Method for joint dispatch of ancillary services and energy

Abstract

The application discloses a day-ahead electric energy and reserve auxiliary service joint clearing method, comprising the following steps: obtaining parameter data; inputting the parameter data into a pre-constructed robust optimization model considering net load uncertainty to obtain an optimal clearing scheme under the worst scenario; wherein, the robust optimization model is constructed based on a risk cost model and a day-ahead electric energy and reserve market joint clearing model; the risk cost model comprises an upper reserve risk cost model considering unit fault and net load prediction error, and a lower reserve risk cost model considering load unplanned exit and net load prediction error; the day-ahead electric energy and reserve market joint clearing model is constructed with the minimum system operation cost as the target. The application can find the optimal clearing scheme under the worst scenario in the system for market decision makers, and provides a more perfect reference decision for market resistance to the risk of uncertainty.

Description

Technical Field

[0001] This invention relates to a method for joint clearing of day-ahead electrical energy and standby ancillary services, belonging to the field of joint clearing technology. Background Technology

[0002] With the continuous advancement of power market reform, my country's current power market trading mechanism has become more flexible. Trading products are transitioning from a single electricity market to a multi-type market encompassing electricity and ancillary services. Trading entities are expanding from single generation-side resources to multiple types of resources including both sources and loads. The operating model is shifting from independent operation in each market to joint operation across multiple markets. Flexible loads are crucial load resources for participating in market competition, and their impact on the power economy is increasingly prominent. In large cities where power supply cannot meet the growing demand, the peak-shaving and valley-filling functions of flexible loads play a key role in ensuring the safe operation of the power grid. As an adjustable load resource, timely interruption or transfer of some loads can provide reserve capacity for the system, improve the flexibility of power dispatch, and alleviate the pressure on thermal power units to provide reserves. Therefore, the dispatching of flexible loads has become a research hotspot in recent years. Flexible loads refer to loads that are flexible and variable within a certain time period, including electric vehicles, energy storage, energy storage, distributed power sources, and microgrids with bidirectional adjustment capabilities. Their electricity consumption behavior can respond flexibly to price signals. Flexible loads can be divided into interruptible loads, shiftable loads, and transferable loads.

[0003] With the continuous improvement of my country's electricity market, the conditions for flexible loads to participate in the day-ahead electricity market and ancillary services market are gradually being met. The ancillary services market typically includes reserve, frequency regulation, peak shaving, automatic generation control, black start services, and reactive power regulation. Interruptible loads refer to loads whose electricity consumption can be reduced to a certain extent according to their own needs, such as air conditioning and lighting. A schematic diagram of interruptible loads is shown below. Figure 2 As shown in the diagram. Transferable load refers to loads where the total electricity consumption remains constant within a scheduling cycle (e.g., one day), but with flexible consumption characteristics, allowing for adjustments to electricity consumption at different times. Examples include electric vehicle battery swapping stations, energy storage, and partial loads from industrial and commercial users. A schematic diagram of transferable load is shown in the diagram. Figure 3 As shown.

[0004] During power system operation, uncertainties such as random generator failures, load and renewable energy output forecasting errors can affect system stability. To ensure the safe and reliable operation of the power system, a certain reserve capacity needs to be reserved to cope with these uncertainties. Generators, due to their good responsiveness, are usually the main source of reserve capacity. However, whether it's reserving generators for backup during high-load periods without responding to load demand, or temporarily adding generators or operating them at low load rates during low-load periods to provide backup, both are uneconomical and even unreliable for the system and the generation side. In this case, considering flexible loads to provide reserve capacity for the system would significantly reduce system operating costs. System reserves can be provided by generators or by adjusting flexible loads to relinquish reserve power. The reserve market can flexibly allocate generator and flexible load reserves based on the system reserve costs provided by generators or flexible loads, achieving efficient resource allocation.

[0005] In the early days-ahead reserve market, only generation-side reserves were considered, and the reserve market operated independently of the electricity market. Based on the clearing results of the days-ahead electricity market, the generation side comprehensively considered the cost of reserve services and their economic and social value, optimizing reserve prices according to the reserve service pricing model. As the reserve market matured, flexible loads gradually participated in providing system reserves. Generating units and flexible load users submitted bids based on their own costs and operating strategies to participate in the reserve market bidding. After the trading institution determined the reserve capacity allocation, generating units and flexible load users cleared their reserves separately. However, the above analysis only considered the reserve market and did not consider the integration with the electricity market. In summary, existing research considered the joint clearing of electricity and reserve ancillary services involving generating units and flexible loads, but most failed to comprehensively consider the risks arising from insufficient upstream and downstream reserves. Furthermore, there are few studies considering the joint clearing of electricity and reserve ancillary services in light of renewable energy and load uncertainties. The uncertainty of these forecasts has a certain impact on the market clearing results; comprehensively considering them will greatly enhance the market's ability to withstand uncertain risks.

