Virtual power plant optimization scheduling strategy considering energy storage system

Abstract

The invention discloses a virtual power plant optimization scheduling strategy considering an energy storage system. The strategy comprises renewable energy sources, the energy storage system and flexible requirements. The total income maximization of the commercial virtual power plant in the day-ahead and balance energy market is taken as a target; the optimization model of the commercial virtual power plant is expressed as a two-stage random optimization problem; the transaction strategy of the commercial virtual power plant in the day-ahead market is a first-stage variable, and the upper and lower reserve energy of the commercial virtual power plant in the balanced energy market and the operation parameters of the renewable energy source and the energy storage system are used as second-stage variables; the objective function is established to maximize the profit of the commercial virtual power plant in the day and the balance energy market in one day. The model aims to determine a sales / purchase strategy of a commercial virtual power plant in a day-ahead market, and the reserve energy supply capability of the commercial virtual power plant in a balanced energy market is estimated by considering the probability that the commercial virtual power plant is selected to provide reserve energy.

Description

Technical Field

[0001] The present invention relates to a virtual power plant optimization scheduling strategy considering an energy storage system, and belongs to the field of virtual power plant optimization scheduling. Background Art

[0002] Few studies in the prior art consider the role of energy storage systems in energy and reserve markets. Energy storage systems can provide upward reserves in a variety of ways: increasing discharge power, reducing charging power, or even changing the state of the energy storage system from charging to discharging. Similarly, downward reserves can be provided by increasing charging power, reducing discharge power, or changing the state of the energy storage system from discharging to charging.

[0003] Virtual power plants are assumed to be active agents in the balancing market, with pre-signed upside reserve capacity contracts in the balancing capacity market. Therefore, before the day-ahead market closes, the operator of a commercial virtual power plant should simultaneously perform the following tasks: determine the optimal trading strategy for the virtual power plant in the day-ahead market, evaluate the profit opportunities of the commercial virtual power plant in the balancing energy market, and then calculate the operating plan for each commercial virtual power plant component. The challenge here is that the operation of the energy storage system is limited by its energy level, and activating the reserve energy of the energy storage system in the balancing energy market will change this energy level. Therefore, the operating plans of the energy storage system and the commercial virtual power plant in the day-ahead market will be affected. Summary of the invention

[0004] In order to solve the problems of the prior art, the present invention provides an optimization scheduling strategy for virtual power plants considering energy storage systems, establishes an optimal scheduling model for commercial virtual power plants consisting of renewable energy, energy storage systems and customers in the day-ahead and balancing energy markets, and determines the optimal scheduling of virtual power plants in the day-ahead and balancing energy markets through a two-stage stochastic optimization model. The operation of the energy storage system is described by an energy model of the day-ahead market and a reserve supply model of the balancing energy market. The two energy storage system models are associated to ensure that reserve activation does not affect day-ahead scheduling. The present invention takes into account the uncertainty of demand, the available power output of renewable energy and the probability of reserve activation.

[0005] In order to achieve the above object, the steps of the present invention are specifically as follows:

[0006] A virtual power plant optimization scheduling strategy considering energy storage system, characterized in that the steps are as follows:

[0007] S1: Establish an optimal dispatch model for commercial virtual power plants in the day-ahead and balancing energy markets, including renewable energy, energy storage systems and flexible demand; aiming to maximize the total revenue of commercial virtual power plants in the day-ahead and balancing energy markets;

[0008] S2: The optimization model of commercial virtual power plants is formulated as a two-stage stochastic optimization problem;

[0009] The trading strategy of a commercial virtual power plant is a first-stage variable in the day-ahead market, and hourly trading decisions should be determined within a 24-hour time frame. The first-stage variable needs to take into account forecast data on the available power output of renewable energy and customer demand, and should be maintained in the event that these forecasts may differ from actual data;

[0010] The commercial virtual power plant reserves energy above and below the balancing energy market, as well as the operating parameters of renewable energy and energy storage systems, as second-stage variables;

[0011] Renewable energy and demand data should be presented as a set of scenarios ( and ), considering the probability of each scenario; assuming that the day-ahead forecast errors for renewable energy and demand follow a normal distribution with mean zero; each scenario can be represented by the sum of the forecast value and a random value generated from the distribution function of the forecast error;

[0012] Objective function:

[0013] It is to maximize the profit of commercial virtual power plants in the day-ahead and balancing energy markets, which can be expressed as follows:

[0014]

[0015] Where t∈T is the time interval, s∈S is the scenario with uncertain parameters, is the day-ahead market price at time t ($ / MWh), is the upstream reserve energy price at time t ($ / MWh), is the energy storage price ($ / MWh) at time t, Δt is the time period equal to 15 minutes, π s is the probability of the case s with uncertain parameters, p act To balance the probability of reserve activation in the energy market, is the sales power of the commercial virtual power plant in the data processing market at time t (MW), is the purchasing power of commercial virtual power plants in the data exchange market at time t (MW), is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s, is the downstream reserve power of the commercial virtual power plant in the balancing energy market at time s under scenario (MW);

[0016] S3: Constraints.

[0017] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized in that: the S3 constraint includes

[0018] First stage constraints:

[0019] The trading strategy of commercial virtual power plants in the day-ahead market is completed under the first-stage constraints;

[0020] Formula (17) describes the participation of renewable energy and energy storage systems in the day-ahead market to sell electricity to the system. Their rated power should limit the sales capacity of commercial virtual power plants;

[0021] Formula (18) shows that the commercial virtual power plant purchases electricity from the system to supply customers and accumulates it in the energy storage system; the purchasing power of the commercial virtual power plant is limited by the sum of customer demand and the maximum charging power of the energy storage system; the binary variable The day-ahead ensures that commercial virtual power plants cannot buy and sell electricity simultaneously in the day-ahead market; in addition, the day-ahead trading contract is determined for each 1h interval, while each time block in the optimization model is 15min; formulas (19) and (20) ensure that the trading capacity of commercial virtual power plants should be similar in the four 15-minute blocks of the 1h interval;

[0022]

[0023] In the formula, is the renewable energy output power provided to the main grid at time t in scenario s (MW), is the energy storage system discharge power sold to the data processing market at time t in scenario s (MW), is a binary variable representing the selling / buying situation of the commercial virtual power plant in the data market at time t. 0 means that the virtual power plant is not selling electricity during this period, and 1 means that the virtual power plant is selling electricity during this period. ESSmax is the rated power of the energy storage system (MW), P RESrated is the rated power of renewable energy (MW), is the power output purchased in the day-ahead market to meet the demand at time t in scenario s (MW), is the charging power of the energy storage system purchased from the day-ahead market at time t in scenario s (MW), Load max is the highest possible value of demand (MW).

