A real-time demand response method based on flexible resource adjustable control capability evaluation

Abstract

The application discloses a real-time demand response method based on flexible resource adjustable control capability evaluation, comprising the following steps: 1, a new type of distribution system interactive collaborative operation framework considering the participation of demand flexible resources in regulation and control is constructed, wherein a real-time response demand is issued by a distribution network operator, and a production and sales aggregator with multiple flexible resources responds to the task in real time; 2, a flexible resource adjustable control capability evaluation method participating in system collaborative interaction demand is proposed, including the adjustable control capability of multiple flexible resources in time and power; 3, according to the collected adjustable control capability data of the flexible resources, the auxiliary service price of the demand response is designed, and the response task of each aggregator is optimized. The application solves the real-time demand response problem based on multiple models under the new type of distribution system interactive collaborative operation framework, and achieves the purposes of peak load shifting and reducing the overall operation cost of the system.

Description

Technical Field

[0001] This invention relates to the field of demand management technology, specifically a real-time demand response method based on the assessment of the adjustability of flexible resources. Background Technology

[0002] As global climate issues worsen, most countries are implementing plans to accelerate the shift of their energy consumption towards cleaner, lower-carbon renewable energy sources. This will lead to significant changes in the structure and ecosystem of new power systems. Simply adjusting the power supply side will not only reduce system economic efficiency but also fail to guarantee reliable power supply and safe grid operation. Furthermore, compared to traditional generating units, renewable energy possesses a series of uncertainties, including randomness, intermittency, and volatility, creating numerous obstacles to its grid connection, dispatch, and consumption. Therefore, with a high proportion of renewable energy connected to the grid, the dual uncertainties on both the power generation and demand sides will further increase instability in the power system, threatening the safe operation of the power grid.

[0003] Demand response (DR) is an important means of addressing energy supply and demand imbalances. Demand response potential represents the available margin for electricity users to participate in demand response. Increasing the potential value of user participation in demand response can not only reduce overall system costs and alleviate grid operation pressure, but also promote the consumption of renewable energy and reduce total energy consumption. Whether users participate in response, the mode of participation, and the achievable response volume are all important factors in ensuring the smooth implementation of demand response. The profitability of participating in response determines whether users choose to participate, while the mode and scale of response are determined by their own response characteristics.

[0004] Accurately assessing demand response potential can provide a reference for formulating scientific and reasonable electricity pricing policies and a reliable basis for developing demand response incentive mechanisms, playing a crucial role in promoting the construction of new power systems. This invention solves the real-time demand response problem using multiple models within a new distribution system's interactive and collaborative operation framework, evaluating the controllability of flexible resources. Summary of the Invention

[0005] The purpose of this invention is to overcome the defects and shortcomings of the existing technology and provide a real-time demand response method based on the assessment of the controllability of flexible resources, so as to accurately assess the controllability of flexible resources, thereby completing real-time demand response and improving the economy of the power distribution system.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] 1. A real-time demand response method based on the assessment of the controllability of flexible resources, constructing a novel interactive and collaborative operation framework for distribution systems with flexible resources participating in regulation, wherein distribution network operators publish real-time response demands, and production and sales aggregators with multiple flexible resources respond to the tasks in real time, characterized by including the following steps:

[0008] S1: Based on the forecast of renewable energy, energy conversion equipment, energy storage systems and various load demands, the production and sales aggregator constructs the objective function of the production and sales aggregator and the pre-scheduling model of energy conversion equipment, energy storage systems and various loads. Based on the energy balance constraints, with the goal of minimizing the economic cost of each production and sales aggregator, day-ahead optimization scheduling is performed for each production and sales aggregator individually, and a pre-scheduling plan for equipment is formulated.

[0009] S2: Based on the pre-scheduling plan of each production and sales aggregator in step S1, from the perspective of the supporting potential of flexible resources to participate in the interactive operation of the power grid, we propose to evaluate the controllability of flexible resources to participate in the system's collaborative interaction needs, including the positive and negative controllability of each production and sales aggregator's flexible resources in terms of time and power, and output the controllability data of the flexible resources corresponding to each production and sales aggregator.

[0010] S3: Based on the data on the controllability of flexible resources collected in step S2, the distribution network operator sets the price of ancillary services for demand response.

[0011] S4: Based on the real-time positive and negative response demands released by the distribution network operator in steps S1-S3, and with the goal of minimizing the economic cost of distribution network services, calculate the ancillary service scheduling plan for each production and sales aggregator.

[0012] S5: The distribution network operator executes the real-time demand response of each production and sales aggregator and optimizes the real-time release of the distribution network operator's demand response tasks. The production and sales aggregator implements the response task requirements while meeting its own supply and demand balance.

[0013] Compared with the prior art, the beneficial effects of the present invention are:

[0014] 1. This invention constructs a novel interactive and collaborative operation framework for power distribution systems that considers the participation of flexible resources in demand regulation, and provides an interface for production and sales aggregators to participate in the demand-side management of the power distribution network, thereby achieving the purpose of peak shaving and valley filling and reducing the overall operating cost of the system.

[0015] 2. This invention proposes a method for evaluating the controllability of flexible resources in response to the collaborative interaction requirements of a system. It evaluates the flexible power and service time of flexible resources from both positive and negative aspects of flexibility, effectively assessing the controllability of flexible resources.

[0016] 3. Based on the assessed flexibility of each production and sales aggregator, this invention designs ancillary service prices for demand response, so as to meet its own supply and demand balance while responding to the demands issued by the power grid in real time at the lowest cost. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the interactive and collaborative operation framework of a new power distribution system;

[0018] Figure 2 This is a schematic diagram of the real-time demand response implementation process based on the assessment of the adjustability of flexible resources;

[0019] Figure 3 These are schematic diagrams illustrating the battery energy storage status of three production and sales aggregators under three different scenarios.