[0006] When considering the joint clearing of the electricity market and the reserve market, the generation side can submit bids based on its own generation costs, start-up costs, and reserve dispatch costs using a bidding function. Flexible load users, based on their electricity consumption benefits and reserve dispatch costs, report their adjustable capacity and prices for different time periods on the day-ahead. The trading institution then clears the market according to the principle of minimizing electricity purchase costs, under certain system constraints. The electricity price received by the user is equal to the system's marginal electricity price after clearing. The spot market based on nodal marginal electricity prices determines the nodal price by the marginal cost of supplying electricity to users at each node. This pricing method provides economic signals to users, promotes full market competition, and has a positive effect on system construction.

[0007] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] The purpose of this invention is to overcome the shortcomings of the prior art and provide a joint clearing method for day-ahead electrical energy and standby ancillary services. Through a robust optimization model constructed based on a risk cost model and a joint clearing model of day-ahead electrical energy and standby markets, it can help market decision-makers find the optimal clearing scheme under the worst-case scenario in the system, and provide a more complete reference decision for the market to resist the risk of uncertainty.

[0009] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0010] This invention discloses a method for joint clearing of day-ahead electrical energy and standby ancillary services, comprising the following steps:

[0011] Get parameter data;

[0012] The parameter data is input into a pre-constructed robust optimization model that takes into account the uncertainty of net load to obtain the optimal clearing scheme in the worst-case scenario;

[0013] The robust optimization model is constructed based on the risk cost model and the day-ahead energy and reserve market joint clearing model. The risk cost model includes an upper reserve risk cost model that considers unit failures and net load forecasting errors, and a lower reserve risk cost model that considers unplanned load withdrawal and net load forecasting errors. The day-ahead energy and reserve market joint clearing model is constructed with the goal of minimizing system operating costs.

[0014] Furthermore, the expression for the risk cost model is as follows:

[0015]

[0016] Among them, A(R) t ) represents risk cost; To cover the costs of backup risk; To prepare for potential risks and costs.

[0017] Furthermore, the constraints of the risk cost model include system power balance constraints, minimum start-up and shutdown time constraints, unit standby capacity constraints, and line power flow constraints.

[0018] Furthermore, the expression for the backup risk cost model is as follows:

[0019]

[0020] in, C. To cover the costs of backup risk; G Cost of decommissioning a unit of generating capacity; Let be the expected value of the reserve deficit in the system at time t.

[0021] Furthermore, the expression for the backup risk cost is as follows:

[0022]

[0023] in, To cover backup risk costs, C L Cost per unit of load loss; Let be the expected value of the system's reserve deficit at time t.

[0024] Furthermore, the objective function minf of the day-ahead energy and reserve market joint clearing model is as follows:

[0025]

[0026] Among them, C(P) i,t Let be the power generation cost quotation function for conventional unit i at time t; Let be the indexed state variable of the reserve capacity of conventional unit i at time t; These are the state variables representing the reserve capacity of conventional unit i at time t. Let k be the state variable representing the reserve capacity of the transferable load k at time t. Let k be the state variable of the reserve capacity at time t, representing the transferable load k. For the quotation function of conventional unit i in the upper standby market; For the quotation function of conventional unit i in the lower standby market; Let j be the bidding function for interruptible load j in the upper standby market; Let k be the bid function for the transferable load k in the upper standby market; S is the bid function for transferable load k in the lower standby market; i The startup cost of conventional unit i; Ω represents the start-stop state variable of conventional unit i at time t; SL For the set of transferable loads; Ω IL For interruptible load sets; Ω T For statistical time sets; Ω G This is a collection of all units.

[0027] Furthermore, the power generation cost quotation function C(P) of the conventional unit i at time t... i,t The expression for ) is as follows:

[0028]

[0029] Among them, a i,1 This represents the first bid coefficient for conventional unit i in the electricity market; a i,2 This refers to the second bidding coefficient for conventional unit i in the electricity market; a i,3 This represents the third bid coefficient for conventional unit i in the electricity market. P is the square of the output of a conventional unit i; i,t Output for conventional unit i; Let be the start / stop state variable of conventional unit i at time t.