[0024] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized in that: the S3 constraint includes the second stage constraint:

[0025] Considering the uncertainty of renewable energy and demand, the operating parameters of each component in the commercial virtual power plant and the ability of the commercial virtual power plant in balancing the energy market are described in the second stage constraints as follows;

[0026] S1: Active power balance constraint in the day-ahead market: Formula (21) shows that at any time t, the net production / consumption of commercial virtual power plants should be equal to the sales / purchase power in the day-ahead contract;

[0027] Formula (22) shows that customers can use renewable energy, energy storage systems or market supply;

[0028]

[0029] In the formula, is the actual output power at time t in renewable energy scenario s, in MW; is the energy storage system discharge power at time t in scenario s (MW), is the charging power of the energy storage system at time s in scenario s (MW), is the forecast demand at time t in scenario s (MW), is the renewable energy power output that meets the demand at time t in scenario s (MW). In scenario s (MW), the energy storage system discharges to the demand at time t.

[0030] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized by: renewable energy operation:

[0031] At time t, the actual power output of renewable energy corresponds to the uncertain parameters of scenario s: Formula (23) shows that the upstream reserve power supply capacity of renewable energy is limited by the difference between the predicted available power output, Pt, and actual power output Similarly, equation (24) shows that the limit on downstream reserve capacity of renewable energy is the difference between the actual power output and the minimum power threshold P RESmin ;

[0032] Formula (25) shows that renewable energy provides a part of the commercial virtual power plant contract in the day-ahead market while meeting the customer's electricity demand in the commercial virtual power plant market;

[0033] Excess energy from renewable energy sources can be accumulated in energy storage systems to utilize the available power output of renewable energy sources;

[0034]

[0035] In the formula, is the on / off state of renewable energy at time t in scenario s (binary); P RESmin The minimum level of renewable energy output power (MW); is the actual output power at time t in renewable energy scenario s, in MW; is the renewable energy upward reserve power at time t under scenario s (MW); is the predicted output power of renewable energy at time t under scenario t (MW); is the renewable energy reserve power at time t under scenario s (MW); is the renewable energy output power provided to the main grid at time t in scenario s (MW); is the renewable energy power output that meets the demand at time t in scenario s (MW); In scenario s (MW), the renewable energy power output is provided to the energy storage system at time t.

[0036] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized by:

[0037] The operation mode of energy storage systems in the energy and reserve markets is described by two sets of constraints as follows:

[0038] The operating parameters of the energy storage system in the day-ahead market are described in equations (26)–(29); equations (26) and (27) limit the charging / discharging power of the energy storage system, and equation (28) describes the change Δt of the energy storage system energy level after each time block; equation (29) shows that the energy level of the energy storage system should be set to the initial level at the end of each day to ensure its operation on the next day. This paper assumes that the initial energy level is 50% of the rated capacity of the energy storage system;

[0039]

[0040] In the formula, is the energy storage system discharge power at time t in scenario s (MW), In scenario s (MW), the energy storage system discharge power is sold to the data processing market at time t. is the energy storage system discharging to the demand at time t in scenario s (MW); is the charge and discharge state of the energy storage system at time t in scenario s (binary); P ESSmax is the rated power of the energy storage system (MW); is the charging power of the energy storage system at time s in scenario s (MW), is the energy storage system discharge power sold to the data processing market at time t in scenario s (MW); is the energy storage system power (MW) charged from renewable energy at time t in scenario s; E ESSmin is the minimum energy level of the energy storage system (MWh); is the initial energy (MWh) of the energy storage system in the data collection market under scenario s at t = 0; is the initial energy (MWh) of the energy storage system in the data collection market under scenario s at t = 1, η is the charging and discharging efficiency of the energy storage system, E ESSmax is the rated capacity of the energy storage system (MWh); is the energy storage system energy at the end of the day in scenario s (t = 96) (MWh); is the energy storage system energy at the beginning of the day in scenario s (t=0) (MWh).

[0041] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized by: the relationship between the up / down reserve reserve capacity of the energy storage system in the balancing energy market and its scheduling and state transition in the day-ahead market is described by equations (2)–(15);

[0042] Formulas (2)–(15) are used to simulate the operation of energy storage systems in reserve supply;

[0043] Formula (2) ensures that if the energy storage system is required to provide upward reserve when discharging, its new state should be the discharge state; this means that if Then case 2 is the same as and is not feasible; similarly, if the energy storage system is required to provide downward reserve when charging, its new state should be the charging state; with bigM as a sufficiently large constant, formulas (3) and (4) ensure that the energy storage system can only provide one type of reserve energy at any time: upward or downward, corresponding to the reserve energy transaction of commercial virtual power plants;

[0044]

[0045] In the formula, is the downside reserve power of the commercial virtual power plant in the BE market at time t under scenario s (MW); The energy storage system status (binary) after providing uplink / downlink reserve at time t in scenario s; is the on / off state of RES at time t in scenario s (binary); and is the downlink reserve power of the energy storage system at time st in the scenario (MW); and is the uplink reserve power of the energy storage system at time t under scenario s (MW);