[0020] Figure 3 (a) is a schematic diagram of the battery energy storage status under three cases of production and sales aggregator 1;

[0021] Figure 3 (b) is a schematic diagram of the battery energy storage status under three cases of production and sales aggregator 2;

[0022] Figure 3 (c) is a schematic diagram of the battery energy storage status under three cases of production and sales aggregator 3;

[0023] Figure 4 These are schematic diagrams illustrating the P2G equipment output of three production and sales aggregators under three different case studies.

[0024] Figure 4 (a) is a schematic diagram of the output of the 1P2G equipment of the production and sales aggregator;

[0025] Figure 4 (b) is a schematic diagram of the output of the 2P2G equipment of the production and sales aggregator;

[0026] Figure 4 (c) is a schematic diagram of the output of the 3P2G equipment of the production and sales aggregator; Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] See appendix Figure 1 and attached Figure 2 ;

[0029] A real-time demand response framework based on the assessment of the controllability of flexible resources is proposed, which constructs a novel interactive and collaborative operation framework for distribution systems with flexible resource participation in regulation. In this framework, distribution network operators publish real-time response demands, and production and sales aggregators with various flexible resources respond to the tasks in real time. The framework includes the following steps:

[0030] S1: Pre-schedule for each production and sales aggregator, considering multiple renewable energy sources, energy conversion equipment, energy storage systems, and various load demands. The pre-schedule model is as follows:

[0031] S1-1: Objective function for pre-scheduling of production and sales aggregators:

[0032]

[0033] In the formula, F n The objective function for the nth user includes the cost of buying and selling electricity and the cost of battery degradation; E buy,t,n and E sell,t,n E represents the electricity volume purchased and sold by the nth user during time period t. ch,t,n and E dis,t,n It is the charge / discharge amount of the nth user during time period t; μ but,t and μ sell is the price of electricity; μ is the battery degradation coefficient; Δt represents the time interval, where Δt∈[1,T].

[0034] S1-2: Construct energy conversion and storage equipment models, including constraints for combined heat and power (CHP) and power to gas (P2G) equipment:

[0035] V gas,t,n =P P2G,t,n ·η gas ·Δt / Q gas (2)

[0036] P CHP,n,t =G CHP,n,t ·Q gas .η eCHP / Δt (3)

[0037] H CHP,t,n =G CHP,t,n ·Q gas .η hCHP / Δt (4)

[0038] 0≤P P2G,t,n ≤P P2G,max (5)

[0039] 0≤P CHP,t,n ≤P CHP,max (6)

[0040] In the formula, P P2G,t,n P represents the input power of the P2G at time t for the nth aggregator; CHP,t,n and H CHP,t,n G represents the electrical and thermal output of CHP at time t for the nth aggregator; CHP,t,n Q represents the gas delivery volume of CHP at time t for the nth aggregator; gas Represents the calorific value of the gas; η gas η represents the conversion efficiency of P2G. eCHP and η hCHP V represents the gas-to-electricity and gas-to-heat conversion efficiency of CHP, respectively; gas,t,n P represents the gas production of the nth aggregator at time t; P P2G,max P represents the maximum input power of the P2G; CHP,max This represents the maximum output power of the CHP;

[0041] Energy storage systems include batteries, gas storage tanks, and hot water storage tanks, and their state of energy storage is SOC (State of Charge). e,t SOC g,t and SOC h,t Below, i refers to e, h, g), and the state values ​​for each time period are as follows:

[0042]

[0043]

[0044] SOC i,min ≤SOC i,t ≤SOC i,max (9)

[0045] In the formula E bes E gas and E heat These are the capacities of the battery, gas tank, and hot water storage tank, respectively; E i,ch，t and E i,dis,t E represents the charging and discharging energy of device i during time period t. i,ch，max and E i,dis,max η represents the upper limit of charge / discharge energy of device i; ch and η dis These are the charge / discharge efficiency and η. w It is the heat loss rate of the hot water storage tank; SOC e,t-Δt SOC h,t-Δt and SOC g,t-Δt These represent the energy storage states of the battery, hot water tank, and gas storage tank during the time interval t-Δt; E e,ch,t-Δt and E e,dis,t-Δt These represent the charging and discharging times of the battery during the t-Δt period; E h,ch,t-Δt and Eh,dis,t-Δt These are the heat charge and release times of the hot water storage tank during the t-Δt period; E g,ch,t-Δt and E g,dis,t-Δt These represent the filling and discharging of the gas storage tank during the t-Δt time period;

[0046] S1-3: Construct various load models, including power-adjustable loads, time-adjustable loads, temperature-adjustable loads, and switching loads;

[0047] Adjustable load refers to a load that can be adjusted during peak or off-peak periods to flexibly regulate power, but the operating power must not be lower than the reference value E. p0 Below is the mathematical model for a power-adjustable load:

[0048] P pf,min ≤P pf,t ≤P pf,max (10)

[0049]

[0050] In the formula: P pf,t It is adjustable power; P pf,min and P pf,max These are the upper and lower limits of the adjustable power.

[0051] Time-adjustable loads refer to loads whose peak-hour energy consumption can be shifted to off-peak hours. Examples include household appliances such as dishwashers and vacuum cleaners. Based on time continuity, they are further divided into discontinuous and continuous loads. Their mathematical models are as follows:

[0052]

[0053]

[0054]

[0055] In the formula P tf and P tf0 These represent the actual power and fixed power of the time-adjustable load within the time period T, respectively. t and u t+1 0 and 1 variables at time t and t+1 respectively, representing the on and off states of the equipment; equations and represent time constraints, where t1 and t2 represent the operating duration of the electrical equipment. Within time T, when a time-continuous load starts at time t, it must operate for time t2 before it can stop; r refers to time (with the same meaning as t);

[0056] For ambient temperature-sensitive loads such as heating, ventilation, and air-conditioning (HVAC) systems, their energy consumption is closely related to external environmental factors. Below is the relationship between energy consumption data and ambient temperature changes, as well as the temperature setting range:

[0057] T tf,t =T tf,t-1 +α1(T out,t -T tf,t-1 )-α2·H tfcool,t +α2·H tfheat,t (15)

[0058] T min ≤T tf,t ≤T max (16)

[0059] In the formula, T tf and T tf，t-1 These are the actual temperatures at time t and time t-1; H tfcool and H tfheat These represent the heat loads for cooling and heating, respectively; α1 is the temperature coefficient; α2 is a 0-1 variable that controls the cooling and heating of the equipment; T min and T max These are the upper and lower limits of temperature; T out，t It is the ambient temperature at time t;

[0060] Another type of load can switch between electricity and gas consumption based on available energy resources and cost. The equipment can operate at a fixed electrical power P. s0 and gas power G s0 The mathematical model is as follows:

[0061]

[0062]

[0063]

[0064] In the formula, P s,t and G s,t This refers to the actual energy consumption; v t It is a 0-1 variable, when v t When the value is 0, the device is in gas mode; otherwise, it switches to power mode. s0 To minimize energy consumption; α3 is the gas-to-electricity conversion coefficient of the equipment;

[0065] S1-4: Based on the energy conversion efficiency and topology of the equipment, the energy balance constraints of the system are as follows:

[0066]

[0067] The left side of the equation represents the load, which includes the fixed electrical load L. e,t,n Fixed heat load L h,t,n Fixed gas load L g,t,n and various flexible loads in S1-3; right side P WT,t,n P PVT,t,n and P geo,t,n Indicates the power output of wind, solar, and geothermal renewable energy sources, η hsolar and η hgeo These represent the efficiency of photovoltaic and geothermal energy conversion into thermal energy, respectively.

[0068] S2: Based on the pre-scheduling plan of each production and sales aggregator, assess the adjustability of flexible resources:

[0069] S2-1: From the perspective of the supporting potential of flexible resources participating in grid interaction operation, assess the controllability of flexible resources, including the positive and negative controllability of each production-sales aggregator's flexible resources; the total controllability e of the nth production-sales aggregator at time t. t,n,flex It can be calculated by the formula, where Δt∈[1,T];

[0070] e t,n,flex =P t,n,flex ·λ t,n,flex ·Δt (21)

[0071] In the formula P t,n,flex Let λ represent the adjustable power of the nth aggregator at time t. t,n,flex Let Δt represent the adjustable time of the nth production and sales aggregator at time t, where Δt represents the time interval.

[0072] S2-2: The method for assessing the controllability of energy storage systems is as follows:

[0073] The battery has strong adjustability, providing both positive and negative flexibility, and can provide the maximum adjustable negative storage capacity ebes for the nth production and sales aggregator at time t. t,n,max- It is related to the current state of charge (SOC) of the battery, as shown in equation (22); equation (23) represents the maximum negative adjustable power Pbes of the battery at time t for the nth production and sales aggregator. t,n,flex- The negative adjustable power is not always equal to the battery's maximum charging power E. e,ch,n,max If the pre-scheduled battery operates in a discharge state, the available negative adjustable power is equal to the battery's maximum charging power plus the planned discharge power E. e,dis,t,n sum;

[0074] ebes t,n,max- =(SOC)e,n,max -SOC e,t,n )·E bes (twenty two)

[0075] Pbes t,n,flex- =E e,ch,n,max +E e,dis,t,n -E e,ch,t,n (twenty three)

[0076] λ k,bes- =N(Pbes) k,n,flex- ≥Pbes t,n,flex- )k∈[t+1,...T] (24)

[0077]

[0078] Equation (24) represents the number of time intervals λ during which the subsequent adjustable power of the battery is greater than or equal to the current adjustable power. k,bes- The adjustable time λbes satisfies both the maximum adjustable limit and the adjustable power. t,n,flex-， As shown in equation (25);

[0079] Where N is a counting symbol; λ k,bes- Indicates the battery's negative adjustable time; k refers to time (with the same meaning as t); SOC e,t，n State of Charge (SOC) represents the energy storage status of the nth production-sales aggregator at time t. e,n，max E represents the upper limit of the energy storage state of the nth production-sales aggregator; e,ch,t,n and E e,dis,t,n E represents the charging and discharging power of the nth production and sales aggregator at time t. e,ch,t,max E represents the upper limit of the charging power of the nth production and sales aggregator at time t; bes Battery capacity;

[0080] The battery also provides positive flexibility, the magnitude of which is related to the battery's current state of charge (SOC), and the adjustable discharge power is related to the battery's current state of charge / discharge.

[0081] ebes t,n,max+ =(SOC) e,t,n -SOC e,n,min )·E bes (26)

[0082] Pbes t,n,flex+ =E e,dis,n,max -E e,dis,t,n +E e,ch,t,n (27)

[0083] λ k,bes+ =N(Pbes) k,n,flex+ ≥Pbest,n,flex+ )k∈[t+1,…T] (28)

[0084]

[0085] Among them: ebes t,n,max+ SOC represents the maximum adjustable storage capacity in the positive direction for the nth production-sales aggregator at time t; e,n，min This represents the lower limit of the energy storage status of the nth production-sales aggregator; E e,dis,n,max E represents the upper limit of the discharge power of the nth production and sales aggregator at time t; bes Battery capacity; Pbes t,n,flex+ λ represents the maximum positive adjustable power of the nth aggregator at time t; k,bes+ This indicates the battery's forward adjustable time; k refers to time (with the same meaning as t); N is a counting symbol.