[0030] Furthermore, the objective function of the robust optimization model is as follows:

[0031] max(minf)

[0032] The constraints are:

[0033]

[0034]

[0035] Among them, Ω T For statistical time sets; Ω W A collection of wind turbine units; Ω PV A collection of photovoltaic units; Ω E For the set of system nodes; Ω G For the set of all units; z w,t The degree of deviation between the actual wind power value and the predicted wind power value; z n,t The deviation between actual photovoltaic (PV) values ​​and load values ​​relative to predicted PV values; z L,e,t This represents the degree of deviation between the actual load and the predicted load. Γ represents the maximum predicted output error of wind turbine w at time t; Γ is an uncertain parameter; P i,t Output for conventional unit i; This represents the predicted output value of the wind turbine unit w at time t. This is the predicted output value of photovoltaic unit n at time t; This represents the predicted load value at time t; The maximum predicted error of the wind turbine w at time t; This represents the maximum prediction error of the output of photovoltaic unit n at time t. P represents the maximum prediction error of the load at time t. DC,t Let be the total power of all inter-provincial connecting lines at time t.

[0036] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0037] The model of the day-ahead energy and reserve ancillary service joint clearing method of this invention comprehensively considers the system reserve demand caused by uncertainties such as unit failures, net load forecasting errors, and unplanned load withdrawals, and establishes upper reserve risk cost models and lower reserve risk cost models respectively. It also considers two types of reserve resources, generators and flexible loads, and optimizes the system reserve capacity through risk costs. Secondly, a joint clearing model for the day-ahead energy and reserve market is constructed to minimize system operating costs. Finally, based on the above model, a robust optimization model considering net load uncertainty is further proposed, providing market decision-makers with the optimal clearing scheme under the worst-case scenario within the system, offering a more comprehensive reference for market risk resistance; simultaneously, it can reduce system operating costs, bringing better social and economic benefits to the power system. Attached Figure Description

[0038] Figure 1 The flowchart of the current method for the joint clearing of electrical energy and standby ancillary services;

[0039] Figure 2 This is a schematic diagram of an interruptible load;

[0040] Figure 3 This is a schematic diagram of a transferable load. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0042] Example

[0043] This embodiment discloses a method for jointly clearing day-ahead electrical energy and standby ancillary services, such as... Figure 1 As shown, it includes the following steps:

[0044] Get parameter data;

[0045] By inputting the parameter data into a pre-built robust optimization model that takes into account the uncertainty of net load, the optimal clearing scheme under the worst-case scenario is obtained.

[0046] Among them, the robust optimization model is constructed based on the risk cost model and the day-ahead energy and reserve market joint clearing model; the risk cost model includes the upper reserve risk cost model that considers unit failures and net load forecasting errors, and the lower reserve risk cost model that considers unplanned load withdrawal; the day-ahead energy and reserve market joint clearing model is constructed with the goal of minimizing system operating costs.

[0047] The technical concept of this invention is as follows: First, considering the uncertainties in system reserve requirements caused by factors such as unit failures, net load forecasting errors, and unplanned load withdrawal, two risk-cost models for upper and lower reserves are established respectively. Simultaneously, considering both generators and flexible loads as reserve resources, the system reserve capacity is optimized through risk costs. Second, a joint clearing model for day-ahead electricity and the reserve market is constructed to minimize system operating costs. Finally, based on the above models, a robust optimization model considering net load uncertainty is further proposed, providing market decision-makers with the optimal clearing scheme under the worst-case scenario within the system, offering a more comprehensive reference for market decision-making in resisting uncertainty risks; simultaneously, it can reduce system operating costs, bringing better social and economic benefits to the power system.

[0048] This invention primarily considers the participation of interruptible and transferable loads within flexible loads in the day-ahead electricity market and the standby ancillary services market. During the joint day-ahead clearing of the electricity market and the standby ancillary services market, generators can submit bids based on their own generation costs, start-up costs, and standby dispatch costs. Flexible load users, based on their electricity revenue and standby dispatch costs, submit adjustable capacity and prices for different time periods on the day-ahead. The trading institution then clears the loads according to the principle of minimizing system operating costs, under certain system constraints. This invention simultaneously considers both generators and flexible loads as standby resources, introducing risk costs to optimize system standby capacity.