[0046] Depending on the state of the energy storage system before and after activation, the upstream reserve provided by the energy storage system can be or both; the limitations of these upward reserve components are expressed by equations (5)–(8); and When equal to 1, equation (7) is non-binding, while equation (8) is mandatory No more than also mean Let equation (6) is equal to zero;

[0047] use and Equation (6) can be simplified to Formula (8) ensures that Limited by P ESSmax ; Equations (5) and (7) do not affect equations (6) and (8);

[0048] use and is equal to 0, then equations (6) and (8) become non-binding; formula (5) limits Depend on Equation (7) can be simplified to therefore is equal to zero;

[0049]

[0050]

[0051] The amount of downside reserve provided by the energy storage system can be or both, subject to the constraints of equations (9)–(12);

[0052]

[0053] Assuming that in the energy market, according to a predetermined operation schedule, the energy level of the energy storage system after time interval t is After providing up / down reserve, the energy storage system energy level will change It can be seen that It is positive in case of downward provisioning and vice versa, it is negative in case of upward provisioning;

[0054] Formula (13) determines the adjustment η is the energy storage system charging and discharging efficiency and Δt is the time interval duration; Equation (14) ensures that the energy storage system reserve energy supply does not affect its dispatch in the energy market; Formula (15) shows that the energy level of the energy storage system should be limited by its rated capacity at any time;

[0055]

[0056]

[0057] In the formula The change in energy storage system energy after providing uplink / downlink reserve at time t for scenario s (MW); The change in energy storage system energy after providing uplink / downlink reserve at time t-1 under scenario s (MW); is the energy storage system energy at the beginning of the day in scenario s (t = 0 (MWh); is the energy storage system energy at the end of the day in scenario s (t=96) (MWh).

[0058] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized in that the commercial virtual power plant supplies the reserve energy in the balanced energy market:

[0059] Equations (30) and (31) describe the ability of commercial virtual power plants to balance the energy market. Reserve energy can be provided by renewable energy, energy storage systems and customers. The binary parameter In (30), it indicates whether the commercial virtual power plant has a balancing capacity contract to provide upward reserve capacity at time t; during the reserve capacity upward adjustment contract period, Equal to 1, the commercial virtual power plant needs to provide reserve energy at least equal to the signed reserve capacity SR t In contrast, during the rest of the day, the CVP can fully utilize its ability to provide both upward and downward reserve energy (32); however, formula (1) ensures that the CVP can only provide one of the above reserve types at any time period;

[0060]

[0061] In the formula, is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t; SR t is the requirement for upstream reserve capacity in the balancing capacity contract at time t (MW); is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s; is the renewable energy upward reserve power at time t under scenario s (MW); is a binary variable equal to 1 if the commercial virtual power plant provides upward reserve energy at time t in scenario s; is the downstream reserve power of the commercial virtual power plant in the balancing energy market at time s under scenario (MW); is the renewable energy reserve power at time t under scenario s (MW), is a binary variable, which is equal to 1 if the commercial virtual power plant provides downward reserve energy at time t in scenario s; is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t.

[0062] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized by: the reserve supply model of the energy storage system:

[0063] If the transmission system operator requests upward reserve when the energy storage system is charging, the maximum upward reserve power of the energy storage system is If the transmission system operator requests upward reserve when the energy storage system is discharged, the maximum upward reserve power of the energy storage system is If the transmission system operator requests downward reserve when the energy storage system is charging, the maximum downward reserve power of the energy storage system is If the transmission system operator requests downward reserve when the energy storage system is discharged, the maximum downward reserve power of the energy storage system is These values ​​are the maximum reserves that a storage system can provide to the balancing market; in practice, the available upper / lower reserves of a storage system depend on its energy level and DA operating schedule;

[0064] Assume that at time interval t, the commercial virtual power plant provides reserve energy and the energy storage system contributes to this transaction; whether the commercial virtual power plant provides upward or downward reserves is described by the binary variable and Binary variables =1 if the commercial virtual power plant provides upward reserve, 0 otherwise; binary variable If the commercial virtual power plant provides downward reserve, it is equal to 1, otherwise it is 0; constraint (1) indicates that the commercial virtual power plant cannot provide both upward and downward reserve energy at the same time:

[0065]

[0066] Before providing backup energy, the energy storage system is arranged on the energy market at a charging level or discharge level Binary variables Displays the state of the energy storage system: 1 if the energy storage system is discharging; after the reservation is activated, the energy storage system will switch to a new state described by a binary variable

[0067]

[0068] The virtual power plant optimization scheduling strategy considering energy storage system is characterized in that: in S2, since the time unit of the balancing energy market is usually 15 minutes, the second stage variables are determined with a granularity of quarter of an hour. Unlike the first stage variables, the operating parameters of the second stage will be adjusted to correspond to the actual values ​​of the uncertain parameters.

[0069] The virtual power plant optimization scheduling strategy considering the energy storage system is characterized in that: in S2, the objective function consists of two items: (i) the first item is the profit obtained from the development agenda market; (ii) the second item is the expected profit of selling reserve services in the balancing energy market; the probability of reserve activation pact is used as a weight factor in the objective function, and all contracted upstream reserve capacity and non-contracted upstream / downstream reserve energy bids are selected and fully activated, p act All quarter-hour intervals are assumed to be known; the calculation range consists of 96 15-minute time blocks.

[0070] The beneficial effects achieved by the present invention are:

[0071] Based on the demand of balancing capacity contracts and power demand forecast data, this paper proposes a two-stage stochastic optimization model to determine the optimal dispatch of virtual power plants in the day-ahead (DA) and balancing energy (BE) markets. The model takes into account the probability of reserve activation in the balancing energy market. In order not to affect its dispatch in the data exchange market, a reserve provision model for energy storage systems is proposed.

