Microgrid risk-constrained dispatch method based on demand response participation

Abstract

The present application relates to the technical field of micro-grid intelligent scheduling, and discloses a micro-grid risk constraint scheduling method based on demand response participation, comprising a pre-scheduling stage and a re-scheduling stage: the pre-scheduling stage schedules the micro-grid composed of controllable micro power supply, wind power and photovoltaic power with deterministic power output, schedules the controllable micro power supply and demand response in the micro-grid through an economic model, and achieves supply and demand balance; in the re-scheduling stage, standby power consumption is added to the scheduling of the micro-grid, and through a "conditional risk constraint scheduling model", the expected profit of the demand response consumption, standby unit consumption and controllable micro power supply cost investment at the current electricity price under the safe frequency state is obtained; the demand response is added to the micro-grid scheduling, the operator is ensured to be within the safe frequency range, and profit maximization is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid intelligent scheduling, and particularly relates to a micro-grid risk constraint scheduling method based on demand response participation. BACKGROUND

[0002] A micro-grid (Micro-Grid, hereinafter referred to as MG) refers to a cluster containing distributed energy and related loads, which can be independently operated or jointly operated with the main grid in the region. With the increasing penetration rate of renewable energy and the development of information technology, micro-grid operators are facing the challenge of how to manage responsive loads to optimize resources and assets.

[0003] The micro-grid has two operation modes of grid-connected and islanded. When grid-connected, there is energy interaction with the external grid, which can support the system energy supply and ensure the reliable supply of all loads. When the grid is faulty or in a remote island area, the system will be in an islanded state. Without the effective support of the external grid, the operation and control of the micro-energy grid will become more complex and difficult.

[0004] Chinese patent publication No. CN108053057A discloses a virtual power plant optimization scheduling modeling method based on CVaR. The method explores the VPP (Virtual Power Plant) scheduling optimization problem through the theory of risk. The VPP contains a large number of renewable energy sources such as wind and light, which have different output characteristics from conventional energy sources, and have strong intermittency and volatility. Therefore, the VPP optimization scheduling problem is an uncertain problem, which makes the scheduling VPP face risks. The CVaR theory can accurately measure the risk of VPP in scheduling operation, thereby balancing the economy and risk.

[0005] However, the scheduling modeling method does not consider demand response as a factor to mitigate grid risk. Demand response refers to a strategy of using price and subsidies to make the demand side complete the corresponding operation according to the scheduling demand, such as reducing the operation of some loads during the power consumption peak to reduce the risk of grid overload. Since users do not necessarily follow the agreement to perform power consumption, especially in the islanded micro-grid, the uncertainty of the micro-grid introduced by the demand response control mode is more prominent, which increases the uncertainty risk of the micro-grid. Therefore, for the control of the islanded micro-grid, how to schedule the controllable micro-power, backup power, and demand response resources in the grid to maximize the operating income under the condition of grid safety. SUMMARY

[0006] In order to achieve the purpose of maximizing the operating income under the condition of grid safety, the problem of how to reasonably schedule the controllable micro-power, backup power, and demand response in the micro-grid is solved.

[0007] The application provides a micro-grid risk constraint scheduling method based on demand response participation, which comprises a pre-scheduling stage and a rescheduling stage.

[0008] S11: the power supply capacity of wind power and photovoltaic in the micro-grid is modeled and analyzed; the power supply capacity curve of the micro-grid composed of wind power and photovoltaic is obtained,

[0009] S12: the load power demand curve is obtained through historical power consumption data analysis;

[0010] S13: the demand response is added to the micro-grid supply and demand regulation by adjusting the power price and subsidizing the users responding to the demand;

[0011] S14: an economic model for deterministic scheduling of the micro-grid is established, the economic model takes economic maximization as the target, and the controllable micro-source and demand response in the micro-grid are scheduled through the economic model to achieve supply and demand balance;

[0012] In the rescheduling stage, the standby power consumption is added to the scheduling of the micro-grid,

[0013] Specifically, the following steps are included:

[0014] S21: the standby power supply equipment is modeled and analyzed to obtain the power supply capacity of the standby power supply equipment;

[0015] S22: a large number of sudden scenarios of power and load changes of wind turbine generators and photovoltaic generators are simulated by the Monte Carlo method, and typical scenarios in which sudden states occur are selected from the large number of sudden scenarios;

[0016] S23: an uncertainty model of the micro-grid in the typical scenario is established to obtain the power supply capacity of the micro-grid formed by wind power and photovoltaic in each typical scenario, so as to simulate the uncertainty of the micro-grid;

[0017] S24: the conditional risk constraint scheduling model is used as the risk aversion model of the operator, based on the scenario-based stochastic optimization method, -CVaR represents the expected profit under the random scenario, and the expected profit of the demand response consumption, the standby unit consumption and the controllable micro-source cost input under the current power price in the safe frequency state is obtained through the conditional risk constraint scheduling model;

[0018] S25: under the condition of meeting the safe state of the micro-grid operation, the demand response consumption and the standby unit input amount of maximum benefit are selected through the objective function.