[0049] The specific steps of this embodiment are as follows:

[0050] Load demand uncertainty is a significant factor in power system reserve demand analysis. We assume that the forecast errors for different time periods are independent. It is generally accepted that short-term load forecast errors follow a standard normal distribution, i.e.:

[0051] δ L,t ~N(0,(σ) L,t ) 2 )

[0052]

[0053] In the formula, δ L,t σ represents the load forecast error at time t; L,t P represents the standard deviation; L,t The actual load value at time t; The load forecast value is given at time t.

[0054] Let δ be the prediction error of the new energy output at time t. R,t It also follows a standard deviation of σ. R,t The standard normal distribution, that is:

[0055] δ R,t ~N(0,(σ)R,t ) 2 )

[0056]

[0057] In the formula, P R,t To contribute to the actual development of new energy sources at all times. It contributes to the prediction of new energy sources at any time.

[0058] The system net load is defined as the difference between the system load and the output of new energy sources. Since the prediction errors for both load and new energy output follow independent normal distributions, the net load prediction error δ is known from the properties of the normal distribution. D,t It also follows the condition of having an expected value of 0 and a standard deviation of σ. D,t The normal distribution is:

[0059] δ D,t ~N(0,(σ) D,t ) 2 )

[0060]

[0061]

[0062] In the formula, P D,t The actual net load value at time t. Let t be the predicted net load value at time t.

[0063] I. Risk Cost Model

[0064] Reserve capacity refers to the backup capacity in the operation of a power system. Reserve capacity enables the power grid to withstand disturbances such as equipment outages due to faults and load fluctuations, allowing for the rapid establishment of a balance between generation and load, ensuring that the frequency remains within the prescribed range, and preventing cascading accidents or even large-scale blackouts due to insufficient reserves. Traditional reserve systems primarily rely on generator units on the power source side, resulting in a single source. This invention, taking a provincial power grid as an example, considers interruptible loads and transferable loads as sources of reserve capacity, while also addressing the issue of insufficient upstream and downstream reserves due to generator unit failures, and analyzes and models the risks and costs involved.

[0065] Considering the constraints of power outage capacity and frequency, the model for providing backup power to interruptible loads is as follows:

[0066]

[0067]

[0068] In the formula, P IL,j,t Let be the power of the interruptible load j at time t; The standby capacity of interruptible load j at time t;P IL,j The minimum value of interruptible load j; Ω represents the maximum value of the interruptible load j. IL For interruptible load sets; u IL,j,t Ω is the state variable of interruptible load j at time t, where "1" indicates that the reserve capacity of load j at time t is interruptible, and "0" indicates that it is not interruptible; T For the statistical time set; N IL,j This represents the maximum number of interruptible loads j that can be interrupted within the scheduling cycle.

[0069] The total electricity consumption of transferable load remains constant within a scheduling cycle, but the electricity consumption in different time periods can be flexibly adjusted. The model is as follows:

[0070]

[0071]

[0072]

[0073] In the formula, P SL,k,t The transferable load k represents the power consumption that was not won in the standby market at time t; P SL,k This represents the minimum value of the transferable load k; The maximum value of the transferable load k; Ω SL A set of transferable loads; The standby capacity of the transferable load k at time t; The reserve capacity of the transferable load k at time t; Let k be the indexed state variable of the reserve capacity of the transferable load k at time t. Let be the bid-winning status variable of the reserve capacity of the transferable load k at time t. Its value "1" indicates that the bid is won and "0" indicates that the bid is not won.

[0074] Risk cost A(R) caused by insufficient reserves t )for:

[0075]

[0076] Risk costs arising from insufficient reserves This is reflected in the cost of load loss caused by unit failures and net load forecasting errors:

[0077]

[0078] In the formula, C L Cost per unit of load loss; Let be the expected value of the system's reserve deficit at time t.

[0079] Analysis of system under different reserve capacities based on random fault information of the generating units The probability of a single unit out of service is:

[0080]

[0081] In the formula, PR G,i,t Let PR be the probability of unit i failing at time t; i,t Let Ω be the probability of unit i failing at time t; G This is a collection of all units.

[0082] In the above situations, the system has a reserve shortage. for:

[0083]

[0084]

[0085] In the formula, For the system's reserve deficit at time t when only unit i is out of service; P i,t The contracted output of unit i at time t; The system's upper reserve capacity at time t; This represents the reserve capacity of conventional unit i at time t.