[0086] Similar to battery energy storage, gas storage tanks also possess bidirectional flexibility, which is related to the current capacity and charge / discharge status of the tank; the maximum negative adjustable gas storage capacity at time t for the nth production and sales aggregator is egas. t,n,max- Adjustable charge / discharge Pgas t,n,flex- and adjustable time λgas t,n,flex- As shown in the formula:

[0087] egas t,n,max- =(SOC) g,n,max -SOC g,t,n )·E gas (30)

[0088] Pgas t,n,flex- =(E g,ch,n,max +E g,dis,t,n -E g,ch,t,n )·Q gas (31)

[0089] λ k,gas- =N(Pgas) k,n,flex- ≥Pgas t,n,flex- )k∈[t+1,…T] (32)

[0090]

[0091] Where λ k,gas- Indicates the battery's forward adjustable time;

[0092] Positive flexibility is manifested in the dispatchable flexible gas storage capacity, which is related to planned gas filling and releasing. The positive controllability indicator is the maximum adjustable gas storage capacity (egas). t,n,max+ Adjustable power Pgas t,n,flex+ and adjustable time λgas t,n,flex+as follows:

[0093] egas t,n,max+ =(SOC) g,t,n -SOC g,n,min )·E gas (34)

[0094] Pgas t,n,flex+ =(E g,dis,n,max -E g,dis,t,n +E g,ch,t,n )·Q gas (35)

[0095] λ k,gas+ =N(Pgas) k,n,flex+ ≥Pgas t,n,flex+ )k∈[t+1,...T] (36)

[0096]

[0097] SOC g,t，n SOC represents the gas storage state at time t for the nth aggregator. g,n，min E represents the lower limit of the gas storage state of the nth gas aggregator; g,ch,t,n and E g,dis,t,n E represents the charging and discharging power of the nth production and sales aggregator at time t; gas The capacity of the gas storage tank; λ k,gas+ Indicates the forward adjustable time of the gas storage tank;

[0098] The assessability of thermal storage tanks is similar to that of batteries. Equations (38)-(41) are negative assessability indicators: maximum negative adjustable heat storage capacity eheat t,n,max- Negative adjustable power Pheat t,n,flex- and negative adjustable time λheat t,n,flex- .

[0099] eheat t,n,max- =(SOC) h,n,max -SOC h,t,n )·E heat (38)

[0100] Pheat t,n,flex- =E h,ch,n,max +E h,dis,t,n -E h,ch,t,n (39)

[0101] λ k,heat- =N(Pheat) k,n,flex- ≥Pheat t,n,flex- )k∈[t+1,...T] (40)

[0102]

[0103] SOC h,t，n SOC represents the thermal energy storage state of the nth production-sales aggregator at time t. h,n，max E represents the upper limit of the thermal energy storage state of the nth production and sales aggregator; h,ch,t,n and E h,dis,t,n E represents the added heat dissipation power of the nth production and sales aggregator at time t; heat The capacity of the thermal storage box; E h,ch,n,max λ represents the upper limit of the heating power of the nth production and sales aggregator; k,heat- Indicates the negative adjustable time of thermal storage;

[0104] Below are the evaluation indicators for the positive adjustable capacity of the thermal storage tank: Maximum positive adjustable heat storage capacity (eheat) t,n,max+ Positive adjustable power Pheat t,n,flex+ and positive adjustable time λheat t,n,flex+

[0105] eheat t,n,max+ =(SOC) h,t,n -SOC h,n,min )·E heat (42)

[0106] Pheat t,n,flex+ =E h,dis,n,max -E h,dis,t,n +E h,ch,t,n (43)

[0107] λ k,heat+ =N(Pheat) k,n,flex+ ≥Pheat t,n,flex+ )k∈[t+1,...T] (44)

[0108]

[0109] Among them: SOC h,n，min E represents the lower bound of the thermal energy storage state of the nth production-sales aggregator; h,dis,,n,max λ represents the upper limit of the heat dissipation power of the nth production and sales aggregator; k,heat+ This represents the positive adjustable time of the thermal storage at time t;

[0110] S2-3: The method for assessing the controllability of energy conversion equipment is as follows:

[0111] When the CHP equipment operates under a scheduled operation, the equipment's negative adjustable power PCHP t,n,flex- That is, the current optimal scheduling value P CHP,t,n .

[0112] PCHP t,n,flex- =P CHP,t,n (46)

[0113] The adjustable time of CHP is related to the switching state and the energy storage state of the thermal storage tank, so the adjustable time λCHP t,n,flex- It is the minimum value that satisfies the above two constraints.

[0114] λ k1,CHP- =N(k·a) CHP,t,n -t≥0)k∈[t+1,...T] (47)

[0115]

[0116] λCHP t,n,flex- =min{λ k1,CHP- ,λ k2,CHP-} (49)

[0117] In the formula a CHP,t,n For 0-1 variables, when a CHP,t,n When the value is 1, the equipment operates according to the scheduling plan; otherwise, the equipment does not operate. Where E... heat The capacity of the thermal storage tank, and the current optimal scheduling value P. CHP,t,n ;λ k1,CHP- and λ k2,CHP- This indicates the negative adjustable time for CHP to satisfy both the switching state and the energy storage state of the thermal storage tank; k refers to time (with the same meaning as t); N is a counting symbol.

[0118] Only CHP devices not involved in planned scheduling can provide positive flexibility; therefore, the positive adjustable power PCHP of CHP t,n,flex+ Maximum operating power

[0119] PCHP t,n,flex+ =P CHP,n,max (50)

[0120] Similarly, the adjustable time of the equipment is related to the on / off state of the equipment and the thermal energy stored in the heat storage tank; therefore, the adjustable time λCHP t,n,flex+ Both requirements must be met simultaneously.

[0121] λ k1,CHP+ =N(k·(1-a) CHP,t,n (-t≥0)k∈[t+1,...T] (51)

[0122]

[0123] λCHP t,n,flex+ =min{λ k1,CHP+ ,λ k2,CHP+} (53)

[0124] Where λ k1,CHP+ and λ k2,CHP+ Indicates the negative adjustable time for CHP to satisfy both the switching state and the energy storage state of the thermal storage tank; η eCHP and η hCHP These represent the gas-to-electricity and gas-to-heat conversion efficiencies of CHP, respectively.

