A technical virtual power plant control method considering multiple distributed energy sources

Abstract

This invention relates to a technical virtual power plant (TVPP) control method that considers multiple distributed energy sources, overcoming the shortcomings of existing technologies that fail to consider multiple distributed energy sources in TVPP control. The invention includes the following steps: generating typical scenarios for a stochastic optimization model based on uncertainties; constructing the objective function; constructing constraints; calculating the maximum profit of the TVPP and the output of various types of distributed energy sources; and controlling the technical virtual power plant. This invention improves the system's ability to integrate distributed energy sources through the proposed TVPP operation model, fully considering the impact of multiple distributed energy sources, and achieving effective control of the technical virtual power plant.

Description

Technical Field

[0001] This invention relates to the field of technology-based virtual power plant technology, specifically a technology-based virtual power plant control method that considers multiple distributed energy sources. Background Technology

[0002] The purpose of the Technical Virtual Power Plant (TVPP) operating model is to optimize the dispatch of various distributed energy sources (DERs) operating in the day-ahead energy market, while taking into account grid management constraints. Given the rapid increase in the number of DERs, the concept of a Virtual Power Plant (VPP) is introduced to optimize the control of these distributed energy sources, benefit both owners and DER allocation, and ensure that various DERs function as a coordinating group in the energy market.

[0003] Typically, VPP models focus on financial or commercial outcomes, neglecting the technical limitations of the distribution system and ignoring the impact of DERs (distributed energy resources, i.e., various types of distributed energy sources) on the physical distribution network. To illustrate these physical impacts, TVPP is proposed. This model plans the optimal output levels of generators and DERs to meet the technical considerations of the distribution network, increases the feasibility of planning flexible generators and controllable loads, and can improve the efficiency, reliability, and penetration of DERs in a given distribution system.

[0004] Meanwhile, due to the predictable charging requirements and mobility of EVs, EV storage is convenient, and the flow of electricity during intelligent charging and discharging can be regulated. This regulation can increase during periods of high renewable energy generation or decrease during periods of high system load, improving network resilience. Another device providing system flexibility within low-voltage networks is the HVAC (heating, ventilation, and air conditioning) system. These devices can operate in a controlled manner, reducing peak loads or operating in environments with high heating / cooling demands. When these devices are aggregated, they provide a significant source of flexibility for low-voltage networks.

[0005] Therefore, how to effectively regulate a technology-based virtual power plant while considering multiple distributed energy sources such as electric vehicles, HVAC systems, solar energy, and wind energy has become an urgent technical problem to be solved. Summary of the Invention

[0006] The purpose of this invention is to address the shortcomings of existing technology in the regulation of technical virtual power plants that do not consider multiple distributed energy sources, and to provide a technical virtual power plant regulation method that considers multiple distributed energy sources to solve the above problems.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] A technical virtual power plant control method considering multiple distributed energy sources includes the following steps:

[0009] 11) Typical scenarios for generating stochastic optimization models based on uncertainties;

[0010] 12) Construction of the objective function: The objective function is constructed to maximize the profit of TVPP, which includes the electricity revenue of TVPP and the operating cost of TVPP;

[0011] 13) Construction of constraints: Constructing electric vehicle charging and discharging constraints, HVAC system constraints, and circuit constraints;

[0012] 14) Calculation of maximum profit of TVPP and output of various types of DER: Construct a technical virtual power plant optimization model based on the objective function and constraints. In typical scenarios, use CPLEX to solve the technical virtual power plant optimization model to obtain the maximum profit of TVPP and the output of various types of DER.

[0013] 15) Regulation of technology-based virtual power plants: Based on the maximum profit of TVPP and the output of various types of DERs, the revenue provided by each type of DER to TVPP is accurately quantified using the VCG mechanism. Technology-based virtual power plants are rewarded with the revenue provided by DER owners to incentivize users to participate in TVPP, expand the power supply regulation scope of TVPP, and enable electric vehicles, HVAC systems, solar and wind power to be converted into electricity under the regulation of TVPP.

[0014] The objective function is constructed as follows:

[0015] The objective function is constructed to maximize the profit of TVPP, and its expression is as follows:

[0016] F = Max∑(TVPPR - TVPPC),

[0017] In the formula, F represents the profit of TVPP, Max represents maximization, Σ represents the summation operator, TVPPR represents the electricity revenue of TVPP, and TVPPC represents the operating cost of TVPP.

[0018] TVPPR charges include each user's daily load, EV charging, HVAC system power consumption, day-ahead electricity market revenue, and revenue from selling EV charging power, as shown in the following formula:

[0019]

[0020] In the formula, Ω s Let Ω represent a scene set, s represent different scenes within the scene set, and Ω represent the scene set. h Let Ω represent a time set, h represent different times within the time set, and Ω represent the time intervals within the time set. k Let Ω represent the load set, k represent the different loads in the load set. ev Ω represents a set of electric vehicles, ev represents electric vehicles participating in operation, and Ω represents the electric vehicle set. HVAC Ω represents an HVAC system set, where HVAC indicates that the HVAC system is involved in operation. g Ω represents a set of generator sets, g represents different generator sets, and Ω represents a set of generator sets. c Let c represent the electricity market set, and 'c' represent electricity market participants buying and selling. This indicates the current electricity market price. λ represents the electricity purchased from the power grid. ev This indicates the cost of discharging an electric vehicle. ρ represents the active power of an electric vehicle's discharge. s P represents the probability of the scenario occurring. k,s,h This indicates the load flow under different loads in different scenarios. Indicates time-of-use electricity pricing. This indicates the active power used in charging an electric vehicle. This indicates the flow of active power in an HVAC system. This indicates the user's daily workload. This indicates the energy consumed during EV charging. Indicates the power consumption of the HVAC system. This indicates the current day's electricity market revenue. This represents revenue from the sale of electric vehicle discharge rates;

[0021] TVPPC charges include the generation and maintenance costs of distributed generation, as well as the start-up costs of EVs and HVAC systems.

[0022]

[0023] In the formula, OC g This represents the unit energy production cost for the user. This represents the output power of the distributed generator, while Penalty represents the start-up cost of EVs and HVAC systems.