[0086] Therefore, we obtain The calculation formula is as follows:

[0087]

[0088] In the formula, Ω G For the collection of all units; PR G,i,t Let i be the probability that only unit i fails at time t; This is for backup quotas in the system.

[0089] Considering unplanned load outages and net load forecasting errors, the system needs to reserve sufficient backup capacity. When transformers, lines, or other equipment fail, the existence of distributed or centralized power restoration equipment or control systems such as reclosing, automatic transfer switches, network reconfiguration, and load transfer may not necessarily cause load outages. Therefore, modeling losses caused by excess power using equipment failures requires considering the substitutability of the equipment and the redundancy capacity of the replacement system, which would make the model overly complex. For a given power grid, unplanned load outages typically follow a certain temporal pattern. Therefore, based on historical load outage data for each time period within a given month (excluding generator failures), the average probability of unplanned load outages for that month can be obtained to reflect the system's backup capacity requirements during unplanned load outages.

[0090] This paper defines the ratio of the cumulative power outages caused by factors other than unit failures to the total load demand within the historical observation period of the month at time t as the system load unplanned outage probability PR. L,t :

[0091]

[0092] In the formula, q C The cumulative power outage amount within the historical observation period of the month containing time t; q L The power supply during the historical observation period of the month in which time t is located.

[0093] Based on the unplanned load outages during the historical observation period, the average percentage of outage load ε at time t when an unplanned load outage occurs is defined. L,t for:

[0094]

[0095] In the formula, P represents the load outage power at time t during the historical observation period when an unplanned load outage occurs due to reasons other than unit failure. L,t,x The load demand at time t during the historical observation period when an unplanned load outage occurs for the xth time, excluding unit failure; The percentage of unplanned load outages at time t within the historical observation period is the xth time that an unplanned load outage occurs due to reasons other than unit failure; N is the cumulative number of load outages at time t within the historical observation period.

[0096] If the system's backup capacity is insufficient, emergency control or corrective control will be used to trip up systems to maintain safe and stable operation. This paper uses the minimum tripping cost caused by unplanned system load outages to assess the consequences of unplanned load outages and defines the backup risk cost. for:

[0097]

[0098] In the formula, C G Cost of decommissioning a unit of generating capacity; Let be the expected value of the reserve deficit in the system at time t.

[0099] Therefore, the expected reserve deficit at time t is analyzed by combining information on unplanned load outages. for:

[0100]

[0101] In the formula, PR L,tThe ratio of the cumulative power outages caused by factors other than unit failures to the total load demand within the historical observation period of the month at time t is used as the probability of unplanned system load outages. This is a backup quota for the system.

[0102] System reserve shortage for:

[0103]

[0104]

[0105]

[0106] In the formula, P L,t Let ε be the actual load value at time t; L,t δ represents the average percentage of outage loads occurring at time t when unplanned outages occur; D,t This represents the net load forecast error. The system's reserve capacity at time t; The reserve capacity of the transferable load k at time t; The reserve capacity of conventional unit i at time t; P represents the system's reserve capacity at time t. SL,k,t The transferable load k represents the power consumption that was not won in the standby market at time t; The standby capacity of the transferable load k at time t; Let q be the reserve capacity of the transferable load k at time t; SL,k Ω represents the total electricity demand of transferable load k during the dispatch cycle; Δt represents the statistical time interval; Ω SL For the set of transferable loads; Ω T For statistical time sets; Ω G This is a collection of all units.

[0107] II. Joint Clearing Model for the Day-ahead Electricity and Reserve Markets

[0108] This embodiment establishes a mathematical model for a provincial power grid with the objective of minimizing the operating costs of a combined electricity and reserve market system. The model considers the generation costs of generating units in the electricity market, with wind and solar power units participating only in the market and their costs ignored, ensuring priority clearing.

[0109] In the standby ancillary services market, considering the standby costs and start-up / shutdown costs of conventional units, the standby costs of flexible loads, and further taking into account the risk costs arising from system standby deficits, a joint clearing model for the day-ahead energy and standby markets is established with the goal of minimizing system operating costs.