[0125] The method for assessing the dispatchability of electro-gas conversion equipment is similar to that for CHP equipment. When electro-gas conversion equipment operates according to the pre-scheduled plan, it can provide negative flexibility (PP2G). t,n,flex- Unscheduled equipment can provide positive flexibility. The formula - indicates negative flexibility.

[0126] PP2G t,n,flex- =P P2G,t,n (54)

[0127]

[0128] In the formula a P2G,t,n E is a 0-1 variable used to control the switching of P2G devices. gas P represents the capacity of the gas storage tank. p2G,t,n This represents the current optimal scheduling value; λP2G t,n,flex- This represents the negative adjustable time of P2G at time t for the nth aggregator; k refers to time (with the same meaning as t); N() is a counting symbol;

[0129] Similarly, a positive flexibility indicator for P2G devices: Adjustable power P2G t,n,flex+ Service time λP2G t,n,flex+ as follows:

[0130] PP2G t,n,flex+ =P P2G,n,max (56)

[0131]

[0132] In the formula P p2G,t,max λP2G is the maximum input of P2G. t,n,flex+ This represents the positive adjustable time of P2G for the nth production and sales aggregator at time t;

[0133] S3: To depict in detail the interaction and response process between flexible resources and the distribution network, an ancillary service pricing mechanism is established. Based on the assessed adjustability potential, a corresponding ancillary service price (pr) is obtained. n,flex .

[0134] For each user n, the price of their ancillary services is determined by their adjustable potential e. n,flex It is determined that its size is related to the adjustable power and adjustable time of each device.

[0135]

[0136] pr n,flex =a·e n,flex 2 +b·e n,flex +c (59)

[0137] Where a, b, and c are price parameters, respectively; j represents the controllable device defined above, including energy storage devices, CHP, and P2G; P j,,t,n,flex λ represents the adjustable power of controllable device j of the nth production-sales aggregator at time t; j,,t,n,flex This represents the adjustable time of controllable device j at time t for the nth production and sales aggregator;

[0138] S4: The distribution network publishes real-time positive and negative energy demands, and calculates the ancillary service scheduling plan provided by each aggregator with the goal of minimizing the economic cost of distribution network services.

[0139] S4-1: Consider minimizing the economic cost of ancillary services:

[0140]

[0141] In the formula P n This represents the adjustable power of each aggregator; minS represents minimizing the objective function S;

[0142] S4-2: Each node's power aggregator needs to satisfy the grid power balance constraint:

[0143]

[0144]

[0145]

[0146] In the formula P mn Q mn p represents the active and reactive power flowing into node n. n,g / p n,d and q n,g / q n,d These represent the active and reactive power generation and demand of the node, respectively; U n U0 and r are the voltages at the node and the slack node, respectively. mn and x mn These are the resistance and reactance of the circuit; P nk and Q nk This represents the active and reactive power flowing out of node n; additionally, the power Pn provided by each aggregator needs to meet the assessed range of controllable capacity.

[0147]

[0148] In the formula: P j,,n,flex- and P j,,n,flex+ This represents the negative and positive adjustable power of the controllable device j of the nth production and sales aggregator at time t;

[0149] S5: Production and sales aggregators must respond to demands in real time while maintaining their own supply and demand balance. Therefore, based on the original constraints, production and sales aggregators add real-time response tasks, aiming to minimize economic costs, and then perform real-time optimization scheduling.

[0150] Example description and simulation result analysis;

[0151] To verify its effectiveness, this invention uses a system with 5 nodes for a computational example analysis. This example includes 3 producer-consumer aggregators, located at nodes 2, 4, and 5 respectively, with node 1 publishing response requests. To fully demonstrate the effectiveness of the proposed method, three cases are set up for comparative simulation analysis:

[0152] 1) Case 1: Real-time demand response based on the assessment of flexible resource adjustability in this invention;

[0153] 2) Case 2: Failure to provide real-time demand response;

[0154] 3) Case 3: Without assessing the adjustability of flexible resources, each production and sales aggregator responds to tasks on average.

[0155] All numerical experiments were conducted in MATLAB 2020b in a 64-bit Windows environment, using the Cplex solver and the YALMIP toolbox to solve the models. The battery status and CHP device output for the three case studies are as follows: Figure 3 As shown and Figure 4 As shown in Table 1, the operating costs of the production and sales aggregator are as follows.

[0156] Table 1

[0157]

[0158]

[0159] Depend on Figure 3 and Figure 4As can be seen, in Case 1 and Case 3, flexible resources implemented real-time response tasks when the distribution network issued demand response tasks. Table 1 shows that in Case 3, due to the average response to tasks, all operating costs still increased even with response incentives. However, in Case 1, because the controllability of various flexible resources was assessed based on their characteristics, and real-time demand response was implemented, the operating costs of each aggregator were significantly reduced.

[0160] Therefore, this invention demonstrates the effectiveness and superiority of real-time demand response based on the assessment of flexible resource controllability, which achieves the goal of peak shaving and valley filling, and reducing the overall operating cost of the system.

[0161] Although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0162] Therefore, the above description is only a preferred embodiment of this application and is not intended to limit the scope of this application; that is, all equivalent modifications made in accordance with the scope of the claims of this application shall be within the protection scope of the claims of this application.