[0072] The present invention aims to determine the sales / purchase strategy of a commercial virtual power plant in the day-ahead market when the available power output of renewable energy sources and customer consumption information are only forecast data. In addition, the reserve energy supply capacity of the commercial virtual power plant in the balancing energy market is estimated, taking into account the probability that the commercial virtual power plant is selected to provide reserve energy. The reserve capacity contract of the commercial virtual power plant in the balancing capacity market needs to be secured. The operation schedule of the renewable energy sources and the charge and discharge status of the energy storage system are coordinated to maximize the total revenue of the commercial virtual power plant in the day-ahead and balancing energy markets; BRIEF DESCRIPTION OF THE DRAWINGS

[0073] The following is a brief description of the contents expressed in each figure in the specification of the present invention:

[0074] Figure 1 It is a flow chart corresponding to the ability of the energy storage system to provide reserves, the type of reserves, and the state of the energy storage system before the reserves are activated.

[0075] Figure 2 It is the state diagram of the energy storage system before and after the reserved activation. DETAILED DESCRIPTION

[0076] The present invention relates to an optimal scheduling strategy for a virtual power plant considering an energy storage system. Few studies have considered the role of energy storage systems in energy and reserve markets. Based on this, the present invention proposes an optimal scheduling strategy for a virtual power plant considering an energy storage system.

[0077] In order to achieve the above object, the steps of the present invention are specifically as follows:

[0078] S1: This section proposes an optimal dispatch model for commercial virtual power plants in the day-ahead and balancing energy markets, including renewable energy, energy storage systems, and flexible demand. The model aims to determine the sales / purchase strategy of commercial virtual power plants in the day-ahead market when the available power output of renewable energy and customer consumption information are only forecast data. In addition, considering the probability that the commercial virtual power plant is selected to provide reserve energy, the reserve energy supply capacity of the commercial virtual power plant in the balancing energy market is estimated. In addition, the reserve capacity contract of the commercial virtual power plant in the balancing capacity market needs to be guaranteed. The operation schedule of renewable energy and the charge and discharge status of the energy storage system are coordinated to maximize the total revenue of the commercial virtual power plant in the day-ahead and balancing energy markets;

[0079] S2: In order to address the uncertainty of renewable energy and demand, the optimization model of the commercial virtual power plant is formulated as a two-stage stochastic optimization problem. The trading strategy of the commercial virtual power plant in the day-ahead market is a first-stage variable, which should be determined every hour within a 24-hour time frame and maintained even if the actual data of renewable energy and demand differ from the forecast results. In contrast, the energy reserves of the commercial virtual power plant above and below the balancing energy market, as well as the operating parameters of the renewable energy and energy storage systems, can be used as second-stage variables. Since the time unit of the balancing energy market is usually 15 minutes, the second-stage variables are determined at a granularity of a quarter of an hour. Unlike the first-stage variables, the operating parameters of the second stage will be adjusted to correspond to the actual values ​​of the uncertain parameters. Therefore, the renewable energy and demand data should be presented as a set of scenarios ( and ), taking into account the probability of each scenario occurring. This ensures the real-time reliability of the best outcome. This paper assumes that the day-ahead forecast errors for renewable energy and demand follow a normal distribution with mean zero. Therefore, each scenario can be represented by the sum of the forecast value and a random value generated from the distribution function of the forecast error;

[0080] S3: Objective function The objective function is to maximize the profit of the commercial virtual power plant in the day-ahead and balancing energy market, which can be expressed as follows:

[0081]

[0082] S4: where t∈T is the time interval, s∈S is the scenario with uncertain parameters, is the day-ahead market price at time t ($ / MWh), is the upstream reserve energy price at time t ($ / MWh), is the energy storage price ($ / MWh) at time t, Δt is the time period (equal to 15 minutes), π s is the probability of the case s with uncertain parameters, pact To balance the probability of reserve activation in the energy market, is the sales power of the commercial virtual power plant in the data processing market at time t (MW), is the purchasing power of commercial virtual power plants in the data exchange market at time t (MW), is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s, is the downstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time s under scenario.

[0083] The objective function consists of two terms: (i) the first term is the profit obtained from the DA market; (ii) the second term is the expected profit from selling reserve services in the balancing energy market. act As weight factors in the objective function, all contracted uplink reserve capacity and non-contracted uplink / downlink reserve energy bids are selected and fully activated, p act It is assumed to be known in all quarters of an hour. In order to simplify the model, reserve scenarios with deactivation or partial activation are not considered in this paper. In addition, the calculation scope consists of 96 15-minute time blocks;

[0084] S5: Constraint;

[0085] S6: first stage constraints;

[0086] S7: In this model, the trading strategy of the commercial virtual power plant in the day-ahead market is completed under the first-stage constraints. Formula (17) describes that renewable energy and energy storage systems participate in the day-ahead market to sell electricity to the system, so their rated power should limit the sales capacity of the commercial virtual power plant. Similarly, formula (18) shows that the commercial virtual power plant purchases electricity from the system to supply customers and accumulates it in the energy storage system; therefore, the purchasing power of the commercial virtual power plant is limited by the sum of customer demand and the maximum charging power of the energy storage system. Binary variable The day-ahead ensures that the commercial virtual power plant cannot buy and sell electricity simultaneously in the day-ahead market. In addition, the day-ahead trading contract is determined for each 1h interval, while each time block in the optimization model is 15min. Therefore, formulas (19) and (20) ensure that the trading capacity of the commercial virtual power plant should be similar in the four 15-minute blocks of the 1h interval.

[0087]

[0088] In the formula, is the renewable energy output power provided to the main grid at time t in scenario s (MW), is the energy storage system discharge power sold to the data processing market at time t in scenario s (MW), is a binary variable representing the selling / buying situation of commercial virtual power plants in the data market at time t, 1 represents selling, 1 represents buying, P ESSmax is the rated power of the energy storage system (MW), P RESrated is the rated power of renewable energy (MW), is the power output purchased in the day-ahead market to meet the demand at time t in scenario s (MW), is the charging power of the energy storage system purchased from the day-ahead market at time t in scenario s (MW), Load max is the highest possible value of demand (MW);

[0089] S8: Second stage constraints:

[0090] S9: In this section, considering the uncertainty of renewable energy and demand, the operating parameters of each component in the commercial virtual power plant and the ability of the commercial virtual power plant in balancing the energy market are described in the second stage constraints as follows;

[0091] S10: Active power balance constraint in the day-ahead market: Formula (21) shows that at any time t, the net production / consumption of commercial virtual power plants should be equal to the sales / purchase power in the day-ahead contract. Formula (22) shows that customers can supply through renewable energy, energy storage systems or the market;

[0092]

[0093] In the formula, is the actual output power at time t in renewable energy scenario s, in MW. is the energy storage system discharge power at time t in scenario s (MW), is the charging power of the energy storage system at time s in scenario s (MW), is the forecast demand at time t in scenario s (MW), is the renewable energy power output that meets the demand at time t in scenario s (MW). In scenario s (MW), the energy storage system discharges to the demand at time t.