[0024] The construction of the constraints includes the following steps:

[0025] 31) Constructing charging and discharging constraints for electric vehicles:

[0026] 32) Construct HVAC system constraints;

[0027] 33) Construct line constraints.

[0028] In the TVPPC, the startup cost Penalty is the price paid for charging the EV or activating the HVAC system. If TVPP is not activated, this cost does not occur. The surplus is interpreted as a reward paid to customers participating in TVPP energy dispatch, expressed as follows:

[0029]

[0030]

[0031] In the formula, Ω s Let Ω represent a scene set, s represent different scenes within the scene set, and Ω represent the scene set. h Let Ω represent a time set, h represent different times within the time set, and Ω represent the time intervals within the time set. k Let Ω represent the load set, k represent the different loads in the load set. ev Ω represents a set of electric vehicles, ev represents electric vehicles participating in operation, and Ω represents the electric vehicle set. HVAC Ω represents an HVAC system set, where HVAC indicates that the HVAC system is involved in operation. w Let ρ represent the normal operating set, w represent the normal operating condition, and ρ represent the normal operating condition. s Penalty represents the probability of a scene occurring. ev Penalty represents the startup cost of an electric vehicle. HVAC This indicates the startup cost of an HVAC system. Indicates time-of-use electricity price, λ ev This indicates the cost of discharging an electric vehicle. This indicates the active power used in charging an electric vehicle. This represents the active power of an electric vehicle charging under normal operating conditions. This represents the active power of an electric vehicle's discharge. This represents the active power of an electric vehicle's discharge under normal operating conditions. This indicates the flow of active power in an HVAC system. This indicates the active power flow rate of an HVAC system under normal operating conditions.

[0032] The process of constructing electric vehicle charging and discharging constraints includes the following steps:

[0033] 51) Set charging and discharging power limits for electric vehicles:

[0034]

[0035]

[0036] In the formula, n represents the node number. This indicates the maximum active power for charging an electric vehicle. Represents the decision variables for electric vehicle charging. The decision variables representing the discharge of electric vehicles;

[0037] 52) Set charging and discharging state constraints for electric vehicles:

[0038]

[0039] when At that time, the electric vehicle is in a discharging state; when At that time, the electric vehicle is in a charging state; when At that time, electric vehicles do not participate in the energy flow of the system;

[0040] 53) Set the state of charge constraints for electric vehicles:

[0041] The state of charge (SOC) of an electric vehicle depends on the SOC from a previous period plus any additional charging and minus any additional discharging, as expressed below:

[0042]

[0043] In the formula, E ev,k,n,s,h Indicates the SOC (State of Charge) status of an electric vehicle. and This indicates the charging and discharging efficiency of an electric vehicle.

[0044]

[0045] In the formula, and Indicates the upper and lower limits of the electric vehicle's SOC state;

[0046]

[0047]

[0048] In the formula, E ev,k,n,s,h0 and E ev,k,n,s,h24 This represents the SOC value of EV at the beginning and end of the operating cycle, μ. ev Indicates the scaling factor. This indicates the maximum energy storage limit for electric vehicles.

[0049] The process of constructing HVAC system constraints includes the following steps:

[0050] 61) Set upper and lower temperature limits for the HVAC system:

[0051]

[0052] In the formula, This indicates the ideal indoor temperature setting. Indicates the upper and lower temperature limits of the HVAC system;

[0053] 62) Set power limiting constraints for the HVAC system:

[0054]

[0055] In the formula, This indicates the maximum active power flow rate in the HVAC system.

[0056] 63) Set the comfort model for the HVAC system:

[0057]

[0058] In the formula, Indicates indoor temperature, M k Indicates indoor air quality, c air R represents the specific heat capacity of indoor air. k ΔT represents the thermal resistance, and ΔT represents the time granularity. Indicates ambient temperature, COP HVAC Indicates the HVAC performance factor of a house. This means for any value;

[0059]

[0060] Indicates the initial indoor temperature. This represents the indoor temperature at t=0;

[0061]

[0062] In the formula, μ k,t,n,s,h This represents the switching variable of the HVAC system, i.e., when μ k,t,n,s,h When μ = 1, the HVAC system unit is operational. k,t,n,s,h When the value is 0, the HVAC system unit shuts down;

[0063]

[0064] In the formula, argmin represents the value of the variable when the expression in parentheses reaches its minimum value. Indicates the increase in indoor temperature. Indicates the decrease in indoor temperature, N k Indicates the number of houses. Indicates the setting point of the indoor thermostat;

[0065] 64) Set comfort constraints for the HVAC system:

[0066]

[0067]

[0068] This constraint minimizes user discomfort and ensures that the indoor thermostat is set close to the ideal indoor temperature.

[0069] 65) Setting upper and lower operating limits for the HVAC system: The upper and lower operating limits of the HVAC system are controlled by the dead zone near the temperature setpoint of the HVAC unit, and their expressions are as follows:

[0070]

[0071]

[0072] In the formula, Indicates the dead zone of the HVAC unit's temperature setpoint;

[0073] 66) Set temperature variation constraints for the HVAC system:

[0074]

[0075]

[0076] This constraint ensures that indoor temperature rise and fall are always positive.

[0077] The construction of circuit constraints includes the following steps:

[0078] 71) Set power balance constraints:

[0079]

[0080]

[0081] In the formula, Ω l Let P represent a set of lines, l represent different lines in the set, and P represent different lines in the set. l,s,h Indicates the active power flow of the line. PL represents the active power requirement of a node. l,s,h This indicates the active power loss of each branch. This represents the reactive power of a distributed generator. Q represents the reactive power flow in the electricity market. l,s,h Indicates the reactive power flow of the line. Indicates the reactive power demand of the node. QL represents the reactive power flow rate in an HVAC system. l,s,h This indicates the reactive power loss of each branch;

[0082] Feeder power flow constraints:

[0083]

[0084]

[0085] In the formula, ΔV n,s,h and ΔV m,s,h b represents the line voltage deviation magnitude for different line indices. l and g l θ represents the conductance and susceptance of the circuit. l,s,h V represents the phase angle deviation. nom Indicates the nominal voltage, MP l and MQ l Indicates the active and reactive power parameters related to the branch lines;

[0086] 72) Set line capacity constraints:

[0087]

[0088]

[0089]

[0090] In the formula, χ l,h Indicates the switching variables of the line. R represents the maximum power flow of the line. l and X l Indicates the resistance and reactance of the circuit;

[0091] 73) Set power limitation constraints for distributed generation units:

[0092]

[0093]

[0094]

[0095] In the formula, This represents the minimum active power of a distributed generation unit. This represents the maximum active power of the distributed generation unit. This represents the minimum reactive power of a distributed generation unit. pf represents the maximum reactive power of a distributed generation unit. g This represents the power factor of a distributed generation unit.