[0019] Further, in step S14, the economic benefits of the user participating in the demand response at a certain time meet the following load economic model:

[0020]

[0021] wherein, represents the load demand of user j after participating in demand response DR at period t; represents the load demand of user j at period t; represents the corresponding load price; represents the price of the electricity bought and sold by the user; represents the self-elasticity coefficient of user j at period t, the coefficient represents the coefficient of user j participating in demand response DR.

[0022] Further, in step S23, a large number of scenes representing uncertainty are generated by the “distribution function”, and a “k-means algorithm” is used to screen out typical scenes that can sufficiently represent uncertainty.

[0023] Further, in step S23, the wind speed, solar radiation and load prediction error in the typical scene are input into the error model, and then the power generation of wind power or photovoltaic and the load consumption are extracted.

[0024] Further, in step S25, the objective function meets the following relationship:

[0025] wherein, is the profit of scenario s; is the probability of scenario s; is a non-negative variable, is equal to is the profit difference when the profit is less than is a risk aversion factor, is a risk aversion factor, represents the profit threshold. A higher value means that the MG operator is more likely to avoid risks; for example, =0 means that the MG operator is a neutral risk decision maker.

[0026] Further, the demand side constraint is introduced in the constraint condition of the conditional value at risk model, and the demand side constraint meets:

[0027] ; ; ; wherein, represents the power demand of load j at period t; , is the maximum and minimum value of the user load demand; , Let t represent the spinning reserve for load j during time period t. By providing spinning reserve, users will reduce their consumption, and vice versa.

[0028] Finally, the constraints of the conditional risk value model also include AC power flow constraints, which ensure that the active and reactive power flows in the microgrid are within safe limits, thereby guaranteeing the safety of microgrid operation.

[0029] Compared to existing microgrid operation and dispatching methods, this solution incorporates demand response into microgrid dispatching. It analyzes the expected profits of microgrid operators using a conditional value-at-risk (VAT) model, predicting different scenarios and providing operators with profit maximization within a safe frequency range. Actual trials have shown that user participation in demand response for microgrid dispatching not only increases operators' expected profits but also improves frequency security within the microgrid. Attached Figure Description

[0030] Figure 1 The diagram shows the logic block diagram of the risk-constrained scheduling method for microgrids.

[0031] Figure 2 The profit change curve in the risk model when no demand response is included;

[0032] Figure 3 The profit change curve in the risk model when demand response is added. Detailed Implementation

[0033] The following detailed description illustrates the specific implementation method:

[0034] Example 1:

[0035] like Figure 1 The microgrid risk-constrained scheduling method shown includes a pre-scheduling phase and a rescheduling phase. The pre-scheduling phase schedules a microgrid with deterministic power output composed of controllable micro-sources, wind power, and photovoltaics, including the following steps:

[0036] S11: Model and analyze the power supply capacity of wind power and photovoltaic power in the microgrid; obtain the power supply curve of the microgrid composed of wind power and photovoltaic power. The modeling of wind power and photovoltaic power can refer to the patent with publication number "CN108053057A".

[0037] S12: Obtain the load demand curve through historical electricity consumption data analysis; identify electricity consumption patterns using historical electricity consumption data, and pinpoint the time periods that exceed the electricity consumption safety threshold.

[0038] S13: Add demand response to the supply-demand regulation of the micro-grid by adjusting the electricity price and subsidizing the users responding to the demand; introduce the demand response mechanism in the peak electricity consumption interval to achieve peak shaving; reduce the input of local controllable micro power supply and the electricity price in the low electricity consumption period to balance supply and demand.

[0039] S14: Establish an economic model for deterministic scheduling of the micro-grid, which aims to maximize the economy, and schedule the controllable micro power supply and demand response in the micro-grid through the economic model to achieve supply-demand balance.

[0040] The economic benefit of the user participating in the demand response at a certain time meets the following load economic model:

[0041]

[0042] Wherein, represents the load demand of user j after participating in demand response DR at time t; represents the load demand of user j at time t; represents the corresponding load price; represents the price of the user's electricity purchase and sale; represents the self-elasticity coefficient of user j at time t, the coefficient represents the coefficient of user j participating in demand response DR.