[0110]

[0111] In the formula, C(P) i,t Let be the power generation cost quotation function for conventional unit i at time t; This is the bidding status variable for the reserve capacity of conventional unit i at time t, where a value of "1" indicates successful bidding and "0" indicates unsuccessful bidding. These are the bidding status variables for the reserve capacity of conventional unit i at time t, where "1" indicates successful bidding and "0" indicates unsuccessful bidding. The bid-winning status variable is the reserve capacity of the transferable load k at time t, with a value of "1" indicating a successful bid and "0" indicating a failed bid. This is the bid-winning status variable for the reserve capacity of the transferable load k at time t, where a value of "1" indicates a successful bid and "0" indicates a failed bid. For the quotation function of conventional unit i in the upper standby market; For the quotation function of conventional unit i in the lower standby market; Let j be the bidding function for interruptible load j in the upper standby market; Let k be the bid function for the transferable load k in the upper standby market; S is the bid function for transferable load k in the lower standby market; i The startup cost of conventional unit i; Ω represents the start / stop status variable of conventional unit i at time t, where a value of "1" indicates start-up and "0" indicates shutdown. SL For the set of transferable loads; Ω IL For interruptible load sets; Ω T For statistical time sets; Ω G This is a collection of all units.

[0112] The power generation cost quotation function C(P) of conventional unit i at time t i,t )for:

[0113]

[0114] In the formula, a i,1 This represents the first bid coefficient for conventional unit i in the electricity market; a i,2 This refers to the second bidding coefficient for conventional unit i in the electricity market; a i,3 This represents the third bid coefficient for conventional unit i in the electricity market. P is the square of the output of a conventional unit i; i,t Output for conventional unit i; Let be the start / stop state variable of conventional unit i at time t.

[0115] Quotation function of conventional unit i in the standby market for:

[0116]

[0117] In the formula, m i,1 m is the first bid coefficient for conventional unit i in the standby market; i,2 For conventional unit i, the second bid coefficient in the standby market; m i,3 r is the third bid coefficient for conventional unit i in the standby market; i,t The standby capacity of conventional unit i at time t is the reserve capacity won in the bid. Let be the square of the reserve capacity of conventional unit i at time t; Let be the start / stop state variable of conventional unit i at time t.

[0118] Quotation function of conventional unit i in the lower standby market The pricing function of conventional unit i in the standby market Similarly.

[0119] The quotation function of interruptible load j in the standby market for:

[0120]

[0121] In the formula, g j,1 The first bid coefficient for interruptible load j in the standby market; g j,2 The second bid coefficient for interruptible load j in the upper standby market; g j,3 The third bid coefficient for interruptible load j in the upper standby market; Let the square of the standby capacity of the interruptible load j at time t be the value of the indexed standby capacity. The standby capacity of interruptible load j at time t.

[0122] Price function of transferable load k in the upper standby market And the quotation function of transferable load k in the lower standby market The quotation function of interruptible load j in the upper standby market Similarly.

[0123] Based on the day-ahead energy and reserve market joint clearing model established above, constraints were set. These constraints include system power balance constraints, minimum unit start-up and shutdown time constraints, unit reserve capacity constraints, and line power flow constraints.

[0124] (1) System power balance

[0125]

[0126] In the formula, ΩW A collection of wind turbine units; Ω is the predicted output value of the wind turbine w at time t; PV A collection of photovoltaic units; Ω is the predicted output value of photovoltaic unit n at time t. E For the system node set; δ represents the predicted load on node e at time t; D,t This represents the net load forecast error; P DC,t The total power of all inter-provincial connection lines at time t (input power is negative, output power is positive).

[0127] (2) Minimum start-up and shutdown time constraints for conventional units

[0128]

[0129] In the formula, T i on T i off These are the minimum continuous start-up time and minimum continuous shutdown time for conventional unit i, respectively. Let m be the start / stop state variable of conventional unit i at time t; m is the time.

[0130] (3) Constraints on the standby capacity of conventional units

[0131]

[0132]

[0133] In the formula, The reserve capacity of conventional unit i at time t; The reserve capacity of conventional unit i at time t; τ is the ramp rate of conventional unit i; τ is the response time of the standby capacity. For conventional unit i, the downhill ramp rate is... Let i be the start / stop state variable of conventional unit i at time t; and P i These are the upper and lower limits of the output of conventional unit i, respectively.

[0134] (4) Line power flow constraints

[0135] When node load demand changes within the static safety domain, ensuring that the line transmission power does not exceed the permissible safety range can be expressed as:

[0136]

[0137] In the formula, Ω BFor the set of system branches; (·) T This is a matrix transpose operation; These are the injection transfer distribution factor vectors of the unit node and the load node to branch b, respectively; P is the injection transfer distribution factor of the tie-line power exchange node to branch b. t P is the output vector of all units at time t; L,t P represents the load vector of all nodes at time t; DC,t The total power of all inter-provincial connecting lines at time t; This represents the upper limit of power transmission for branch b.