[0096] 74) Power limitation constraints of substations:

[0097]

[0098]

[0099]

[0100] In the formula, This represents the minimum active power purchased from the power grid. This indicates the maximum active power purchased from the power grid. This represents the minimum reactive power purchased from the power grid. pf represents the maximum reactive power purchased from the power grid. ss This indicates the power factor of the substation;

[0101] 75) Set input constraints for terminal nodes: Each node with input requirements has an input stream, and each terminal node has only one input stream.

[0102]

[0103]

[0104] In the formula, in represents power inflow and out represents power outflow. This indicates that any node n does not belong to the load set, and line l belongs to the set n;

[0105] 76) In setting the power constraints for the measured line, the power constraints for two adjacent time periods on the same line are as follows:

[0106]

[0107] l represents different routes, s represents different scenarios, h represents different time periods, and S l,s,h and S l,s,h+1 This represents the line power flow rate between two adjacent time periods. This represents the maximum sum of power call rates between two consecutive time periods.

[0108] Beneficial effects

[0109] The present invention provides a technical virtual power plant control method that considers multiple distributed energy sources. Compared with the prior art, the proposed TVPP operation model improves the system integration DER capability, fully considers the impact of multiple distributed energy sources, and realizes effective control of the technical virtual power plant.

[0110] This invention quantifies the benefits of different types of DERs, utilizing the VCG mechanism in cooperative game theory to allocate these marginal benefits in a fair and efficient manner, emphasizing which DERs can provide the greatest contribution to the system. It also quantifies the impact of TVPP on the thermal comfort of building occupants under different HVAC operating strategies. This quantification allows for the assessment of the thermal comfort and technical impact of HVAC systems under different operating strategies within a DR (Resource Demand) plan; and studies the impact of TVPP on power loss, voltage distribution, and line congestion combinations, helping to reduce or eliminate overload exceeding line capacity. Attached Figure Description

[0111] Figure 1 This is a layout diagram of the test system used in this invention;

[0112] Figure 2 This is a percentage chart showing the arrival time of the electric vehicle involved in this invention.

[0113] Figure 3 This is an energy combination diagram for Example 4 of the present invention;

[0114] Figure 4 This is a voltage curve diagram of Example 1 of the present invention;

[0115] Figure 5 This is the voltage curve diagram of Example 4 of the present invention;

[0116] Figure 6 This is a diagram showing the total V2G power of the electric vehicle involved in this invention;

[0117] Figure 7 This is a diagram showing the total charging demand of the electric vehicles involved in this invention;

[0118] Figure 8 This diagram illustrates the application of different temperature bandwidths in the HVAC system involved in this invention.

[0119] Figure 9 This is a sequence diagram of the method of the present invention;

[0120] Figure 10 This is a structural block diagram of the method described in this invention. Detailed Implementation

[0121] To provide a better understanding of the structural features and effects achieved by the present invention, a detailed description is provided below, accompanied by preferred embodiments and accompanying drawings:

[0122] This invention sets up a typical scenario for a stochastic optimization model based on uncertainties. The objective function is to maximize the profit of a Technical Virtual Power Plant (TVPP), including the TVPP's revenue from operating the TVPP (TVPPR) and its operating cost (TVPPC). Furthermore, it constructs constraints on electric vehicle charging and discharging, temperature and power constraints on the heating, ventilation, and air conditioning (HVAC) system, and capacity and power flow constraints on the tested lines. Next, based on the objective function and constraints, an optimization model for the technical virtual power plant is constructed. In the set classic scenario, the model is solved using CPLEX (a linear programming solver) to obtain the maximum profit of the TVPP and the output of various types of distributed energy resources (DERs). Finally, the VCG (Vickrey–Clarke–Grove) mechanism is used to accurately quantify the revenue provided by each type of DER to the TVPP, thereby evaluating the contribution of each type of DER to the TVPP.

[0123] like Figure 9 and Figure 10 As shown, the present invention provides a technical virtual power plant control method considering multiple distributed energy sources, comprising the following steps:

[0124] The first step is to generate a typical scenario for the stochastic optimization model based on the uncertainties. The stochastic optimization model has three uncertainties: solar power generation, wind power generation, and electricity load; each uncertainty considers three scenarios. Therefore, the stochastic optimization model can produce three... 3 Different typical scenarios.

[0125] The second step is to construct the objective function: construct the objective function to maximize the profit of TVPP, which includes the electricity revenue of TVPP and the operating costs of TVPP.

[0126] The objective function is constructed to maximize the profit of TVPP, and its expression is as follows:

[0127] F = Max∑(TVPPR - TVPPC),

[0128] In the formula, F represents the profit of TVPP, Max represents maximization, Σ represents the summation operator, TVPPR represents the electricity revenue of TVPP, and TVPPC represents the operating cost of TVPP.