Claims

1. A real-time demand response method based on the assessment of the controllability of flexible resources, constructing a novel interactive and collaborative operation framework for distribution systems with flexible resources participating in regulation, wherein distribution network operators publish real-time response demands, and production and sales aggregators with multiple flexible resources respond to the tasks in real time, characterized in that... Includes the following steps: S1: Based on various renewable energy sources, energy conversion equipment, energy storage systems, and various load demands, the production and sales aggregator constructs the objective function of the production and sales aggregator and the pre-scheduling model of energy conversion equipment, energy storage systems, and various loads. According to energy balance constraints, with the goal of minimizing the economic cost of each production and sales aggregator, day-ahead optimization scheduling is performed for each production and sales aggregator individually, and a pre-scheduling plan for equipment is formulated. S2: Based on the pre-scheduling plan of each production and sales aggregator in step S1, from the perspective of the supporting potential of flexible resources to participate in the interactive operation of the power grid, we propose to evaluate the controllability of flexible resources to participate in the system's collaborative interaction needs, including the positive and negative controllability of each production and sales aggregator's flexible resources in terms of time and power, and output the controllability data of the flexible resources corresponding to each production and sales aggregator. Based on the pre-scheduling plan of each production and sales aggregator, assess the adjustability of flexible resources: S2-1: From the perspective of the supporting potential of flexible resources participating in grid interaction operation, assess the controllability of flexible resources, including the positive and negative controllability of each production-sales aggregator's flexible resources; the total controllability e of the nth production-sales aggregator at time t. t,n,flex can be derived from formula Calculate, where ∆t∈[1,T]; In the formula P t,n,flex Let λ represent the adjustable power of the nth aggregator at time t. t,n,flex ∆t represents the adjustable time of the nth production and sales aggregator at time t, where ∆t represents the time interval. S2-2: Assess the controllability of the energy storage system; S2-3: Evaluate the controllability of energy conversion equipment; S3: Based on the data on the controllability of flexible resources collected in step S2, the distribution network operator sets the price of ancillary services for demand response. S4: Based on the real-time positive and negative response demands released by the distribution network operator in steps S1-S3, and with the goal of minimizing the economic cost of distribution network services, calculate the ancillary service scheduling plan for each production and sales aggregator. S5: The distribution network operator executes the real-time demand response of each production and sales aggregator and optimizes the real-time release of the distribution network operator's demand response tasks. The production and sales aggregator implements the response task requirements while meeting its own supply and demand balance.

2. The real-time demand response method based on the assessment of flexible resource adjustability as described in claim 1, characterized in that: The pre-scheduling for each production and sales aggregator takes into account various renewable energy sources, energy conversion equipment, energy storage systems, and various load demands. The pre-scheduling model is as follows: S1-1: Objective function for pre-scheduling of production and sales aggregators: In the formula, F n The objective function for the nth user includes the cost of buying and selling electricity and the cost of battery degradation; E buy,t,n and E sell,t,n E represents the electricity volume purchased and sold by the nth user during time period t. ch,t,n and E dis,t,n It is the charge / discharge amount of the nth user during time period t; μ but,t and μ sell It is the price of buying and selling electricity; μ is the battery degradation coefficient, and ∆t represents the time interval, where t∈{1,2,3…T}; S1-2: Construct a model for energy conversion and storage equipment, including constraints for combined heat and power (CHP) equipment and power-to-gas (HPC) equipment. Mode In the middle, P P2G,t,n P represents the input power of the P2G at time t for the nth aggregator; CHP,t,n and H CHP,t,n G represents the electrical and thermal output of CHP at time t for the nth aggregator; CHP,t,n Q represents the gas delivery volume of CHP at time t for the nth aggregator; gas Represents the calorific value of the gas; ƞ gas Represents the conversion efficiency of P2G; ƞ eCHP and ƞ hCHP V represents the gas-to-electricity and gas-to-heat conversion efficiency of CHP, respectively; gas,t,n P represents the gas production of the nth aggregator at time t; P P2G,max P represents the maximum input power of the P2G; CHP,max This represents the maximum output power of the CHP; The energy storage system includes batteries, gas storage tanks, and hot water storage tanks, and their states of energy storage are SOC (State of Charge). e,t SOC g,t and SOC h,t Below, i will refer to e, h, and g, and the state values ​​for each time period are as follows: In the formula: E bes E gas and E heat These are the capacities of the battery, gas tank, and hot water storage tank, respectively; E i,ch，t and E i,dis,t E represents the charging and discharging energy of device i during time period t. i,ch，max and E i,dis,max This represents the upper limit of the charge and discharge energy of device i; ƞ ch and ƞ dis These are the charge and discharge efficiencies; w It is the heat loss rate of the hot water storage tank; SOC e,t-∆t SOC h,t-∆t and SOC g,t-∆t These represent the energy storage states of the battery, hot water tank, and gas storage tank during the time period t-∆t; E e,ch,t-∆t and E e,dis,t-∆t These represent the charging and discharging times of the battery during the t-∆t period; E h,ch,t-∆t and E h,dis,t-∆t These represent the heat charge and release of the hot water storage tank during the t-∆t time period; E g,ch,t-∆t and E g,dis,t-∆t These represent the filling and discharging of the gas storage tank during the t-∆t time period; S1-3: Construct various load models, including power-adjustable loads, time-adjustable loads, temperature-adjustable loads, and switching loads; Adjustable load refers to a load that can be adjusted during peak or off-peak periods to flexibly regulate power, but the operating power must not be lower than the reference value E. p0 Below is the mathematical model for a power-adjustable load: In the formula: P pf,t It is adjustable power; P pf,min and P pf,max These are the upper and lower limits of the adjustable power; Time-adjustable loads refer to a type of load where peak-hour energy consumption can be shifted to off-peak hours. Based on the continuity of time, they are further divided into discontinuous time loads and continuous time loads. Below are their mathematical models: In equation (12), P tf，t and P tf0 These represent the actual power and fixed power of the time-adjustable load at time t within the time period T, respectively. t and u t+1 The variables are 0 and 1 at time t and time t+1, respectively, representing the device being on and off; Equations (13) and (14) represent time constraints, where t1 and t2 represent the working time of the electrical equipment. Within time T, when a time-continuous load starts at time t, it must work for time t2 before it can stop. r refers to time and has the same meaning as t. For ambient temperature-sensitive loads, including heating, ventilation, and air conditioning systems, their energy consumption is closely related to external environmental factors. Below is the relationship between energy consumption data and ambient temperature changes, as well as the temperature setting range: In the formula, T tf，t and T tf，t-1 These are the actual temperatures at time t and time t-1; H tfcool、t and H tfheat，t These represent the cooling and heating loads at time t; α1 is the temperature coefficient; α2 is a 0-1 variable that controls the cooling and heating of the equipment; T min and T max These are the upper and lower limits of temperature; T out，t It is the ambient temperature at time t; Another type of load can switch between electricity and gas consumption based on available energy and cost, and the equipment can operate at a fixed electrical power P. s0 and gas power G s0 The mathematical model is as follows: In the formula, P s,t and G s,t v is the actual energy consumption of electricity and gas at time t; t It is a 0-1 variable, when v t When the value is 0, the device is in gas mode; otherwise, it switches to power mode. s0 α3 represents the minimum energy consumption; α3 is the gas-to-electricity conversion coefficient for equipment energy consumption. S1-4: Based on the energy conversion efficiency and topology of the equipment, the energy balance constraints of the system are as follows: Mode The left side represents the load, including the stationary electrical load L. e,t,n Fixed heat load L h,t,n Fixed gas load L g,t,n and various flexible loads in S1-3; right side P WT,t,n P PVT,t,n and P geo,t,n Indicates the output of renewable energy sources such as wind, solar, and geothermal; ƞ hsolar and ƞ hgeo P represents the efficiency of photovoltaic and geothermal energy conversion into thermal energy, respectively; pf,t，n P tf，t，n P s,t,n This represents the adjustable power, actual power, and actual energy consumption of the nth production and sales aggregator at time t. H tfcool、t，n and H tfheat，t,n These are the cooling and heating loads of the nth production and sales aggregator at time t; G s,t，n It represents the actual energy consumption of electricity and gas at time t for the nth production and sales aggregator. P CHP,t,n and H CHP,t,n Let G represent the electrical and thermal output of CHP at time t for the nth aggregator. CHP,t,n V represents the gas delivery volume of CHP at time t for the nth aggregator; gas,t,n This represents the gas production of P2G at time t for the nth aggregator; E buy,t,n and E sell,t,n P represents the electricity volume purchased and sold by the nth user during time period t; P2G,t,n This represents the input power of the P2G at time t for the nth aggregator; E e,ch,t,n and E e,dis,t,n E represents the charge / discharge amount of the nth production and sales aggregator at time t; h,ch,t,n and E h,dis,t,n E represents the heat dissipation added by the nth production and sales aggregator at time t; g,ch,t,n and E g,dis,t,n Let represent the gas filling and releasing volume of the nth production and sales aggregator at time t.