[0043] In the rescheduling stage, the standby power supply is added to the scheduling of the micro-grid,

[0044] Specifically, the following steps are included:

[0045] S21: Model and analyze the standby power supply equipment to obtain the power supply capacity of the standby power supply equipment; the standby power supply equipment is selected from micro-turbines, fuel cells or gas turbines. Since the power output of these power sources is constant, the power output at a certain time is obtained by adopting the power-time correspondence.

[0046] S22: A large number of sudden scenarios of power and load changes of wind turbines and photovoltaic units are simulated by "Monte Carlo method generation", and typical scenarios in which sudden states occur are selected from the large number of sudden scenarios;

[0047] S23: Establish a micro-grid uncertainty model of the typical scenario to obtain the micro-grid power supply capacity formed by wind power and photovoltaic power under each typical scenario to simulate the uncertainty of the micro-grid; a large number of scenes representing uncertainty are generated by "distribution function", and "k-means algorithm" is used to screen out typical scenarios that can fully represent uncertainty. The wind speed, solar radiation and load prediction error in the typical scenario are input into the error model, and then the power generation of wind power or photovoltaic power and the load consumption are extracted.

[0048] S24: The conditional risk constraint scheduling model is taken as the risk-averse model of the operator, and in the scenario-based stochastic optimization method, the -CVaR represents the expected profit under the random scenario, and the expected profit of the demand response consumption, standby unit consumption, and controllable micro power cost investment at the current electricity price under the safe frequency state is obtained through the "conditional risk constraint scheduling model";

[0049] S25: Under the condition of meeting the safe state of microgrid operation, the demand response consumption and standby unit investment that maximize the benefit are selected through the "objective function".

[0050] Embodiment 2:

[0051] In the pre-scheduling stage, an economic model for deterministic scheduling of the microgrid is established, and the economic model derivation process is as follows:

[0052] Through demand response, users can adjust the electricity demand according to the system load level change and electricity price. In order to achieve maximum benefit, the user electricity mode change can be expressed as:

[0053] (1)

[0054] wherein, represents the load demand of user j after participating in demand response DR at period t; represents the load demand of user j at period t; represents the load change caused by user participation in demand response DR.

[0055] The profit obtained by user j can be expressed by formula (2):

[0056] (2)

[0057] wherein, and respectively represent the profit and income of user j after participating in demand response DR; represents the corresponding load price; generally speaking, the lower the price, the higher the user's electricity enthusiasm, and the higher the price, the less the user's electricity consumption, in order to maximize the user benefit, condition (3) must be met.

[0058] (3)

[0059] According to the quadratic utility function of the incentive response load participating in demand response, the income of user j expanded according to the power index model is:

[0060] (4)

[0061] wherein, represents the initial benefit of user participating in demand response (DR) ; represents the electricity price of user buying and selling electricity; represents the self-elasticity coefficient of user j in period t, which is used to measure the influence of current single-period electricity price change on electricity demand, and the meanings of other variables are the same as above.

[0062] Derivate formula (4) to get:

[0063] (5)

[0064] Substitute formula (5) into formula (3) to get:

[0065] (6)

[0066] (7)

[0067] Get the load consumption of user j at t time as:

[0068] (8)

[0069] The electricity load response of a certain period is not only related to the current period electricity price, but also affected by other period electricity price. Cross-elasticity coefficient is used to measure the influence of multi-period electricity price change on multi-period electricity load demand, then:

[0070] (9)

[0071] Wherein, NT represents the scheduling period. Combine formula (1), (8) and (9), and take coefficient represents user j participating in demand response (DR), get the load economic model at t time as:

[0072] (10) .

[0073] Example 3:

[0074] Considering the expected profit, the variability of available profit, the CVaR method is applied to the risk aversion model of operator, and the CVaR under the confidence level -CVaR is defined as the profit allocation whose profit expectation value is less than (1- ) quantile. In the scenario-based stochastic optimization method, -CVaR represents the expected profit under about (1- ) × 100% scenarios, which can be obtained by solving the following optimization problem:

[0075] (11)

[0076] where p s is the profit of scenario s ; is the probability of scenario s ; is a non-negative variable, equal to the difference between the profit of scenario and the profit of scenario . The weighted CVaR value of the profit is added to a neutral risk optimization problem by a risk-averse factor with a weighted parameter

[0077] . The objective function is modified as: (12)

[0078] For higher values of , the operator MG operator is more risk-averse; = 0 means that the operator MG operator is a neutral risk decision maker.

