits an hourly bidding decision of energy and reserve of a market and a rotary standby market in the day ahead on the next day, and in the first stage, the decision is made under the condition of non-expectation before knowing the future market price, the load demand and the power generation amount of renewable energy sources, so that a bidding parameter of each transaction period in the day ahead is obtained; clearing results based on the day-ahead power market and the spinning reserve market; in the second stage, the VPP decides to make a transaction decision with the main power grid, and makes a real-time scheduling decision on the distributed generator set, the energy storage device and the flexible resources of the demand side aiming at each transaction period, wherein the real-time scheduling decision comprises the state of the DGs, the optimal output power of the DGs, the load demand after DR implementation, the DGs and the rotating reserve of supply and demand resources.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the method comprises the following steps of: maximum profit of virtual power plant, profit of virtual power plant f pro And an operation cost f co And risk f caused by uncertainty to virtual power plant ris The expression of (a) is:
max F＝f pro -f co +f ris
wherein, the profit f of the virtual power plant pro The revenue including the power exchange between the VPPs and the main grid of the market at the day-ahead, the revenue for selling electricity to the customers, and the revenue for providing backup services to the grid are expressed as:
operating cost f of virtual power plant co Consisting of two parts, i.e. f co ＝f co1 +f co2 Wherein f is 1 The operating cost of the distributed power supply and the energy storage system; f. of 2 Engaging in demand response for usersThe backup cost provided by the main grid is expressed as:
the risk caused by uncertainty to the virtual power plant is quantified by CVaR, a weighting parameter beta for avoiding the risk is introduced to model the balance between the income and the risk of the virtual power plant, and the expression is as follows:
f ris ＝β×CVaR
in the formula: eta s ζ is the auxiliary variable and risk value for calculating CVaR, respectively; alpha is the confidence level of the virtual plant.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the step of establishing the demand response model is as follows: respectively using the coefficient of self-elasticity
And cross elastic coefficient
To indicate transferable loads and reducible load sensitivity to price:
and establishing an economic model formula when the user responds to the demand through load reduction and load reduction participation:
the optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the power balance constraint refers to the power balance constraint at the node n, and the expression is as follows:
in the formula:
respectively the active power and the reactive power interacted between a virtual power plant and a main power grid in the market at the day before;
respectively the active and reactive power output of the distributed power supply i under the scene s of a time period t;
respectively the active power and the reactive power of the wind generating set under the scene s of a time interval t;
respectively the active and reactive requirements of the user j in the time interval t under the scene s;
the active and reactive loads transferred by the user j under the scene s are the time period t.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the capacity constraint of the distributed power supply meets the following formula:
in the formula:
the minimum value and the maximum value of the i output of the distributed generator set are respectively.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the operation constraint relation of the distributed power supply meets the following conditions:
in the formula:
respectively the start-up/shut-down costs of the distributed generator set i;
respectively representing the ascending/descending rates of the output of the distributed generator set i; y is i，ts ，z i，t，s Is a variable from 0 to 1.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the charge/discharge constraint conditions of the energy storage device are as follows:
in the formula:
respectively the charging and discharging power of the energy storage device k;
the variable is 0-1, and represents the charge-discharge state of the energy storage device k;
respectively is the maximum value of the charging/discharging power of the energy storage device k;
the electric quantity of the energy storage device k under the scene s of the time t;
the charging/discharging efficiencies of the energy storage device k, respectively;
respectively the minimum and maximum of the k-capacity of the energy storage device.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that the electric quantity transaction constraint conditions of the virtual power plant are as follows:
in the formula:
purchasing the maximum power from the main network for the virtual power plant;
selling electricity to the main network for the virtual power plant; sigma i，s The variable is 0-1, the virtual power plant purchases power from the main network when the variable is 0, and the virtual power plant sells power to the main network when the variable is 1.
The optimal scheduling method of the virtual power plant based on the two-stage risk constraint is characterized in that in the simulation analysis step, the virtual power plant topology is subjected to simulation analysis, and the provided mixed integer linear programming model is solved by adopting MATLAB, YALMIP and CPLEX.
The invention has the following main beneficial effects: the energy and standby service scheduling of the VPP is optimized, the risks caused by uncertainty to the virtual power plant are quantized, and the risks of VPP operators under different conditions are avoided in advance.
Drawings
Fig. 1 is a schematic diagram of day-ahead electricity prices and load demands.