[0094] S11: Renewable energy operation: At time t, the actual power output of renewable energy corresponds to the uncertain parameters of scenario s. Formula (23) shows that the upstream reserve power supply capacity of renewable energy is limited by the difference between the predicted available power output, Pt, and actual power output Similarly, equation (24) shows that the limit on downstream reserve capacity of renewable energy is the difference between the actual power output and the minimum power threshold P RESminFormula (25) shows that renewable energy provides a portion of the commercial virtual power plant contract in the day-ahead market while meeting the customer's electricity demand in the commercial virtual power plant market. In addition, excess energy from renewable energy can be accumulated in the energy storage system to utilize the available power output of renewable energy;

[0095]

[0096] In the formula, is the on / off state of renewable energy at time t in scenario s (binary). RESmin The minimum level of renewable energy output power (MW). is the actual output power at time t in renewable energy scenario s, in MW. is the renewable energy reserve power (MW) at time t under scenario s. is the predicted output power of renewable energy at time t under scenario t (MW). is the renewable energy reserve power (MW) at time t under scenario s. In scenario s (MW), the renewable energy output power is provided to the main grid at time t. is the renewable energy power output that meets the demand at time t in scenario s (MW). In scenario s (MW), the renewable energy power output is provided to the energy storage system at time t.

[0097] S12: Energy storage system operation: The operation mode of the energy storage system in the energy and reserve market is described by two sets of constraints as follows: The operation parameters of the energy storage system in the day-ahead market are described in Equations (26)–(29). Equations (26) and (27) limit the charging / discharging power of the energy storage system, while Equation (28) describes the change Δt of the energy storage system energy level after each time block. Equation (29) shows that the energy storage system energy level should be set to the initial level at the end of each day to ensure its operation on the next day. This paper assumes that the initial energy level is 50% of the rated capacity of the energy storage system.

[0098]

[0099] In the formula, is the energy storage system discharge power at time t in scenario s (MW), In scenario s (MW), the energy storage system discharge power is sold to the data processing market at time t. In scenario s (MW), the energy storage system discharges to the demand at time t. is the charging and discharging state of the energy storage system at time t in scenario s (binary). ESSmax is the rated power of the energy storage system (MW). is the charging power of the energy storage system at time s in scenario s (MW), In scenario s (MW), the energy storage system discharge power is sold to the data processing market at time t. is the energy storage system power (MW) charged from renewable energy at time t in scenario s. ESSmin is the minimum energy level of the energy storage system (MWh). At t=0, the initial energy (MWh) of the energy storage system in the data collection market under scenario s. is the initial energy (MWh) of the energy storage system in the data collection market under scenario s at t = 1, η is the charging and discharging efficiency of the energy storage system, E ESSmax is the rated capacity of the energy storage system (MWh). is the energy storage system energy at the end of the day in scenario s (t=96) (MWh). is the energy storage system energy at the beginning of the day in scenario s (t=0) (MWh).

[0100] S13: The relationship between the up / down reserve reserve capacity of the energy storage system in the balancing energy market and its dispatch in the day-ahead market and state transition is described by equations (2)–(15). In addition, this study only focuses on the operation schedule of commercial virtual power plants within a day, so the degradation and self-discharge level of the energy storage system can be ignored.

[0101] S14: Reserve energy supply of commercial virtual power plants in balancing energy markets: The following equation describes the ability of commercial virtual power plants in balancing energy markets. Equations (30) and (31) show that reserve energy can be provided by renewable energy, energy storage systems, and customers. Binary parameters In (30), it indicates whether the commercial virtual power plant has a balancing capacity contract to provide upward reserve capacity at time t. During the reserve capacity upward adjustment contract period, Equal to 1, the commercial virtual power plant needs to provide reserve energy at least equal to the signed reserve capacity SR t In contrast, during the rest of the day, a commercial virtual power plant can fully utilize its ability to provide both upward and downward reserve energy (32). However, formula (1) ensures that a commercial virtual power plant can only provide one of the above reserve types at any time.

[0102]

[0103] In the formula, It is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t. t is the requirement for upstream reserve capacity in the balancing capacity contract at time t (MW). is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s. is the renewable energy reserve power (MW) at time t under scenario s. is a binary variable equal to 1 if the commercial virtual power plant provides upward reserve energy at time t in scenario s. is the downstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time s under scenario. is the renewable energy reserve power at time t under scenario s (MW), is a binary variable, which is equal to 1 if the commercial virtual power plant provides downward reserve energy at time t in scenario s. is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t.

[0104] S15: Reserve supply model of energy storage system:

[0105] Figure 1 The reserve readiness of the energy storage system is described, depending on the reserve type and the state of the energy storage system before the reserve activation.