[0129] TVPPR charges include each user's daily load, EV charging, HVAC system power consumption, day-ahead electricity market revenue, and revenue from selling EV charging power, as shown in the following formula:

[0130]

[0131] In the formula, Ω s Let Ω represent a scene set, s represent different scenes within the scene set, and Ω represent the scene set. h Let Ω represent a time set, h represent different times within the time set, and Ω represent the time intervals within the time set. k Let Ω represent the load set, k represent the different loads in the load set. ev Ω represents a set of electric vehicles, ev represents electric vehicles participating in operation, and Ω represents the electric vehicle set. HVAC Ω represents an HVAC system set, where HVAC indicates that the HVAC system is involved in operation. g Ω represents a set of generator sets, g represents different generator sets, and Ω represents a set of generator sets. c Let c represent the electricity market set, and 'c' represent electricity market participants buying and selling. This indicates the current electricity market price. λ represents the electricity purchased from the power grid. ev This indicates the cost of discharging an electric vehicle. ρ represents the active power of an electric vehicle's discharge. s P represents the probability of the scenario occurring. k,s,h This indicates the load flow under different loads in different scenarios. Indicates time-of-use electricity pricing. This indicates the active power used in charging an electric vehicle. This indicates the flow of active power in an HVAC system. This indicates the user's daily workload. This indicates the energy consumed during EV charging. Indicates the power consumption of the HVAC system. This indicates the current day's electricity market revenue. This represents revenue from the sale of electric vehicle discharge rates;

[0132] TVPPC charges include the generation and maintenance costs of distributed generation, as well as the start-up costs of EVs and HVAC systems.

[0133]

[0134] In the formula, OC g This represents the unit energy production cost for the user. This represents the output power of the distributed generator, while Penalty represents the start-up cost of EVs and HVAC systems.

[0135] Meanwhile, in TVPPC, the start-up cost Penalty is the price paid for charging EVs or activating HVAC systems. If TVPP is not activated, this cost does not occur. This surplus is interpreted as a reward paid to customers participating in TVPP energy dispatch, expressed as follows:

[0136]

[0137]

[0138] In the formula, Ω s Let Ω represent a scene set, s represent different scenes within the scene set, and Ω represent the scene set. h Let Ω represent a time set, h represent different times within the time set, and Ω represent the time intervals within the time set. k Let Ω represent the load set, k represent the different loads in the load set. ev Ω represents a set of electric vehicles, ev represents electric vehicles participating in operation, and Ω represents the electric vehicle set. HVAC Ω represents an HVAC system set, where HVAC indicates that the HVAC system is involved in operation. w Let ρ represent the normal operating set, w represent the normal operating condition, and ρ represent the normal operating condition. s Penalty represents the probability of a scene occurring. ev Penalty represents the startup cost of an electric vehicle. HVAC This indicates the startup cost of an HVAC system. Indicates time-of-use electricity price, λ ev This indicates the cost of discharging an electric vehicle. This indicates the active power used in charging an electric vehicle. This represents the active power of an electric vehicle charging under normal operating conditions. This represents the active power of an electric vehicle's discharge. This represents the active power of an electric vehicle's discharge under normal operating conditions. This indicates the flow of active power in an HVAC system. This indicates the active power flow rate of an HVAC system under normal operating conditions.

[0139] The third step is to construct the constraints: construct electric vehicle charging and discharging constraints, HVAC system constraints, and circuit constraints.

[0140] Electric vehicles (EVs) have predictable charging requirements and mobility; the amount of electricity extracted during charging can be adjusted to increase during periods of high renewable energy generation or decrease during periods of high system load. HVAC (Heating, Ventilation, and Air Conditioning) systems can operate in a controlled manner to reduce peak loads or operate under high heating / cooling demands, improving user comfort. Line constraints take into account the effects of system congestion and voltage distribution, reducing line losses and node voltage shifts, and improving system operational stability.

[0141] (1) Constructing electric vehicle charging and discharging constraints.

[0142] A1) Set limits on the charging and discharging power of electric vehicles:

[0143]

[0144]

[0145] In the formula, n represents the node number. This indicates the maximum active power for charging an electric vehicle. Represents the decision variables for electric vehicle charging. The decision variables representing the discharge of electric vehicles;

[0146] A2) Set constraints for the charging and discharging states of electric vehicles:

[0147]

[0148] when At that time, the electric vehicle is in a discharging state; when At that time, the electric vehicle is in a charging state; when At that time, electric vehicles do not participate in the energy flow of the system;

[0149] A3) Set the state of charge constraints for electric vehicles:

[0150] The state of charge (SOC) of an electric vehicle depends on the SOC from a previous period plus any additional charging and minus any additional discharging, as expressed below:

[0151]

[0152] In the formula, E ev,k,n,s,h Indicates the SOC (State of Charge) status of an electric vehicle. and This indicates the charging and discharging efficiency of an electric vehicle.

[0153]

[0154] In the formula, and Indicates the upper and lower limits of the electric vehicle's SOC state;

[0155]

[0156]

[0157] In the formula, E ev,k,n,s,h0 and E ev,k,n,s,h24 This represents the SOC value of EV at the beginning and end of the operating cycle, μ.ev Indicates the scaling factor. This indicates the maximum energy storage limit for electric vehicles.

[0158] (2) Construct HVAC system constraints.

[0159] B1) Set upper and lower temperature limits for the HVAC system:

[0160]

[0161] In the formula, This indicates the ideal indoor temperature setting. Indicates the upper and lower temperature limits of the HVAC system;

[0162] B2) Set power limiting constraints for the HVAC system:

[0163]

[0164] In the formula, This indicates the maximum active power flow rate in the HVAC system.

[0165] B3) Setting the HVAC system comfort model:

[0166]

[0167] In the formula, Indicates indoor temperature, M k Indicates indoor air quality, c air R represents the specific heat capacity of indoor air. k The value represents the thermal resistance, and ΔT represents the time granularity. Indicates ambient temperature, COP HVAC Indicates the HVAC performance factor of a house. This means for any value;

[0168]

[0169] Indicates the initial indoor temperature. This represents the indoor temperature at t=0;

[0170]

[0171] In the formula, μ k,t,n,s,h This represents the switching variable of the HVAC system, i.e., when μ k,t,n,s,h When μ = 1, the HVAC system unit is operational. k,t,n,s,h When the value is 0, the HVAC system unit shuts down;

[0172]

[0173] In the formula, argmin represents the value of the variable when the expression in parentheses reaches its minimum value. Indicates the increase in indoor temperature. Indicates the decrease in indoor temperature, N k Indicates the number of houses. Indicates the setting point of the indoor thermostat;

[0174] B4) Set comfort constraints for the HVAC system:

[0175]

[0176]

[0177] This constraint minimizes user discomfort and ensures that the indoor thermostat is set close to the ideal indoor temperature.