Fig. 2 is a load demand distribution diagram in four scenarios.
FIG. 3 is a graph of VPP yield versus CVaR value for parameter β.
Detailed Description
Implementation example:
referring to fig. 1 to 3, a two-stage risk constraint-based virtual power plant optimal scheduling method may also be referred to as a virtual power plant two-stage optimal scheduling method considering user comprehensive demand response and risk constraint to optimize energy and standby service scheduling of a VPP. In the first stage, the VPP submits a bidding parameter of each trading period in the electric energy market and the rotating standby market in the day ahead; and in the second stage, based on the clearing results of the day-ahead electric energy market and the rotating standby market, the VPP is traded with the main power grid, and the distributed generator set, the energy storage device and the flexible resources on the demand side are scheduled in real time according to each trading period. Risks caused by uncertainty are quantified by Conditional Value-at-Risk (CVaR), and a Risk avoidance weighting parameter beta is introduced to balance gains and risks of the virtual power plant so as to simulate Risk avoidance behaviors of a VPP (virtual pressure platform) operator under different conditions. Finally, the effectiveness of the method is verified through example simulation.
The first step is as follows: determining a virtual power plant structure and a scheduling strategy:
the virtual power plant structure comprises a schedulable distributed power unit, a wind generating set, an energy storage device and users participating in demand response. The VPP operator makes a transaction decision according to the information such as the energy supply and demand information, the electricity price and the renewable energy output so as to maximize the benefits of the VPP operator, and meanwhile, the users participating in demand response can reduce the electricity charge by managing the use of the intelligent household appliances and need to consider the VPP operator when making a decision.
The proposed scheduling strategy proceeds in two phases, in the first phase, the VPP submits hourly bidding decisions for the next day's market and rotating alternate market energy and reserves. In this stage, decisions are made on the unexpected condition before the future market price, load demand and renewable energy generation are known, and the bidding parameters of each trading period in the day ahead are obtained. Based on the clearing results of the day-ahead electric energy market and the rotating standby market, in the second stage, the VPP decides to make a trading decision with the main power grid, and makes a real-time scheduling decision on the distributed generator set, the energy storage device and the flexible resource of the demand side for each trading period. Including the state of Distributed Generators (DGs), the optimal output power of DGs, the load demand after DR implementation, DGs, and the rotating reserves of supply and demand resources. Due to the uncertainty caused by random variables, the VPP presents a certain risk in the decision process. Therefore, the risk caused by uncertainty to the virtual power plant is quantified by the CVaR, and the balance between the benefit and the risk of the virtual power plant is modeled by introducing the risk evasion weighting parameter beta so as to research the risk evasion behaviors of the VPP operator under different conditions.
The second step is that: establishing a VPP two-stage scheduling model considering risk constraints:
firstly: establishing an objective function:
the invention aims at maximizing the income of the virtual power plant, and the objective function comprises the income f of the virtual power plant pro Operational cost f co And risk f caused by uncertainty to virtual power plant ris Expressed as:
max F＝f pro -f co +f ris (1)
virtual power plant revenue f pro The revenue including the amount of power exchanged between the VPPs and the main grid of the market at the day-ahead, the revenue of electricity sold to the customers, and the revenue of providing backup services to the grid can be expressed as:
in the formula: n is a radical of T ，N J ，N s ，N G Respectively representing a time interval, a user, a scene and a set of distributed power supply units; pi s Representing the probability of occurrence of the scene s;
representing the total active power sold by the virtual power plant in the market at the day before for a time period t;
the VPP electricity selling price in the market at the day before represents the time period t;
representing the load demand of a user j after participating in demand response in a time interval t under a scene s; p is a radical of shed Representing the active power transferred; rho j，i，s Represents the electricity price of the user j under the scene s in the time period t;
the electricity selling and bidding price of the distributed power supply i rotating upwards/downwards in the time period t is represented;
representing the amount of spin up/spin down backup power provided by the distributed power source i for a period t.