[0138] Assuming the electricity market adopts nodal marginal pricing and the reserve market adopts regional pricing, based on the Karush-Kuhn-Tucker conditions, an extended Lagrangian function is constructed to obtain the dual multipliers for each constraint. The electricity and reserve market prices at time t are calculated as follows:

[0139]

[0140] L t,2 =l t,2

[0141] In the formula, L t,e,1 Let L be the marginal electricity price at node e at time t in the electricity market; t,2 The standby electricity price at time t; t,1 The dual multiplier for power balance constraints; l t,2 Dual multipliers for backup demand constraints; The dual multiplier for the upper limit safety constraint of the branch; The dual multiplier for the lower limit safety constraint of the branch; K e,b Let be the power transfer factor from node e to branch b.

[0142] III. Robust Optimization Model

[0143] Robust optimization is a novel modeling method for uncertain optimization problems. Originating from robust control theory, it complements stochastic optimization and sensitivity analysis, aiming to find a solution that performs well for all realizations of uncertain inputs. Each possible value derived by this method is equally important, representing a most conservative result when facing the worst-case scenario. Considering the prediction error of uncertain variables, an uncertainty set model is established, defining a polyhedral set of uncertain variables Ω. U With the set of uncertain errors Ω Z :

[0144] Ω U ={P t |P t =Pt F +P t ^z t ,t∈Ω T}

[0145]

[0146] P t ∈[P t F -P t ^,P t F +P t ^ ],P t ^>0

[0147]

[0148] In the formula, P t P represents the actual value of the uncertain variable at time t. t F Let P be the predicted value of the uncertain variable at time t. t ^ represents the maximum prediction error of the uncertain variable at time t; z t Ω indicates the degree of deviation between the actual value and the predicted value of an uncertain variable; T Γ represents the statistical time set; Γ is the uncertainty parameter, reflecting the degree of influence of uncertainty on decision-making. When Γ = 0, the corresponding robust optimization model is a deterministic model.

[0149] Robust optimization models aim to find the uncertain variables in the set of uncertain variables Ω. U To determine the most economically efficient scheduling scheme under the worst-case scenario, the optimal solution is found. To this end, the day-ahead spot market clearing model is constructed using the following expression, where the decision variables are the deviations z of the actual values ​​of wind power and solar power output and load from their respective predicted values. w,t z n,t With z L,e,t The winning bids include the unit's output and reserve capacity, and the winning bids for the reserve capacity of flexible loads.

[0150] max(minf)

[0151] The constraints are:

[0152]

[0153]

[0154] Among them, Ω T For statistical time sets; Ω W A collection of wind turbine units; Ω PVA collection of photovoltaic units; Ω E For the set of system nodes; Ω G For the set of all units; z w,t The degree of deviation between the actual wind power value and the predicted wind power value; z n,t The deviation between actual photovoltaic (PV) values ​​and load values ​​relative to predicted PV values; z L,e,t This represents the degree of deviation between the actual load and the predicted load. Γ represents the maximum predicted output error of wind turbine w at time t; Γ is an uncertain parameter; P i,t Output for conventional unit i; This represents the predicted output value of the wind turbine unit w at time t. This is the predicted output value of photovoltaic unit n at time t; This represents the predicted load value at time t; The maximum predicted error of the wind turbine w at time t; This represents the maximum prediction error of the output of photovoltaic unit n at time t. P represents the maximum prediction error of the load at time t. DC,t Let be the total power of all inter-provincial connecting lines at time t.

[0155] It should be noted that, in this embodiment, the acquired parameter data includes: unit load loss cost, unit failure probability, unplanned load outage probability, average outage load percentage when unplanned load outage occurs, and unit unit switching cost of the risk cost model; all bidding coefficients of conventional units in the energy market, all bidding coefficients of conventional units in the upper and lower reserve markets, all bidding coefficients of flexible loads in the upper and lower reserve markets, unit start-up and shutdown costs, upper and lower limits of conventional unit output, conventional unit ramp rate, line parameters, total power of inter-provincial interconnection lines, and predicted values ​​of renewable energy and load of the robust optimization model; and maximum prediction error and uncertain parameters of renewable energy and load of the robust optimization model.

[0156] In summary, this invention comprehensively considers the uncertainties of net load forecasting error, random unit failures, and unplanned load withdrawals in relation to the system's reserve requirements. It establishes risk cost models for insufficient upper and lower reserves, respectively, and simultaneously considers two types of reserve resources: generators and flexible loads. By optimizing the system's reserve capacity through risk costs, this invention optimizes the system's reserve capacity.