[0178] B5) Setting the upper and lower operating limits of the HVAC system: The upper and lower operating limits of the HVAC system are controlled by the dead zone near the temperature setpoint of the HVAC unit, and their expressions are as follows:

[0179]

[0180]

[0181] In the formula, Indicates the dead zone of the HVAC unit's temperature setpoint;

[0182] B6) Set temperature variation constraints for the HVAC system:

[0183]

[0184]

[0185] This constraint ensures that indoor temperature rise and fall are always positive.

[0186] (3) Constructing line constraints.

[0187] C1) Set power balance constraints:

[0188]

[0189]

[0190] In the formula, Ω l Let P represent a set of lines, l represent different lines in the set, and P represent different lines in the set. l,s,h Indicates the active power flow of the line. PL represents the active power requirement of a node. l,s,h This indicates the active power loss of each branch. This represents the reactive power of a distributed generator. Q represents the reactive power flow in the electricity market. l,s,h Indicates the reactive power flow of the line. Indicates the reactive power demand of the node. QL represents the reactive power flow rate in an HVAC system. l,s,h This indicates the reactive power loss of each branch;

[0191] Feeder power flow constraints:

[0192]

[0193]

[0194] In the formula, ΔV n,s,h and ΔV m,s,h b represents the line voltage deviation magnitude for different line indices. l and g l θ represents the conductance and susceptance of the circuit. l,s,h V represents the phase angle deviation. nom Indicates the nominal voltage, MP l and MQ l Indicates the active and reactive power parameters related to the branch lines;

[0195] C2) Set line capacity constraints:

[0196]

[0197]

[0198]

[0199] In the formula, χ l,h Indicates the switching variables of the line. R represents the maximum power flow of the line. l and X l Indicates the resistance and reactance of the circuit;

[0200] C3) Set power limitation constraints for distributed generation units:

[0201]

[0202]

[0203]

[0204] In the formula, This represents the minimum active power of a distributed generation unit. This represents the maximum active power of the distributed generation unit. This represents the minimum reactive power of a distributed generation unit. pf represents the maximum reactive power of a distributed generation unit. g This represents the power factor of a distributed generation unit.

[0205] C4) Power limitation constraints for substations:

[0206]

[0207]

[0208]

[0209] In the formula, This represents the minimum active power purchased from the power grid. This indicates the maximum active power purchased from the power grid. This represents the minimum reactive power purchased from the power grid. pf represents the maximum reactive power purchased from the power grid. ss This indicates the power factor of the substation;

[0210] C5) Set constraints on the number of inputs to terminal nodes: each node with demand has an input stream, and each terminal node has only one input stream.

[0211]

[0212]

[0213] In the formula, in represents power inflow and out represents power outflow. This indicates that any node n does not belong to the load set, and line l belongs to the set n;

[0214] C6) In setting the power constraints for the measured line, the power constraints for two adjacent time periods on the same line are as follows:

[0215]

[0216] l represents different routes, s represents different scenarios, h represents different time periods, and S l,s,h and S l,s,h+1 This represents the line power flow rate between two adjacent time periods. This represents the maximum sum of power call rates between two consecutive time periods.

[0217] The fourth step is to calculate the maximum profit of TVPP and the output of various types of DERs. Based on the objective function and constraints, a technical virtual power plant optimization model is constructed. In typical scenarios, the traditional CPLEX algorithm is used to solve the technical virtual power plant optimization model to obtain the maximum profit of TVPP and the output of various types of DERs.

[0218] The fifth step is the regulation of technology-based virtual power plants. Based on the maximum profit of TVPP and the output of various types of DERs, the revenue provided by each type of DER to TVPP is accurately quantified using the VCG mechanism. Technology-based virtual power plants are rewarded with the revenue provided by DER owners, incentivizing users to participate in TVPP. This gives TVPP a wider range of control over its power supply. Under the regulation of TVPP, traditional technologies are used to convert electricity from electric vehicles, HVAC systems, solar energy, and wind energy.

[0219] For example, electric vehicles provide a significant amount of V2G power during the evening peak hours between 7:00 PM and 9:00 PM, while electric vehicle charging typically occurs during off-peak hours (9:00 PM - 7:00 AM); HVAC systems use a large amount of power at 6:00 PM to pre-cool buildings, which reduces the demand for HVAC during peak hours (8:00 PM and 9:00 PM), thus reducing system load; solar power provides power during periods of ample sunshine (10:00 AM - 4:00 PM), and various types of DERs can complement each other, with the various DERs operating within TVPP providing optimal results for TVPP operators.

[0220] The embodiments of the present invention are as follows:

[0221] This invention uses a 119 bus test system for numerical analysis. The nominal voltage of the system is 11kV, and the required power is 22,709.72kW and 17,041.068kVA. Figure 1 The document also shows the types of DG units and their connected buses, as well as the interconnection points between the electric vehicle parking lot and the HVAC system and the network. Two types of distributed generator sets are considered: wind power and solar power. Basic parameters for electric vehicles and generators are shown in Table 1. Each customer has both HVAC and EV units, and each electric vehicle parking lot has 25 vehicles. Considering demand response (DR), the electric vehicles will be charged during the minimum electricity purchase price period. The arrival and departure times of the electric vehicles are as follows: Figure 2 As shown.

[0222] Table 1 Comparison of Basic Parameters of Electric Vehicle Generators

[0223] Wind turbine operating costs €13.2 / MWh Operating costs of solar generators €18.24 / MWh Electric vehicle charging and discharging operating costs 5 euros / MWh Electric vehicle charging and discharging efficiency 90％ Minimum discharge SOC limit for electric vehicles 40％ Initial SOC value of electric vehicle 50% or 62.5% Node voltage deviation ±5％ Distributed generator power factor 0.95 substation power factor 0.8

[0224] To investigate the contributions of distributed energy resources, electric vehicles, and HVAC systems to TVPP operations, this invention sets out four scenarios. The first scenario uses an external power grid to meet the system's demand. This serves as a baseline case to examine the various impacts of subsequent case studies. The second scenario allows for the aggregation of distributed renewable energy systems (solar photovoltaic, wind power) to meet some of the demand beyond the external power grid. Furthermore, the impact of time-of-use pricing (DR) was investigated using time-of-use pricing (as shown in Table 2).