Operating cost f of virtual power plant co Consisting of two parts, i.e. f co ＝f col +f co2 . Wherein f is 1 The operating cost of the distributed power supply and the energy storage system; f. of 2 The spare cost provided for the user to participate in the demand response and the main grid is expressed as:
in the formula: n is a radical of hydrogen k Is a collection of energy storage devices; a is i ，b i The operation coefficient of the distributed power supply i is; u. of i，t，s The starting and stopping state of the distributed power supply i in a scene s at a time t; p is i，t，s The active power of the distributed power supply i under the scene s of the time t;
respectively representing the start-stop cost of the distributed power supply i; y is i，t，s ，z i，t，s Respectively representing binary variables of starting and stopping the distributed power supply i;
respectively representing the competitive bidding price of the distributed power supply i in the time period t for upward/downward/no rotation;
the standby power quantity of the distributed power supply i in the time period t, which is up/down/no rotation, is respectively represented;
the electricity purchase price of the energy storage device k is the time period t;
charging power for the energy storage device k;
the electric quantity of the energy storage device k is in a scene s of a time period t;
is the discharge efficiency of the energy storage device k;
respectively representing the competitive bidding prices of the user j in the time period t for rotating up/down for standby under the scene s;
respectively representing the up/down rotation standby electricity consumption provided by the user j in the time period t;
respectively representing the electricity purchase price of the distributed power supply i rotating upwards/downwards under the situation s of the time t;
and the spare electricity purchasing quantity of the distributed power supply i rotating upwards/downwards in the time period t is respectively represented.
According to the method, the risk caused by uncertainty to the virtual power plant is quantified by the CVaR, a weighting parameter beta for avoiding the risk is introduced to model the balance between the income and the risk of the virtual power plant, a conservative operator can select a larger beta value to increase the risk weight, and a risk neutral operator prefers a higher risk to obtain a higher profit. Can be expressed as:
f ris ＝β×CVaR (5)
in the formula: eta s ζ is the auxiliary variable and risk value for calculating CVaR, respectively; alpha is the confidence level of the virtual plant.
Then: establishing a demand response model:
the users participate in the demand response of the virtual power plant through load transfer and load reduction to minimize own power consumption cost, and the self-elasticity coefficients are respectively adopted
And cross elastic coefficient
To indicate transferable loads and reducible load sensitivity to price:
in the formula:
an initial value representing the electricity price of the time period tsuer j; Δ ρ j，t A variation amount representing the electricity rate of the time period ttuser j;
an initial value representing the load demand of user j during a time period t; delta D j，i Representing the amount of change in load demand for time period tuser j.
The economic model when the user responds to the demand by load shedding and load shedding participation can be expressed as follows:
in the formula:
representing the load demand after the user j participates in demand response in the time period t; lambda j Probability of participating in demand response for user j; rho j，t Representing the electricity rate of the time period ttuser j.
Then: establishing a constraint condition:
(1) Power balance constraint
To ensure that the power purchased from the main grid and the power generated by the virtual plant generator sets can meet the customer's demand, the power balance constraint at node n can be described as:
in the formula:
respectively the active power and the reactive power interacted between a virtual power plant and a main power grid in the market at the day before;
respectively the active and reactive power output of the distributed power supply i under the scene s of a time period t;
respectively the active power and the reactive power of the wind generating set under the scene s of a time interval t;
respectively the active and reactive requirements of the user j in the time interval t under the scene s;
for time period t, the transferred active and reactive loads of user j under scenario s.
(2) Distributed power capacity constraints
In the formula:
the minimum value and the maximum value of the i output of the distributed generator set are respectively.
(3) Distributed power supply operation constraints
In the formula:
respectively the start-up/shut-down costs of the distributed generator set i;
respectively representing the ascending/descending rates of the output of the distributed generator set i; y is i，ts ，z i，i，s Is a variable from 0 to 1.
(4) Energy storage device charge/discharge restraint
In the formula:
respectively the charging and discharging power of the energy storage device k;
the variable is 0-1, and represents the charge-discharge state of the energy storage device k;
respectively is the maximum value of the charging/discharging power of the energy storage device k;
the electric quantity of the energy storage device k under the scene s of the time t;
the charging/discharging efficiencies of the energy storage device k, respectively;
the minimum and maximum values of the k capacity of the energy storage device are respectively.
(5) Virtual power plant electricity quantity transaction constraint
The virtual power plant may trade its own remaining/shortage power with the main grid by selling and buying power, with the constraints:
in the formula:
purchasing the maximum power from the main network for the virtual power plant;
selling electricity to the main network for the virtual power plant; sigma i，s The variable is 0-1, the virtual power plant purchases power from the main network when the variable is 0, and the virtual power plant sells power to the main network when the variable is 1.