[0157] A joint clearing model for day-ahead spot market electricity and ancillary services was established to minimize system operating costs. Furthermore, a robust optimization model that takes into account net load uncertainty was proposed, which can help market decision-makers find the optimal market clearing scheme under the worst-case scenario within the system and provide a reference decision for the market to resist the risk of uncertainty.

[0158] The model of this invention is more comprehensive, introducing flexible loads into the day-ahead electricity and backup ancillary services joint market through quantity-based bidding, and optimizing the market electricity price for flexible loads to participate in the clearing of the day-ahead electricity and backup ancillary services joint market.

[0159] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0160] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0161] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0162] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0163] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A day-ahead clearing method for electrical energy and standby ancillary services, characterized in that, Includes the following steps, Get parameter data; The parameter data is input into a pre-constructed robust optimization model that takes into account the uncertainty of net load to obtain the optimal clearing scheme in the worst-case scenario; The robust optimization model is constructed based on the risk cost model and the day-ahead energy and reserve market joint clearing model. The risk cost model includes an upper reserve risk cost model that considers unit failures and net load forecasting errors, and a lower reserve risk cost model that considers unplanned load withdrawal and net load forecasting errors. The day-ahead energy and reserve market joint clearing model is constructed with the goal of minimizing system operating costs. The expression for the risk cost model is as follows: ； in, For risk costs; To cover the costs of backup risk; To mitigate risk and cost; The expression for the backup risk cost model is as follows: ； in, To cover the costs of backup risk; Cost of decommissioning a unit of generating capacity; Let t be the expected value of the reserve deficit in the system at time t; The expression for the above-mentioned backup risk cost model is as follows: ； in, To mitigate risk costs, Cost per unit of load loss; Let be the expected value of the system's reserve deficit at time t; The objective function of the day-ahead electricity and reserve market joint clearing model as follows: ； in, Let i be the cost quotation function for the generation of conventional unit i at time t; Let be the indexed state variable of the reserve capacity of conventional unit i at time t; These are the state variables representing the reserve capacity of conventional unit i at time t. Let k be the state variable representing the reserve capacity of the transferable load k at time t. Let k be the state variable of the reserve capacity at time t, representing the transferable load k. For the quotation function of conventional unit i in the upper standby market; For the quotation function of conventional unit i in the lower standby market; Let j be the bidding function for interruptible load j in the upper standby market; Let k be the bid function for the transferable load k in the upper standby market; Let k be the bid function for the transferable load in the lower standby market; The startup cost of conventional unit i; Let i be the start / stop state variable of conventional unit i at time t; A set of transferable loads; A set of interruptible loads; For statistical time sets; For the collection of all units; The objective function of the robust optimization model is as follows: ； The constraints are: ； ； in, For statistical time sets; A collection of wind turbine units; A collection of photovoltaic units; For the system node set; For the collection of all units; This represents the degree of deviation between the actual wind power value and the predicted wind power value. This represents the degree of deviation between the actual photovoltaic value and the load relative to the photovoltaic forecast value. This represents the degree of deviation between the actual load and the predicted load. The maximum predicted error of the wind turbine w at time t; These are uncertain parameters; Output for conventional unit i; This represents the predicted output value of the wind turbine unit w at time t. This is the predicted output value of photovoltaic unit n at time t; This represents the predicted load value at time t; The maximum predicted error of the wind turbine w at time t; This represents the maximum prediction error of the output of photovoltaic unit n at time t. The maximum prediction error of the load at time t; Let be the total power of all inter-provincial connecting lines at time t.

2. The method for joint clearing of day-ahead electrical energy and standby ancillary services according to claim 1, characterized in that, The constraints of the risk cost model include system power balance constraints, minimum start-up and shutdown time constraints, unit standby capacity constraints, and line power flow constraints.

3. The method for joint clearing of day-ahead electrical energy and standby ancillary services according to claim 1, characterized in that, The power generation cost quotation function of conventional unit i at time t The expression is as follows: ； in, This represents the first bid coefficient for conventional unit i in the electricity market. This is the second bid coefficient for conventional unit i in the electricity market; This represents the third bid coefficient for conventional unit i in the electricity market. The square of the output of conventional unit i; Output for conventional unit i; Let be the start / stop state variable of conventional unit i at time t.

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