3. The real-time demand response method based on the assessment of flexible resource adjustability as described in claim 1, characterized in that: In S2-2, the method for assessing the controllability of energy storage systems is as follows: The battery has strong adjustability, providing both positive and negative flexibility, and can provide the maximum adjustable negative storage capacity ebes for the nth production and sales aggregator at time t. t,n,max- It is related to the current state of charge (SOC) of the battery, as shown in equation (22); equation (23) represents the maximum negative adjustable power Pbes of the battery at time t for the nth production and sales aggregator. t,n,flex- The negative adjustable power is not always equal to the battery's maximum charging power E. e,ch,n,max If the pre-scheduled battery operates in a discharge state, the available negative adjustable power is equal to the battery's maximum charging power plus the planned discharge power E. e,dis,t,n sum; Equation (24) represents the number of time intervals λ during which the subsequent adjustable power of the battery is greater than or equal to the current adjustable power. k,bes- The adjustable time λbes satisfies both the maximum adjustable limit and the adjustable power. t,n,flex-， As shown in equation (25); Where N is a counting symbol; λ k,bes- Indicates the battery's negative adjustable time; k refers to time and has the same meaning as t; SOC e,t，n State of Charge (SOC) represents the energy storage status of the nth production-sales aggregator at time t. e,n，max This represents the upper limit of the energy storage status of the nth production and sales aggregator; E e,ch,t,n and E e,dis,t,n E represents the charging and discharging power of the nth production and sales aggregator at time t. e,ch,t,max This represents the upper limit of the charging power of the nth production and sales aggregator at time t; E bes Battery capacity; The battery also provides positive flexibility, the magnitude of which is related to the battery's current state of charge (SOC), and the adjustable discharge power is related to the battery's current state of charge / discharge. Among them: ebes t,n,max+ SOC represents the maximum adjustable storage capacity in the positive direction for the nth production-sales aggregator at time t; e,n，min This represents the lower limit of the energy storage status of the nth production-sales aggregator; E e,dis,n,max E represents the upper limit of the discharge power of the nth production and sales aggregator at time t; bes Battery capacity; Pbes t,n,flex+ λ represents the maximum positive adjustable power of the nth aggregator at time t; k,bes+ This indicates the battery's forward adjustable time; k refers to time and has the same meaning as t; N is a counting symbol. Similar to battery energy storage, gas storage tanks also possess bidirectional flexibility, which is related to the current capacity and charge / discharge status of the tank. The maximum negative adjustable gas storage capacity at time t for the nth production-sales aggregator is egas. t,n,max- Adjustable charge / discharge Pgas t,n,flex- and adjustable time λgas t,n,flex- As shown in the formula: Where λ k,gas- Indicates the battery's forward adjustable time; Positive flexibility is manifested in adjustable gas storage capacity, which is related to planned gas filling and releasing; the maximum adjustable gas storage capacity is egas. t,n,max+ Adjustable power Pgas t,n,flex+ and adjustable time λgas t,n,flex+ as follows: SOC g,t，n SOC represents the gas storage state at time t for the nth aggregator. g,n，min E represents the lower limit of the gas storage state of the nth gas aggregator; g,ch,t,n and E g,dis,t,n E represents the charging and discharging power of the nth production and sales aggregator at time t; gas The capacity of the gas storage tank; λ k,gas+ Indicates the forward adjustable time of the gas storage tank; The assessability of the thermal storage tank is similar to that of the battery. Equations (38)-(41) are negative assessability indicators: maximum negative adjustable heat storage capacity eheat t,n,max- Negative adjustable power Pheat t,n,flex- and negative adjustable time λheat t,n,flex- ; SOC h,t，n SOC represents the thermal energy storage state of the nth production-sales aggregator at time t. h,n，max E represents the upper limit of the thermal energy storage state of the nth production and sales aggregator; h,ch,t,n and E h,dis,t,n E represents the added heat dissipation power of the nth production and sales aggregator at time t; heat The capacity of the thermal storage box; E h,ch,n,max λ represents the upper limit of the heating power of the nth production and sales aggregator; k,heat- Indicates the negative adjustable time of thermal storage; Below are the evaluation indicators for the positive adjustable capacity of the thermal storage tank: Maximum positive adjustable heat storage capacity (eheat) t,n,max+ Positive adjustable power Pheat t,n,flex+ and positive adjustable time λheat t,n,flex+ Among them: SOC h,n，min E represents the lower bound of the thermal energy storage state of the nth production-sales aggregator; h,dis,n,max λ represents the upper limit of the heat dissipation power of the nth production and sales aggregator; k,heat+ This represents the positive adjustable time of the thermal storage at time t; In S2-3, the method for evaluating the controllability of energy conversion equipment is as follows: When the CHP equipment operates under a scheduled operation, the equipment's negative adjustable power PCHP t,n,flex- That is, the current optimal scheduling value P CHP,t,n ; The adjustable time of CHP is related to the switching state and the energy storage state of the thermal storage tank, so the adjustable time λCHP t,n,flex- It is the minimum value that satisfies the following two constraints; In the formula a CHP,t,n For 0-1 variables, when a CHP,t,n When the value is 1, the equipment operates according to the scheduling plan; otherwise, the equipment does not operate. Where E... heat The capacity of the thermal storage tank, and the current optimal scheduling value P. CHP,t,n ;λ k1,CHP- and λ k2,CHP- This indicates the negative adjustable time for CHP to satisfy both the switching state and the energy storage state of the thermal storage tank; k refers to time and has the same meaning as t; N is a counting symbol. Only CHP devices not involved in planned scheduling can provide positive flexibility; therefore, the positive adjustable power PCHP of CHP t,n,flex+ Maximum operating power P CHP,n,max ; Similarly, the adjustable time of the equipment is related to the on / off state of the equipment and the thermal energy stored in the storage tank; therefore, the adjustable time λCHP t,n,flex+ Both requirements must be met simultaneously; Where λ k1,CHP+ and λ k2,CHP+ This indicates the negative adjustable time for CHP to satisfy both the on / off state and the energy storage state of the thermal storage tank; eCHP and ƞ hCHP These represent the gas-to-electricity and gas-to-heat conversion efficiencies of CHP, respectively. The method for assessing the dispatchability of electro-gas conversion equipment is similar to that for CHP equipment. When electro-gas conversion equipment operates according to the pre-scheduled plan, it can provide negative flexibility (PP2G). t,n,flex- Unscheduled equipment can provide positive flexibility; equations (54)-(55) represent negative flexibility. In the formula a P2G,t,n E is a 0-1 variable used to control the switching of P2G devices. gas P represents the capacity of the gas storage tank. p2G,t,n This represents the current optimal scheduling value; λP2G t,n,flex- This represents the negative adjustable time of P2G at time t for the nth aggregator; k refers to time and has the same meaning as t; N is a counting symbol. Similarly, a positive flexibility metric for P2G devices is flexible power PP2G. t,n,flex+ Service time λP2G t,n,flex+ as follows: In the formula P p2G,t,max λP2G is the maximum input of P2G. t,n,flex+ This represents the positive adjustable time of P2G at time t for the nth production and sales aggregator.