[0225] Table 2 Comparison of Time-of-Use Electricity Prices

[0226] time Electricity price (Euro / MWh) 10:00-21:00 61.27 21:00-10:00 40.36

[0227] The third case study examines the potential of electric vehicles and HVAC systems to enhance system flexibility. This case study explores the possibility of electric vehicles as mobile energy storage systems to help support grid operation in the event of a sudden drop in renewable energy generator output. This case study considers electricity purchased from the market and the DR flexibility of using electric vehicles, HVAC systems, and not using renewable energy generators.

[0228] The fourth case study incorporates renewable energy generation into electric vehicles and HVAC systems to examine the combined impact of these technologies on TVPP technology and economic performance. This case study considers electricity purchased from the market, power aggregation from distributed generation systems, and power aggregation from various sources, such as electric vehicles via vehicle-to-grid (V2G) interface technology, the use of HVAC in commercial buildings, and the flexibility of time-of-use pricing (DR). In this final case, variations in temperature range within commercial buildings are assessed.

[0229] Table 3 shows the revenue from electricity sold to consumers, the operating costs of TVPPs, and the final profit of TVPPs in all case studies. As can be seen from the table, TVPP profits increase as more DERs are added to the system, such as solar PV, wind power, electric vehicles, and HVAC units. This increase is primarily due to relatively stable TVPP revenue and reduced operating costs. This cost reduction is due to several factors, such as increased local generation, which is cheaper than the external grid, and the intelligent dispatching of EVs and HVAC units to minimize usage during periods of high time-of-use pricing.

[0230] Table 3: Comparison of Electricity Revenue Sold to Consumers, TVPP Operating Costs, and Final Profit

[0231] Income (Euros) Cost (Euros) Profit (in Euros) Case 1 44091.62 28849.79 15241.83 Case 2 44517.26 14992.79 29524.47 Case 3 44143.67 16989.28 27154.38 Case 4 44783.89 12757.65 32026.23

[0232] Table 3 shows that, compared to Case 1, operating costs were reduced by 48% when the system included a DER (Case 2). When EV and HVAC were added to the DER (Case 4), costs were reduced by 56% relative to the basic case. In terms of profit, profits in Case 2 increased by 94% compared to Case 1. When all DERs were included (Case 4), profits increased to €32,026.23. The energy mix in Case 4 is as follows: Figure 3 As shown in the figure, the contributions of DER (wind, solar, and V2G) are illustrated, along with the charging demand for electric vehicles. It can be seen that electric vehicles charge significantly earlier in the morning, with a slight increase in charging as solar photovoltaic generators generate electricity.

[0233] Electric vehicle charging decreases during peak evening hours. Importantly, the implementation of time-of-use pricing ensures reduced load during peak periods. Without this DR scheme, we might see increased peak nighttime load due to electric vehicle charging. This would impose additional costs on consumers and negatively impact the network.

[0234] The marginal contribution of each DER asset type to the overall TVPP profit was investigated using the VCG mechanism. By quantifying the contribution of each DER type, appropriate compensation can be paid to DER owners, thereby incentivizing their participation in TVPP. The marginal contribution of DER assets to TVPP profit is shown in Table 4. This table shows that electric vehicles have the greatest impact on TVPP operation. This is due to their ability to absorb and supply power to the TVPP at different times. The ability of electric vehicles to provide appropriate power compensation is limited by battery size. HVAC systems do not offer significant technological advantages; however, they provide an important source of thermal comfort control for commercial buildings and are therefore very important to TVPP. HVAC units can also provide important peak-reduction services.

[0235] Table 4. Comparison of the Marginal Contribution of DER Assets to TVPP Profits

[0236] Types of Distributed Energy Contribution percentage (%) Solar energy 36 Wind energy 18 electric vehicles 39 HVAC unit 7

[0237] In this optimization model, technical constraints of the power distribution system are considered. These include the capacity of the lines within the system. Table 5 shows the cases of exceeding line load capacity in Case 1 and how subsequent cases help reduce or eliminate such cases. In Case 1, there were 19 cases where the load exceeded the rated line capacity. In Case 2, there were three instances. In Case 3, there were 15 instances, and in Case 4, there was no instance of exceeding the rated line capacity. In Case 3, only electric vehicles and the HVAC system were operating. The ability of electric vehicles to provide power compensation is limited by their size. This is why the exceedance of line capacity was relatively high.

[0238] Table 5 Comparison of Line Load Conditions in Four Case Studies

[0239]

[0240] Another important technical constraint to consider in system management is the voltage distribution among nodes within the network. The voltage distribution of the bus is a crucial indicator of system reliability. In Case 1, the voltage distribution of all nodes over 24 hours is as follows: Figure 4 As shown.

[0241] and Figure 4 Conversely, the node voltage distribution results in Case 4 are as follows: Figure 5 As shown. In this case study, the node voltage distribution was significantly improved. This improvement in voltage distribution is due to the presence of DG cells generating electricity locally, which increases the voltage at the local nodes.

[0242] DG units are also often located near the end of the line, which improves voltage distribution. The maximum voltage drop occurs at the node at the end of the feeder, which can be seen in the grid topology diagram.

[0243] In Case 4, the average voltage deviation of each node over 24 hours is as follows: Figure 6 As shown in the figure, this graph illustrates the voltage distribution deviation from the nominal 11kV voltage (pu), with no voltage distribution exceeding the 0.05pu threshold.

[0244] Case studies 3 and 4 include electric vehicles and HVAC systems in commercial and service buildings. Electric vehicles have limited power compensation capabilities; therefore, they are less affected by voltage deviations and can be charged and discharged according to system requirements and TOU pricing. This is as follows: Figure 6 As shown, it displays the active power discharge of the electric vehicle aggregator. This graph only shows active power discharge and does not show the charging of the electric vehicles, which typically occurs between 21:00 and 07:00.