[0106] S16: If the transmission system operator requests an upward reserve when the energy storage system is charging, the maximum upward reserve power of the energy storage system is

[0107] S17: If the transmission system operator requests upward reserve when the energy storage system is discharged, the maximum upward reserve power of the energy storage system is

[0108] S18: If the transmission system operator requests downward reserve when the energy storage system is charging, the maximum downward reserve power of the energy storage system is

[0109] S19: If the transmission system operator requests downward reserve when the energy storage system is discharged, the maximum downward reserve power of the energy storage system is

[0110] S20: However, these values ​​are the maximum reserves that the energy storage system can provide to the balancing market. In practice, the available upper / lower reserves of the energy storage system depend on its energy level and DA operation schedule;

[0111] S21: Assume that at time interval t, the commercial virtual power plant provides reserve energy and the energy storage system contributes to this transaction. Whether the commercial virtual power plant provides upward or downward reserves is described by the binary variable and Binary variables If the commercial virtual power plant provides upward reserve, it is equal to 1, otherwise it is equal to 0. Similarly, the binary variable If the commercial virtual power plant provides downward reserve, it is equal to 1, otherwise it is 0. Constraint (1) indicates that the commercial virtual power plant cannot provide both upward and downward reserve energy at the same time:

[0112]

[0113] S22: Energy storage systems are arranged on the energy market at a charging level before providing backup energy or discharge level Binary variables Displays the energy storage system status: 1 if the energy storage system is discharging. After the reservation is activated, the energy storage system will switch to a new state described by the binary variable The type of reserve energy sold to the transmission system operator and the state of the energy storage system before and after reserve activation lead to four scenarios, e.g. Figure 2 And as shown in Table 1.

[0114] Table 1 Four reservation situations of energy storage system.

[0115]

[0116] S23: Based on a set of feasible cases shown in Table 1, formulas (2)–(15) are proposed to simulate the operation of the energy storage system in the reserve supply. Formula (2) ensures that if the energy storage system is required to provide upward reserve when discharging, its new state should be the discharge state. This means that if Then case 2 is the same as and is not feasible. Similarly, if the energy storage system is required to provide downward reserve when charging, its new state should be the charging state. With bigM as a sufficiently large constant, formulas (3) and (4) ensure that the energy storage system can only provide one type of reserve energy at any time: upward or downward, corresponding to the reserve energy trading of commercial virtual power plants.

[0117]

[0118] In the formula, is the downstream reserve power (MW) of the commercial virtual power plant in the BE market at time t under scenario s. The energy storage system status (binary) after providing uplink / downlink reserve at time t in scenario s. is the on / off state of RES at time t in scene s (binary). and is the downstream reserve power of the energy storage system at time st in the scenario (MW). and is the uplink reserve power of the energy storage system at time t under scenario s (MW).

[0119] S24: Table 1 shows that the uplink reserve provided by the energy storage system can be Or both. The limitations of these upward reserves are expressed by equations (5)–(8). Specifically, we have the following:

[0120] S25: Case 1: In this case, and is equal to 1 (Table 1). Therefore, equation (7) is non-binding, while equation (8) is mandatory No more than also mean Let equation (6) is equal to zero.

[0121] S26: Case 3: Use and Equation (6) can be simplified to Formula (8) ensures that Limited by P ESSmax Furthermore, it can be seen that equations (5) and (7) do not affect equations (6) and (8).

[0122] S27: Case 4: Use and If is equal to 0, equations (6) and (8) become non-binding. Formula (5) limits Depend on Equation (7) can be simplified to therefore is equal to zero.

[0123]

[0124] S28: Similarly, according to each case in Table 1, the amount of downside reserve provided by the energy storage system can be or both, subject to the constraints of equations (9)–(12).

[0125]

[0126] S29: Assume that in the energy market, according to a predetermined operating schedule, the energy level of the energy storage system after time interval t is After providing up / down reserve, the energy storage system energy level will change It can be seen that It is positive in case of downward provisioning and vice versa, it is negative in case of upward provisioning. Formula (13) determines the adjustment η is the energy storage system charging and discharging efficiency and Δt is the time interval duration. Equation (14) ensures that the energy storage system reserve energy supply does not affect its dispatch in the energy market.

[0127] In addition, formula (15) shows that the energy level of the energy storage system should be limited by its rated capacity at any time.

[0128]

[0129] In the formula. The change in energy storage system energy after providing uplink / downlink reserves at time t for scenario s (MW). The change in energy storage system energy after providing uplink / downlink reserves at time t-1 under scenario s (MW). is the energy storage system energy at the beginning of the day in scenario s (t = 0 (MWh). is the energy storage system energy at the end of the day in scenario s (t=96) (MWh).

Claims

1. An optimal scheduling strategy for virtual power plants considering energy storage systems, Features: The steps are as follows: S1: Establish an optimal dispatch model for commercial virtual power plants in the day-ahead and balancing energy markets, including renewable energy, energy storage systems and flexible demand; aiming to maximize the total revenue of commercial virtual power plants in the day-ahead and balancing energy markets; S2: The optimization model of commercial virtual power plants is formulated as a two-stage stochastic optimization problem; The trading strategy of a commercial virtual power plant is a first-stage variable in the day-ahead market, and hourly trading decisions should be determined within a 24-hour time frame. The first-stage variable needs to take into account forecast data on the available power output of renewable energy and customer demand, and should be maintained in the event that these forecasts may differ from actual data; The commercial virtual power plant reserves energy above and below the balancing energy market, as well as the operating parameters of renewable energy and energy storage systems, as second-stage variables; Renewable energy and demand data should be presented as a set of scenarios ( and ), considering the probability of each scenario; assuming that the day-ahead forecast errors for renewable energy and demand follow a normal distribution with mean zero; each scenario can be represented by the sum of the forecast value and a random value generated from the distribution function of the forecast error; Objective function: It is to maximize the profit of commercial virtual power plants in the day-ahead and balancing energy markets, which can be expressed as follows: Where t∈T is the time interval, s∈S is the scenario with uncertain parameters, is the day-ahead market price at time t ($ / MWh), is the upstream reserve energy price at time t ($ / MWh), is the energy storage price ($ / MWh) at time t, Δt is the time period equal to 15 minutes, π s is the probability of the case s with uncertain parameters, p act To balance the probability of reserve activation in the energy market, is the sales power of the commercial virtual power plant in the data processing market at time t (MW), is the purchasing power of commercial virtual power plants in the data exchange market at time t (MW), is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s, is the downstream reserve power of the commercial virtual power plant in the balancing energy market at time s under scenario (MW); S3: Constraints, including the trading strategy of commercial virtual power plants in the day-ahead market, which are called first-stage constraints; including the uncertainty of renewable energy and demand, the operating parameters of each component in the commercial virtual power plant, and the ability of commercial virtual power plants in balancing the energy market, which are called second-stage constraints.

2. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: The S3 constraints include First stage constraints: The trading strategy of commercial virtual power plants in the day-ahead market is completed under the first-stage constraints; Formula (17) describes the participation of renewable energy and energy storage systems in the day-ahead market to sell electricity to the system. Their rated power should limit the sales capacity of commercial virtual power plants; Formula (18) shows that the commercial virtual power plant purchases electricity from the system to supply customers and accumulates it in the energy storage system; The purchasing power of a commercial virtual power plant is limited by the sum of customer demand and the maximum charging power of the energy storage system; binary variable The day-ahead ensures that commercial virtual power plants cannot buy and sell electricity simultaneously in the day-ahead market; in addition, the day-ahead trading contract is determined for each 1h interval, while each time block in the optimization model is 15min; formulas (19) and (20) ensure that the trading capacity of commercial virtual power plants should be similar in the four 15-minute blocks of the 1h interval; In the formula, is the renewable energy output power provided to the main grid at time t in scenario s (MW), is the energy storage system discharge power sold to the data processing market at time t in scenario s (MW), is a binary variable representing the selling / buying situation of the commercial virtual power plant in the data market at time t. 0 means that the virtual power plant is not selling electricity during this period, and 1 means that the virtual power plant is selling electricity during this period. ESSmax is the rated power of the energy storage system (MW), P RESrated is the rated power of renewable energy (MW), is the power output purchased in the day-ahead market to meet the demand at time t in scenario s (MW), is the charging power of the energy storage system purchased from the day-ahead market at time t in scenario s (MW), Load max is the highest possible value of demand (MW).

3. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: The S3 constraints include the second stage constraints: Considering the uncertainty of renewable energy and demand, the operating parameters of each component in the commercial virtual power plant and the ability of the commercial virtual power plant in balancing the energy market are described in the second stage constraints as follows; S1: Active power balance constraint in the day-ahead market: Formula (21) shows that at any time t, the net production / consumption of commercial virtual power plants should be equal to the sales / purchase power in the day-ahead contract; Formula (22) shows that customers can use renewable energy, energy storage systems or market supply; In the formula, is the actual output power at time t in renewable energy scenario s, in MW; is the discharge power of the energy storage system at time t in scenario s (MW), is the charging power of the energy storage system at time s in scenario s (MW), is the forecast demand at time t in scenario s (MW), is the renewable energy power output that meets the demand at time t in scenario s (MW). In scenario s (MW), the energy storage system discharges to the demand at time t.

4. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: Renewable Energy Operations: At time t, the actual power output of renewable energy corresponds to the uncertain parameters of scenario s: Formula (23) shows that the upstream reserve power supply capacity of renewable energy is limited by the difference between the predicted available power output, Pt, and actual power output Similarly, equation (24) shows that the limit on downstream reserve capacity of renewable energy is the difference between the actual power output and the minimum power threshold P RESmin ; Formula (25) shows that renewable energy provides a part of the commercial virtual power plant contract in the day-ahead market while meeting the customer's electricity demand in the commercial virtual power plant market; Excess energy from renewable energy sources can be accumulated in energy storage systems to utilize the available power output of renewable energy sources; In the formula, is the on / off state of renewable energy at time t in scenario s (binary); P RESmin The minimum level of renewable energy output power (MW); is the actual output power at time t in renewable energy scenario s, in MW; is the renewable energy upward reserve power at time t under scenario s (MW); is the predicted output power of renewable energy at time t under scenario t (MW); is the renewable energy reserve power at time t under scenario s (MW); is the renewable energy output power provided to the main grid at time t in scenario s (MW); is the renewable energy power output that meets the demand at time t in scenario s (MW); In scenario s (MW), the renewable energy power output is provided to the energy storage system at time t.

5. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: The operation mode of energy storage systems in the energy and reserve markets is described by two sets of constraints as follows: The operating parameters of the energy storage system in the day-ahead market are described in equations (26)–(29); equations (26) and (27) limit the charging / discharging power of the energy storage system, and equation (28) describes the change Δt of the energy storage system energy level after each time block; equation (29) shows that the energy level of the energy storage system should be set to the initial level at the end of each day to ensure its operation on the next day. This paper assumes that the initial energy level is 50% of the rated capacity of the energy storage system; In the formula, is the discharge power of the energy storage system at time t in scenario s (MW), In scenario s (MW), the energy storage system discharge power is sold to the data processing market at time t. is the energy storage system discharging to the demand at time t in scenario s (MW); is the charge and discharge state of the energy storage system at time t in scenario s (binary); P ESSmax is the rated power of the energy storage system (MW); is the charging power of the energy storage system at time s in scenario s (MW), is the energy storage system discharge power sold to the data processing market at time t in scenario s (MW); is the energy storage system power (MW) charged from renewable energy at time t in scenario s; E ESSmin is the minimum energy level of the energy storage system (MWh); is the initial energy (MWh) of the energy storage system in the data collection market under scenario s at t = 0; is the initial energy (MWh) of the energy storage system in the data collection market under scenario s at t = 1, η is the charging and discharging efficiency of the energy storage system, E ESSmax is the rated capacity of the energy storage system (MWh); is the energy storage system energy at the end of the day in scenario s (t = 96) (MWh); is the energy storage system energy at the beginning of the day in scenario s (t=0) (MWh).

6. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: The relationship between the up / down reserve reserve capacity of the energy storage system in balancing the energy market and its dispatch in the day-ahead market and state transition is described by equations (2)–(15); Formulas (2)–(15) are used to simulate the operation of energy storage systems in reserve supply; Formula (2) ensures that if the energy storage system is required to provide upward reserve when discharging, its new state should be the discharge state; this means that if Then case 2 is the same as and is not feasible; similarly, if the energy storage system is required to provide downward reserve when charging, its new state should be the charging state; with bigM as a sufficiently large constant, formulas (3) and (4) ensure that the energy storage system can only provide one type of reserve energy at any time: upward or downward, corresponding to the reserve energy transaction of commercial virtual power plants; In the formula, is the downside reserve power of the commercial virtual power plant in the BE market at time t under scenario s (MW); The energy storage system status (binary) after providing uplink / downlink reserve at time t in scenario s; is the on / off state of RES at time t in scenario s (binary); and is the downlink reserve power of the energy storage system at time st in the scenario (MW); and is the uplink reserve power of the energy storage system at time t under scenario s (MW); Depending on the state of the energy storage system before and after activation, the upstream reserve provided by the energy storage system can be or both; the limitations of these upward reserve components are expressed by equations (5)–(8); and When equal to 1, equation (7) is non-binding, while equation (8) is mandatory No more than also mean Let equation (6) is equal to zero; use and Equation (6) can be simplified to Formula (8) ensures that Limited by P ESSmax ; Equations (5) and (7) do not affect equations (6) and (8); use and is equal to 0, then equations (6) and (8) become non-binding; formula (5) limits Depend on Determine, and equation (7) can be simplified to therefore is equal to zero; The amount of downside reserve provided by the energy storage system can be or both, subject to the constraints of equations (9)–(12); Assuming that in the energy market, according to a predetermined operation schedule, the energy level of the energy storage system after time interval t is After providing up / down reserve, the energy storage system energy level will change It can be seen that It is positive in case of downward provisioning and vice versa, it is negative in case of upward provisioning; Formula (13) determines the adjustment η is the energy storage system charging and discharging efficiency and Δt is the time interval duration; Equation (14) ensures that the energy storage system's reserve energy supply does not affect its dispatch in the energy market; Formula (15) shows that the energy level of the energy storage system should be limited by its rated capacity at all times; In the formula The change in energy storage system energy after providing uplink / downlink reserve at time t for scenario s (MW); The change in energy storage system energy after providing uplink / downlink reserve at time t-1 under scenario s (MW); is the energy storage system energy at the beginning of the day in scenario s (t = 0 (MWh); is the energy storage system energy at the end of the day in scenario s (t=96) (MWh).

7. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: Commercial virtual power plants provide reserve energy supply in balancing energy markets: Equations (30) and (31) describe the ability of commercial virtual power plants to balance the energy market. Reserve energy can be provided by renewable energy, energy storage systems and customers. The binary parameter In (30), it indicates whether the commercial virtual power plant has a balancing capacity contract to provide upward reserve capacity at time t; during the reserve capacity upward adjustment contract period, Equal to 1, the commercial virtual power plant needs to provide reserve energy at least equal to the signed reserve capacity SR t In contrast, during the rest of the day, the CVP can fully utilize its ability to provide both upward and downward reserve energy (32); however, formula (1) ensures that the CVP can only provide one of the above reserve types at any time period; In the formula, is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t; SR t is the requirement for upstream reserve capacity in the balancing capacity contract at time t (MW); is the upstream reserve power (MW) of the commercial virtual power plant in the balancing energy market at time t under scenario s; is the renewable energy upward reserve power at time t under scenario s (MW); is a binary variable equal to 1 if the commercial virtual power plant provides upward reserve energy at time t in scenario s; is the downstream reserve power of the commercial virtual power plant in the balancing energy market at time s under scenario (MW); is the renewable energy reserve power at time t under scenario s (MW), is a binary variable, which is equal to 1 if the commercial virtual power plant provides downward reserve energy at time t in scenario s; is a binary parameter indicating whether the commercial virtual power plant has a balancing capacity contract at time t.

8. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: Reserve supply model of energy storage system: If the transmission system operator requests upward reserve when the energy storage system is charging, the maximum upward reserve power of the energy storage system is If the transmission system operator requests upward reserve when the energy storage system is discharged, the maximum upward reserve power of the energy storage system is If the transmission system operator requests downward reserve when the energy storage system is charging, the maximum downward reserve power of the energy storage system is If the transmission system operator requests downward reserve when the energy storage system is discharged, the maximum downward reserve power of the energy storage system is These values ​​are the maximum reserves that a storage system can provide to the balancing market; in practice, the available upper / lower reserves of a storage system depend on its energy level and DA operating schedule; Assume that at time interval t, the commercial virtual power plant provides reserve energy and the energy storage system contributes to this transaction; whether the commercial virtual power plant provides upward or downward reserves is described by the binary variable and Binary variables =1 if the commercial virtual power plant provides upward reserve, 0 otherwise; binary variable If the commercial virtual power plant provides downward reserve, it is equal to 1, otherwise it is 0; constraint (1) indicates that the commercial virtual power plant cannot provide both upward and downward reserve energy at the same time: Before providing backup energy, the energy storage system is arranged on the energy market at a charging level or discharge level Binary variables Displays the state of the energy storage system: 1 if the energy storage system is discharging; after the reservation is activated, the energy storage system will switch to a new state described by a binary variable 9. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: In the above S2, since the time unit of the balancing energy market is usually 15 minutes, the second-stage variables are determined with a granularity of a quarter of an hour. Unlike the first-stage variables, the operating parameters of the second stage will be adjusted to correspond to the actual values ​​of the uncertain parameters.

10. A virtual power plant optimization scheduling strategy considering energy storage system according to claim 1, Features: In S2, the objective function consists of two terms: (i) the first term is the profit obtained from the DA market; (ii) the second term is the expected profit from selling reserve services in the balancing energy market; the probability of reserve activation pact is used as a weight factor in the objective function, and all contracted upstream reserve capacity and non-contracted upstream / downstream reserve energy bids are selected and fully activated, p act All quarter-hour intervals are assumed to be known; the calculation range consists of 96 15-minute time blocks.

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