[0079] In the second stage, the conditional risk-constrained scheduling model is used to schedule the process, considering that the objective function is composed of three parts, as shown in equation (13). The first part Y1 is the total revenue from selling electricity minus the operating / start-stop cost of controllable generator DG, the standby capacity cost of controllable generator DG and demand response DR, and the cost of purchasing electricity from wind turbine WT and photovoltaic PV. The second part Y2 is the random operating cost of controllable generator DG and renewable energy, which represents the penalty for power curtailment caused by the difference between schedulable resources and real-time power demand. The third part Y3 represents the weighted CVaR value of balancing expected profit and risk.

[0080] (13)

[0081] Embodiment 4

[0082] The constraint conditions of the conditional risk-constrained scheduling model include the following parts:

[0083] (1) Active power balance constraint

[0084] (14)

[0085] In the formula, Pit represents the planned power of unit i at period t, unit kW; Pwt represents the output power of wind turbine w at period t, unit kW; Pv represents the output power of photovoltaic v at period t, unit kW; Dj represents the load demand of user j at period t; Pn,r represents the active power loss from node n to node r at period t, calculated by equation (15).

[0086] (15)

[0087] where, , denotes the conductance, susceptance between node n and node r; , denotes the voltage amplitude of node n, node r at time t, , denotes its corresponding voltage phase angle.

[0088] (2) Controllable generator set DG actual power generation and standby capacity constraints

[0089] (16)

[0090] (17)

[0091] (18)

[0092] (19)

[0093] (20)

[0094] where, denotes the planned power of unit i at time t; , denotes the maximum and minimum power generation of unit i; is a 0, 1 variable indicating the start and stop of the unit, and 1 indicates start; , denote the planned upper and lower spinning reserve capacity of unit i at time t, and equations (18), (19) are the corresponding upper and lower limit constraints; denotes the planned non-spinning reserve capacity of unit i at time t.

[0095] (3) Demand side constraints

[0096] The demand side constraints determine the degree of user participation in dispatch. According to the degree of user participation in demand response DR, the residential load is divided into j groups, and each group must satisfy the following constraints:

[0097] (21)

[0098] (22)

[0099] (23)

[0100] where, denotes the power demand of load j at time t; , the maximum and minimum values of the user load demand; , the upper and lower spinning reserve amounts of load j at time period t. By providing upper and lower spinning reserves, the user will respectively reduce and increase its consumption.

[0101] (4) Power flow constraints

[0102] Equations (24) and (25) give the AC power flow constraints for the normal operation of the operator MG to ensure the safe operation of the operator MG.

[0103] (24)

[0104] (25)

[0105] wherein subscript s represents scenario s; represents the load frequency correlation model of the power load, which is calculated as shown in equation (26).

[0106] (26)

[0107] wherein, is the unit regulation power; is the controllable generator set DG frequency deviation, which can be calculated by equation (27).

[0108] (27)

[0109] wherein, represents the frequency control gain of unit i; NG represents the number of generator sets; the meanings of the remaining variables are the same as described above.

[0110] (5) Reserve constraints actually participated by the operator MG

[0111] (28)

[0112] (29)

[0113] (30)

[0114] wherein, , , respectively represent the upper spinning reserve, the lower spinning reserve, and the non-spinning reserve actually participated by unit i at time period t; , , respectively represent the upper spinning reserve, the lower spinning reserve, and the non-spinning reserve planned by unit i at time period t.

[0115] (6) Actual participation of demand side in reserve constraints

[0116] (31)

[0117] (32)

[0118] Example 5

[0119] As Figure 2 and Figure 3 , the microgrid is operated in islanded mode with a dispatch horizon of 1 day, divided into 24 time periods. The grid contains five dispatchable distributed generators, including two microturbines (MT1 and MT2), two fuel cells (FC1 and FC2), and one gas engine (GE). In addition, three WT (wind turbine) units with a capacity of 80 kW are installed at nodes 6, 9, and 16; two PV (photovoltaic) units with a capacity of 75 kW are installed at nodes 5 and 10. It is assumed that the microgrid operator purchases energy from the wind turbine and photovoltaic units at a fixed price of 0.3 and 0.5 yuan / kWh, respectively. The system frequency is set to 50 Hz.