Carrying out verification examples and results:
to verify the validity of the proposed scheduling strategy, document 26 is utilized: qia J, meng K, zheng Y, et al, optimal scheduling of distributed energy resources as a virtual power plant in a reactive energy frame [ J ]. IET Generation, transmission & Distribution,2017, 11 (13): 3417-3427, performing simulation analysis on the topology of the 15-node virtual power plant, and solving the mixed integer linear programming model provided by the invention by adopting MATLAB + YALMIP/CPLEX. The predicted value of the output power of the wind generating set of the virtual power plant is detailed in document 20. The day-ahead electricity prices and load demands are shown in fig. 1. The daily load curve is divided into three different periods, namely, a valley period (00-5); the parameters of DG and ESS devices are shown in tables 2 and 3, respectively.
TABLE 1 Flexible price of demand response
TABLE 2 relevant parameters of distributed power generating units
TABLE 3 parameters associated with energy storage devices
In order to study the influence of different user demand responses on VPP scheduling decisions under different risk conditions, four scenarios are considered for data analysis: scene one: the optimal scheduling of the virtual power plant without considering user demand response; scene two: the optimal scheduling of the virtual power plant considering user load reduction; scene three: the optimal scheduling of the virtual power plant considering user load transfer; scene four: and simultaneously, the load reduction of a client user and the optimal scheduling of the transferred virtual power plant are considered.
The load demand distribution of the virtual power plant users under the 4 scenarios is shown in fig. 2. It can be seen that the load demand of the user in the peak period is reduced in the scenario 2 to reduce the electricity charge, but the other periods are not changed; while scenario 3 reduces power usage during peak hours and shifts partial consumption to other hours, especially during low-ebb hours. Because the daily energy demand of the user is not changed, the energy consumption mode is changed through load transfer, and therefore the electricity utilization cost can be reduced.
The VPP gain and CVaR values for the parameter β under different scenarios are shown in FIG. 3. It can be seen that the benefit of VPP decreases as β increases. When the beta value is lower, the influence of risk avoidance on the yield of VPP and CVaR is smaller, and under the condition of higher beta value, the yield reduction of the scheduling scheme under all scenes is increased. Compared with other scenes, in the scene 4, the comprehensive consideration of the demand response of the user results in the highest profit and the lowest CVaR value of the scene relative to other scenes, and the scheduling economy is better. Risk aversion results in a reduction in energy transactions for sale in the market today, and in a risk avoidance situation, a VPP tends to buy electricity from its local DG unit to meet more load demands than the main network. Thus, energy trading in risk avoidance situations is smaller than in risk neutral situations, since the VPP shows more risk-avoiding behavior, it tends to supply load from the DG units to eliminate fluctuations in market price.
Four different beta values are set to analyze the influence of risk avoidance on VPP reserve scheduling, and Table 4 provides total ascending and descending rotation reserve for distributed unit and user demand response under four scenes. It can be seen that in all scenarios, the user will cause a reduction in the backup of the distributed set by participating in the demand response. However, in scenario four, the user may participate in the backup service through load shifting and load shedding, thereby affecting the backup power supply more than in other scenarios. In addition, under the condition that the beta value is larger, the whole scheduling standby is increased so as to reduce the load shedding under the unexpected condition and ensure the reliable operation of the system. By increasing the beta value, the severe scenario faced by the system will be reduced, thereby mitigating the multiple uncertainty effects faced by the virtual power plant.
TABLE 4 energy Standby case of VPP scheduling
The invention provides a virtual power plant optimal scheduling method based on two-stage risk constraint, which is used for optimizing VPP energy and standby service scheduling, quantifying risks caused by uncertainty to a virtual power plant by using CVaR, and introducing a risk avoidance weighting parameter beta to model balance between income and risks of the virtual power plant so as to research risk avoidance behaviors of a VPP operator under different conditions. Simulation results show that: the participation of users in demand response can be effectively improved into the market income of the virtual power plant participating in the main network, and the profits of the VPPs can be improved through different types of demand response behaviors. When the weighting parameter beta value of the risk avoidance is low, the influence of the risk avoidance on the benefit of the VPP and the CVaR is small, and under the condition that the beta value is high, the reduction range of the benefit of the scheduling scheme under all scenes is increased, so that reference is provided for VPP operators with different risk preferences.