[0245] The impact of time-of-use pricing is evident, as electric vehicles provide a significant amount of V2G power during the evening peak hours between 7:00 PM and 9:00 PM. Figure 6 This demonstrates the capacity of electric vehicles in parking lots equipped with V2G services to meet system load balancing requirements, and their potential in disaster recovery plans. The total charging demand for various electric vehicle aggregators is shown as follows: Figure 7 As shown in the figure, charging demand is low during peak hours between 10:00 and 20:00.

[0246] In addition to the impact of electric vehicles as seen in Case Studies 3 and 4, these cases also include HVAC systems. These HVAC units are optimized to respond to DR (Diverterless Delivery) schemes under time-of-use pricing, thereby reducing power supply during periods of high time-of-use pricing while maintaining thermal comfort for users within commercial buildings. There is a trade-off between user thermal comfort and the flexibility that the HVAC system can provide. More stringent thermal comfort requirements mean that the HVAC system needs to operate for longer periods and may operate during high-demand periods with high time-of-use pricing.

[0247] These three thermal comfort zones cover a wide range: wide (18°–24°), standard (19°–23°), and narrow (20°–22°). For example... Figure 8 As shown, this compares the operation of HVAC within the model with three different thermal comfort requirements. Within a narrower thermal operating range, the operating costs of HVAC units increase due to stricter operating restrictions on HVAC systems and time-of-use pricing, resulting in no HVAC operation during peak hours (7:00 PM and 9:00 PM). Within a narrower thermal comfort range, HVAC demand increases by 13.11% compared to standard thermal conditions in Southern Europe, with no significant technical impact on the distribution network despite the increased electricity demand. This indicates a trade-off between thermal comfort and operating costs, but no significant trade-off between thermal comfort and grid technology. Due to the DR scheme, the HVAC system uses a large amount of electricity at 6:00 PM to pre-cool the building, reducing the demand for HVAC use during peak hours (8:00 PM and 9:00 PM). Figure 8 This demonstrates the ability of HVAC systems to contribute to DR programs while maintaining thermal comfort.

[0248] TVPP optimizes the operating strategies of numerous distributed energy resources to maximize profits when bids enter the day-ahead energy market. This optimization increases system flexibility, consumer participation, and renewable energy generation while maintaining consumer thermal comfort requirements. Line losses are reduced by nearly 70%, TVPP lowers operating costs, and increases revenue from electricity sales. Analysis shows that electric vehicles contribute the most to the marginal profit of TVPP, followed by solar photovoltaic systems, and then wind turbines. Therefore, various types of DERs can complement each other. Overall, TVPP can bring many benefits to the distribution network, including improved financial performance, improved technical operations, increased energy efficiency, and improved environmental impact.

[0249] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A technical virtual power plant control method considering multiple distributed energy sources, characterized in that, Includes the following steps: 11) Typical scenarios for generating stochastic optimization models based on uncertainties; 12) Construction of the objective function: The objective function is constructed to maximize the profit of TVPP, which includes the electricity revenue of TVPP and the operating cost of TVPP; The objective function is constructed as follows: The objective function is constructed to maximize the profit of TVPP, and its expression is as follows: ， In the formula, F represents the profit of TVPP. To maximize, The summation symbol is used; TVPPR represents the electricity revenue of TVPP; and TVPPC represents the operating costs of TVPP. TVPPR charges include each user's daily load, EV charging, HVAC system power consumption, day-ahead electricity market revenue, and revenue from selling EV charging power, as shown in the following formula: ， In the formula, Represents a scene set, Represents different scenes within a scene set. Represents a time set, Represents different times within a time set. Represents the load set, This represents different loads within a load cluster. Indicates electric vehicle set, This indicates that electric vehicles are involved in the operation. Represents an HVAC system assembly. This indicates that the HVAC system is in operation. Indicates the electricity market collection, This indicates participation in the electricity market. This indicates the current electricity market price. This indicates the electricity purchased from the power grid. This indicates the cost of discharging an electric vehicle. This represents the active power of an electric vehicle's discharge. This indicates the probability of a scenario occurring. This indicates the load flow under different loads in different scenarios. Indicates time-of-use electricity pricing. This indicates the active power used in charging an electric vehicle. This indicates the flow of active power in an HVAC system. This indicates the user's daily workload. This indicates the energy consumed during EV charging. Indicates the power consumption of the HVAC system. This indicates the current day's electricity market revenue. This represents revenue from the sale of electric vehicle discharge rates; TVPPC charges include the generation and maintenance costs of distributed generation, as well as the start-up costs of EVs and HVAC systems. ， In the formula, This represents the unit energy production cost for the user. This represents the output power of the distributed generator. This indicates the startup cost of EVs and HVAC systems. Represents a generator set set. This indicates different generator sets; 13) Construction of constraints: Constructing electric vehicle charging and discharging constraints, HVAC system constraints, and line constraints; 14) Calculation of maximum profit of TVPP and output of various types of DER: Construct a technical virtual power plant optimization model based on the objective function and constraints. In typical scenarios, use CPLEX to solve the technical virtual power plant optimization model to obtain the maximum profit of TVPP and output of various types of DER. 15) Regulation of technology-based virtual power plants: Based on the maximum profit of TVPP and the output of various types of DERs, the revenue provided by each type of DER to TVPP is accurately quantified using the VCG mechanism. Technology-based virtual power plants use the revenue provided by DER owners to reward and incentivize users to participate in TVPP, thereby expanding the scope of TVPP's power supply regulation. Under the regulation of TVPP, electric vehicles, HVAC systems, solar and wind power are converted into electricity.

2. The technical virtual power plant control method considering multiple distributed energy sources according to claim 1, characterized in that, The construction of the constraints includes the following steps: 21) Constructing charging and discharging constraints for electric vehicles: 22) Construct HVAC system constraints; 23) Construct line constraints.