[0120] The real-time electricity price is set to encourage users to participate in demand response DR. It is assumed that the electricity price before the implementation of demand response DR is fixed at 1 yuan / kWh. The daily load curve can be divided into low valley period (00:00-5:00), non-peak period (5:00-10:00, 16:00-19:00, and 22:00-24:00), and peak period (11:00-15:00 and 20:00-22:00). It is assumed that the ability of users to participate in demand response DR during the dispatch period is 40%. The demand price elasticity is shown in the table below:

[0121] When the demand response DR is not considered for dispatch, when

[0122] increases from 0.01 to 0.1, the profit changes little, and as further increases, the expected profit will decrease significantly, and as increases, the operator needs to pay additional costs to ensure system safety. When users participate in demand response DR, as the parameter

[0123] increases, the operator can obtain more reserve units for dispatch through demand response DR at a lower cost.

[0124] ​Therefore, the demand response is added to the micro-grid scheduling, which can increase the safety of the micro-grid, and by selecting a suitable risk aversion factor value by the operator, the grid safety and the operation benefit maximization can be effectively balanced.

[0125] The above-mentioned is only the embodiment of the present application, and the specific technical solutions and / or common knowledge of the scheme are not described in detail. It should be pointed out that for those skilled in the art, without departing from the technical solutions of the present application, a number of modifications and improvements can be made, which should also be considered as the protection scope of the present application, which will not affect the effect and practicality of the present application. The protection scope of the present application shall be subject to the content of its claims, and the specific implementation mode and the like recorded in the specification can be used to explain the content of the claims.

Claims

1. A microgrid risk-constrained scheduling method based on demand response participation, characterized in that: It includes a pre-schedule phase and a re-schedule phase: The pre-schedule phase schedules the microgrid with deterministic power output composed of controllable micro-sources, wind turbines, and photovoltaic units. The pre-schedule phase includes the following steps: S11: Model and analyze the power supply capacity of wind turbines and photovoltaic units in the microgrid; obtain the power supply curve of the microgrid composed of wind turbines and photovoltaic units; S12: Obtain the load demand curve by analyzing historical electricity consumption data; S13: By adjusting electricity prices and subsidizing users who respond to demand, demand response is incorporated into the supply and demand regulation of the microgrid; S14: Establish an economic model for deterministic dispatching of the microgrid. This model aims to maximize economic benefits by dispatching controllable micro-sources and demand response within the microgrid to achieve supply-demand balance. The economic benefits for users participating in demand response at a given time satisfy the following load economic model: in, This indicates the load demand after user j participates in demand response (DR) during time period t; This represents the load demand of user j during time period t; This indicates the corresponding load electricity price; This indicates the electricity price for users buying and selling electricity; This represents the self-elasticity coefficient of user j during time period t. This represents the coefficient indicating user j's participation in the Demand Response (DR). During the rescheduling phase, the standby generating units are incorporated into the microgrid's scheduling. Specifically, the steps include the following: S21: Model and analyze the standby unit to obtain its power supply capacity; S22: Generate a large number of sudden scenarios simulating power and load changes of wind turbines and photovoltaic units using the Monte Carlo method, and select typical scenarios where sudden states occur from among the large number of sudden scenarios; S23: Establish microgrid uncertainty models for typical scenarios to obtain the power supply capacity of microgrids formed by wind turbines and photovoltaic units under each typical scenario, in order to simulate the uncertainty of microgrids; S24: The conditional risk-constrained scheduling model is used as the operator's risk avoidance model. In the stochastic optimization method for the scenario, the following approach is adopted: -CVaR represents the expected profit under stochastic scenarios. It is obtained through the conditional risk constraint scheduling model, which yields the expected profit of demand response absorption, standby unit absorption, and controllable micro-power source cost investment at the current electricity price under safe frequency conditions. S25: Under the condition of ensuring the safe operation of the microgrid, select the demand response absorption and standby unit input to maximize benefits through the objective function.

2. The microgrid risk-constrained scheduling method based on demand response participation according to claim 1, characterized in that: In step S23, a large number of scenarios representing uncertainty are generated through the distribution function, and the k-means algorithm is used to select typical scenarios that can fully represent uncertainty.

3. The microgrid risk-constrained scheduling method based on demand response participation according to claim 1, characterized in that: In step S24, wind speed, solar radiation, and load prediction errors in typical scenarios are input into the error model, thereby extracting the power generation of wind turbines or photovoltaic units and the load consumption.

4. The microgrid risk-constrained scheduling method based on demand response participation according to claim 3, characterized in that: The conditional risk-constrained scheduling model incorporates demand-side constraints, which satisfy the following: ; ; ;in, This represents the load demand of user j during time period t; , Let j be the maximum and minimum demand values; , This represents the up and down rotation reserve for user j during time period t.

5. The microgrid risk-constrained scheduling method based on demand response participation according to claim 4, characterized in that: The constraints of the conditional risk-constrained scheduling model also include AC power flow constraints.

Images