The invention has the following main beneficial effects: the energy and standby service scheduling of the VPP is optimized, the risks caused by uncertainty to the virtual power plant are quantized, and the risks of VPP operators under different conditions are avoided in advance.
The above-mentioned embodiments are merely preferred technical solutions of the present invention, and should not be construed as limiting the present invention. The protection scope of the present invention is defined by the claims, and includes equivalents of technical features of the claims. I.e., equivalent alterations and modifications within the scope hereof, are also intended to be within the scope of the invention.
Claims (10)
1. A virtual power plant optimal scheduling method based on two-stage risk constraint is characterized by comprising the following steps:
the first step is as follows: determining a virtual power plant structure and a scheduling strategy;
the second step is that: establishing a VPP two-stage scheduling model considering risk constraints: the method comprises the following steps: the method comprises the steps of determining an objective function, establishing a demand response model and establishing constraint conditions, wherein the constraint conditions comprise: the method comprises the following steps of power balance constraint, distributed power supply capacity constraint, distributed power supply operation constraint, energy storage device charging/discharging constraint and virtual power plant electric quantity transaction constraint;
the third step: and carrying out simulation analysis on the virtual power plant topology, and solving the models of the first step, the strategy and the second step by adopting simulation analysis software.
2. The optimal scheduling method for the virtual power plant based on the two-stage risk constraint of claim 1, wherein the virtual power plant structure comprises a schedulable distributed power generating unit, a wind generating set, an energy storage device and a user participating in the demand response.
3. The two-stage risk constraint-based virtual power plant optimal scheduling method of claim 2, wherein the scheduling strategy is performed in two stages, in the first stage, the VPP submits an hourly bidding decision for the next day-ahead market and rotating standby market energy and reserves, in this stage, the decision is made under the unexpected condition before knowing the future market price, the load demand and the renewable energy power generation amount, and the bidding parameters of each transaction period in the day-ahead are obtained; clearing results based on the day-ahead power market and the spinning reserve market; in the second stage, the VPP decides to make a transaction decision with the main power grid, and makes a real-time scheduling decision on the distributed generator set, the energy storage device and the flexible resources of the demand side aiming at each transaction period, wherein the real-time scheduling decision comprises the state of the DGs, the optimal output power of the DGs, the load demand after DR implementation, the DGs and the rotating reserve of supply and demand resources.
4. The two-stage risk constraint-based virtual power plant optimal scheduling method according to claim 3, characterized by the step of determining an objective function: maximized profit of virtual power plant, profit f of virtual power plant pro Operational cost f co And risk f caused by uncertainty to virtual power plant ris The expression of (a) is:
max F＝f pro -f co +f ris
wherein the profit f of the virtual power plant pro The revenue including the power exchange between the VPPs and the main grid of the market at the day-ahead, the revenue for selling electricity to the customers, and the revenue for providing backup services to the grid are expressed as:
operating cost f of virtual power plant co Consisting of two parts, i.e. f co ＝f co1 +f co2 Wherein, f 1 The operating cost of the distributed power supply and the energy storage system; f. of 2 The spare cost provided for the user to participate in the demand response and the main grid is expressed as:
the risk caused by uncertainty to the virtual power plant is quantified by CVaR, a weighting parameter beta for avoiding the risk is introduced to model the balance between the income and the risk of the virtual power plant, and the expression is as follows:
f ris ＝β×CVaR
in the formula: eta s ζ is the auxiliary variable and risk value for calculating CVaR, respectively; α is the confidence level of the virtual plant.
5. The two-stage risk constraint-based virtual power plant optimal scheduling method according to claim 4, wherein the step of establishing the demand response model is as follows: respectively using the coefficient of self-elasticity
And cross elastic coefficient
To represent the transferable loads and to reduce the sensitivity of the loads to price:
and establishing an economic model formula when the user responds to the demand through load reduction and load reduction participation:
6. the optimal scheduling method of the virtual power plant based on the two-stage risk constraint of claim 5, wherein the power balance constraint is a power balance constraint at a node n, and the expression is as follows:
in the formula:
respectively the active power and the reactive power interacted between a virtual power plant and a main power grid in the market at the day before;
respectively the active and reactive power output of the distributed power supply i under the situation epsilon of a time period t;
respectively the active power and the reactive power of the wind generating set under the scene epsilon of a time period t;
respectively the active and reactive requirements of the user j in the time interval t under the scene s;
for time period t, the transferred active and reactive loads of user j under scenario s.