3. The technical virtual power plant control method considering multiple distributed energy sources according to claim 1, characterized in that: In the TVPPC, the startup cost Penalty is the price paid for charging the EV or activating the HVAC system. If the TVPP is not activated, it does not occur, and its expression is as follows: ， ， In the formula, Represents a scene set, Represents different scenes within a scene set. Represents a time set, Represents different times within a time set. Represents the load set, This represents different loads within a load cluster. Indicates electric vehicle set, This indicates that electric vehicles are involved in the operation. Represents an HVAC system assembly. This indicates that the HVAC system is in operation. Indicates the normal operating set, This indicates normal operating status. This indicates the probability of a scenario occurring. This represents the startup cost of an electric vehicle. This indicates the startup cost of an HVAC system. Indicates time-of-use electricity pricing. This indicates the cost of discharging an electric vehicle. This indicates the active power used in charging an electric vehicle. This represents the active power of an electric vehicle charging under normal operating conditions. This represents the active power of an electric vehicle's discharge. This represents the active power of an electric vehicle's discharge under normal operating conditions. This indicates the flow of active power in an HVAC system. This indicates the active power flow rate of an HVAC system under normal operating conditions.

4. The technical virtual power plant control method considering multiple distributed energy sources according to claim 2, characterized in that, The process of constructing electric vehicle charging and discharging constraints includes the following steps: 41) Set charging and discharging power limits for electric vehicles: ， ， In the formula, n represents the node number. This indicates the maximum active power for charging an electric vehicle. This indicates the maximum active power discharged by the electric vehicle. Represents the decision variables for electric vehicle charging. The decision variables representing the discharge of electric vehicles; 42) Set charging and discharging state constraints for electric vehicles: ， when , At that time, the electric vehicle was in a discharging state; when , At that time, the electric vehicle was charging; when , At that time, electric vehicles do not participate in the energy flow of the system; 43) Set the state of charge constraints for electric vehicles: The state of charge (SOC) of an electric vehicle depends on the SOC from a previous period plus any additional charging and minus any additional discharging, as expressed below: ， In the formula, Indicates the SOC (State of Charge) status of an electric vehicle. and This indicates the charging and discharging efficiency of an electric vehicle. ， In the formula, and Indicates the upper and lower limits of the electric vehicle's SOC state; ， ， In the formula, and This represents the SOC value of the EV at the beginning and end of its operating cycle. Indicates the scaling factor. This indicates the maximum energy storage limit for electric vehicles.

5. The technical virtual power plant control method considering multiple distributed energy sources according to claim 2, characterized in that, The process of constructing HVAC system constraints includes the following steps: 51) Set upper and lower temperature limits for the HVAC system: ， In the formula, This indicates the ideal indoor temperature setting. , Indicates the upper and lower temperature limits of the HVAC system; 52) Set power limiting constraints for the HVAC system: ， In the formula, This indicates the maximum active power flow rate in the HVAC system. 53) Setting the comfort model for the HVAC system: ， In the formula, Indicates the indoor temperature. Indicates indoor air quality. Indicates the specific heat capacity of indoor air. Represents thermal resistance. Indicates time granularity. Indicates ambient temperature. Indicates the HVAC performance factor of a house. This means for any value; ， Indicates the initial indoor temperature. This represents the indoor temperature at t=0; ， In the formula, This represents the switching variable of the HVAC system, i.e., when... At that time, the HVAC system unit is working. At that time, the HVAC system unit shuts down; ， In the formula, This indicates the value of the variable when the expression in parentheses reaches its minimum value. Indicates the increase in indoor temperature. This indicates the decrease in indoor temperature. Indicates the number of houses. Indicates the setting point of the indoor thermostat; 54) Set comfort constraints for the HVAC system: ， ， This constraint minimizes user discomfort and ensures that the indoor thermostat is set close to the ideal indoor temperature. 55) Setting upper and lower operating limits for the HVAC system: The upper and lower operating limits of the HVAC system are controlled by the dead zone near the temperature setpoint of the HVAC unit, and their expressions are as follows: ， ， In the formula, Indicates the dead zone of the HVAC unit's temperature setpoint; 56) Set temperature variation constraints for the HVAC system: ， ， This constraint ensures that indoor temperature rise and fall are always positive.

6. The technical virtual power plant control method considering multiple distributed energy sources according to claim 2, characterized in that, The construction of circuit constraints includes the following steps: 61) Set power balance constraints: ， ， In the formula, Represents a set of lines. This indicates different lines within a line group. Indicates the active power flow of the line. Indicates the node's active power demand. This indicates the active power loss of each branch. This represents the reactive power of a distributed generator. Represents reactive power flow in the electricity market. Indicates the reactive power flow of the line. Indicates the reactive power demand of the node. This represents the reactive power flow rate in an HVAC system. This indicates the reactive power loss of each branch; Feeder power flow constraints: ， ， In the formula, and This indicates the magnitude of line voltage deviation for different line indices. and Indicates the conductance and susceptance of the circuit. Indicates phase angle deviation, Indicates the nominal voltage. and Indicates the active and reactive power parameters related to the branch lines; 62) Set line capacity constraints: ， ， ， In the formula, Indicates the switching variables of the line. Indicates the maximum power flow of the line. and Indicates the resistance and reactance of the circuit; 63) Set power limitation constraints for distributed generation units: ， ， ， In the formula, This represents the minimum active power of a distributed generation unit. This represents the maximum active power of the distributed generation unit. This represents the minimum reactive power of a distributed generation unit. This indicates the maximum reactive power of the distributed generation unit. This represents the power factor of a distributed generation unit. 64) Power limitation constraints of substations: ， ， ， In the formula, This represents the minimum active power purchased from the power grid. This indicates the maximum active power purchased from the power grid. This represents the minimum reactive power purchased from the power grid. This indicates the maximum reactive power purchased from the power grid. This indicates the power factor of the substation; 65) Set constraints on the number of inputs per terminal node: Each node with input requirements has an input stream, and each terminal node has only one input stream. ， ， In the formula, in represents power inflow and out represents power outflow. This indicates that any node n does not belong to the load set, and line l belongs to the set n; 66) In setting the power constraints for the measured line, the power constraints for two adjacent time periods on the same line are as follows: ， l represents different routes, s represents different scenarios, and h represents different time periods. and This represents the line power flow rate between two adjacent time periods. This represents the maximum sum of power call rates between two consecutive time periods.

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