4. The real-time demand response method based on the assessment of flexible resource adjustability as described in claim 1, characterized in that, To characterize the interaction and response process between flexible resources and the distribution network in detail, an ancillary service pricing mechanism is established. Based on the assessed adjustability potential, a corresponding ancillary service price (pr) is obtained. n,flex For each user n, the price of their ancillary services is determined by their adjustable potential e. n,flex It is determined that its size is related to the adjustable power and adjustable time of each device. Where a, b, and c are price parameters, respectively; j represents controllable equipment, including energy storage devices, CHP, and P2G; P j,t,n,flex λ represents the adjustable power of controllable device j of the nth production-sales aggregator at time t; j,t,n,flex This represents the adjustable time of controllable device j at time t for the nth production and sales aggregator.

5. The real-time demand response method based on the assessment of flexible resource adjustability as described in claim 1, characterized in that, The aforementioned distribution network publishes real-time positive and negative energy demands, and calculates the ancillary service scheduling plan provided by each production and sales aggregator with the goal of minimizing the economic cost of distribution network services. S4-1: Consider minimizing the economic cost of ancillary services: In the formula P n This represents the adjustable power of each production and sales aggregator; minS represents minimizing the objective function S; S4-2: Each node's power aggregator needs to satisfy the grid power balance constraint: In the formula P mn Q mn p represents the active and reactive power flowing into node n. n,g / p n,d and q n,g / q n,d These represent the active and reactive power generation and demand of the node, respectively; U n U0 and r are the voltages at the node and the slack node, respectively. mn and x mn These are the resistance and reactance of the circuit; P nk and Q nk This represents the active and reactive power flowing out of node n; additionally, the power Pn provided by each producer-consumer aggregator needs to meet the assessed range of controllable capacity. In the formula: P j,n,flex- and P j,n,flex+ Let represent the negative and positive adjustable power of the controllable device j of the nth production and sales aggregator at time t.

6. The real-time demand response method based on the assessment of flexible resource adjustability as described in claim 1, characterized in that, The aforementioned production and sales aggregators implement real-time response to demand while maintaining their own supply and demand balance; based on the original constraints, real-time response tasks are added, with the goal of minimizing economic costs, and then real-time optimization scheduling is carried out.

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