7. The two-stage risk constraint-based optimal scheduling method for the virtual power plant according to claim 6, wherein the distributed power supply capacity constraint satisfies the following formula:
in the formula: i P，
the minimum value and the maximum value of the i output of the distributed generator set are respectively.
8. The two-stage risk constraint-based virtual power plant optimal scheduling method according to claim 7, wherein the distributed power supply operation constraint relationship satisfies the following conditions:
in the formula:
respectively the start-up/shut-down costs of the distributed generator set i;
respectively representing the ascending/descending rates of the output of the distributed generator set i; y is i，t，s ，z i，t，s Is a variable from 0 to 1.
9. The optimal scheduling method of the virtual power plant based on the two-stage risk constraint of claim 8, wherein the constraint conditions of the charging/discharging of the energy storage device are as follows:
in the formula:
respectively the charging and discharging power of the energy storage device k; theta k，i，s The variable is 0-1, and represents the charge-discharge state of the energy storage device k;
respectively is the maximum value of the charging/discharging power of the energy storage device k;
the electric quantity of the energy storage device k under the situation epsilon of a time period t;
the charging/discharging efficiencies of the energy storage device k, respectively;
respectively the minimum value and the maximum value of the k capacity of the energy storage device;
the electric quantity transaction constraint conditions of the virtual power plant are as follows:
in the formula:
purchasing the maximum power from the main network for the virtual power plant;
the maximum value of electricity sold to the main network for the virtual power plant; sigma t，s The variable is 0-1, the virtual power plant purchases power from the main network when the variable is 0, and the virtual power plant sells power to the main network when the variable is 1.
10. The optimal scheduling method of the virtual power plant based on the two-stage risk constraint of claim 9, wherein in the simulation analysis step, the topology of the virtual power plant is subjected to simulation analysis, and the provided mixed integer linear programming model is solved by using MATLAB, YALMIP and CPLEX.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113688567A (en) * | 2021-08-10 | 2021-11-23 | 华北电力大学（保定） | Two-stage optimal scheduling method of virtual power plant considering impact load |
| CN115879983A (en) * | 2023-02-07 | 2023-03-31 | 长园飞轮物联网技术(杭州)有限公司 | Virtual power plant scheduling method and system |
| CN116526468A (en) * | 2023-05-09 | 2023-08-01 | 国网湖北省电力有限公司经济技术研究院 | High-proportion renewable energy power system optimal scheduling method considering multi-type standby auxiliary service |
| CN117196173A (en) * | 2023-07-27 | 2023-12-08 | 河海大学 | Virtual power plant distributed scheduling method considering operation risk and network transmission |
| CN117526454A (en) * | 2024-01-05 | 2024-02-06 | 国网浙江省电力有限公司宁波供电公司 | Virtual power plant operation management method, device and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113688567A (en) * | 2021-08-10 | 2021-11-23 | 华北电力大学（保定） | Two-stage optimal scheduling method of virtual power plant considering impact load |
| CN113688567B (en) * | 2021-08-10 | 2023-08-11 | 华北电力大学（保定） | Virtual power plant two-stage optimization scheduling method considering impact load |
| CN115879983A (en) * | 2023-02-07 | 2023-03-31 | 长园飞轮物联网技术(杭州)有限公司 | Virtual power plant scheduling method and system |
| CN116526468A (en) * | 2023-05-09 | 2023-08-01 | 国网湖北省电力有限公司经济技术研究院 | High-proportion renewable energy power system optimal scheduling method considering multi-type standby auxiliary service |
| CN116526468B (en) * | 2023-05-09 | 2024-04-26 | 国网湖北省电力有限公司经济技术研究院 | High-proportion renewable energy power system optimal scheduling method considering multi-type standby auxiliary service |
| CN117196173A (en) * | 2023-07-27 | 2023-12-08 | 河海大学 | Virtual power plant distributed scheduling method considering operation risk and network transmission |
| CN117196173B (en) * | 2023-07-27 | 2024-04-09 | 河海大学 | Virtual power plant distributed scheduling method considering operation risk and network transmission |
| CN117526454A (en) * | 2024-01-05 | 2024-02-06 | 国网浙江省电力有限公司宁波供电公司 | Virtual power plant operation management method, device and storage medium |
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