Energy control method and device for energy community, equipment and medium

Abstract

The invention belongs to the field of economic operation of an integrated energy system, and discloses an energy control method, device and equipment for an energy community and a medium. The method comprises the steps of solving a pre-constructed operation model of each household energy system in a community power grid by taking the lowest operation cost J1 of each household energy system as an objective function, and obtaining a first operation scheme of each household energy system; controlling each household energy system in the community power grid to operate according to the first operation scheme; or, solving the pre-constructed operation model of each household energy system in the community power grid by taking the minimum operation cost J2 of the community power grid as the objective function, and obtaining a second operation scheme of each household energy system; and controlling each household energy system in the community power grid to operate according to the second operation scheme. According to the invention, through a multi-level optimization strategy, economic operation of a single household energy system is realized, global optimization is carried out on the community level, and the operation cost of the whole energy community is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of economic operation of integrated energy systems, and in particular relates to an energy control method, device, equipment and medium for an energy community. Background Art

[0002] Building operations contribute significantly to global energy consumption, accounting for about a quarter of total global energy consumption. However, there is great potential to significantly reduce carbon emissions through the widespread adoption of clean energy technologies and the implementation of flexible energy demand in the building sector. When a group of buildings adopts green technologies to generate, store and exchange electricity and heating internally, it is called an energy community. In this context, energy flexibility becomes crucial because it enables the adjustment of demand and supply to achieve various goals such as balancing, peaking or shifting, as well as responding to economic and environmental factors. Energy management strategies can increase the flexibility of energy communities in two key ways: 1) maximizing own consumption by utilizing on-site renewable energy sources (Renewable Energy Sources, or RES); and 2) powering end-use sectors such as electric vehicles and heat pumps. By implementing these energy management strategies, energy communities can effectively take advantage of the benefits of renewable energy, optimize energy use, and contribute to a sustainable and resilient energy system.

[0003] The concept of integrated energy system or multi-energy system, also known as energy hub (EH), refers to a system that integrates multiple energy systems, including electricity, heat and natural gas, as well as various energy technologies such as renewable energy, energy storage and demand response. The main goal of this concept is to optimize the utilization of energy systems and energy technologies by combining them into a unified system, thereby improving efficiency and sustainability. In addition, EH can enhance energy flexibility by enabling the conversion and storage of surplus energy, reducing peak demand, and providing backup power during power outages. However, the current energy management system is not flexible enough in terms of energy storage and scheduling strategies when facing complex loads (such as electric vehicles, heat pumps, etc.), and cannot fully utilize the potential of distributed energy storage devices. The current energy management methods do not provide sufficient support for the integration and interoperability of distributed energy resources, making it difficult to form an efficient energy community network. Therefore, how to optimize the energy flow between different energy sources and loads to improve the flexibility of the energy community is a fundamental challenge in the operation of multi-energy systems. Summary of the invention

[0004] The purpose of the present invention is to provide an energy control method, device, equipment and medium for an energy community, so as to fully utilize the potential of distributed energy storage equipment when facing complex loads.

[0005] In a first aspect, the present invention provides an energy control method for an energy community, comprising:

[0006] Taking the lowest operating cost J1 of each household energy system as the objective function, solving the operating model of each household energy system pre-built in the community power grid, and obtaining the first operating plan of each household energy system; controlling each household energy system in the community power grid to operate according to the first operating plan;

[0007] Alternatively, the operation model of each household energy system pre-constructed in the community power grid is solved with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; each household energy system in the community power grid is controlled to operate according to the second operation plan;

[0008] Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

[0009] The present invention is further improved in that: the expression of the photovoltaic power generation and battery temperature function of the solar photovoltaic module is:

[0010]

[0011] Where P t pv Represents the photovoltaic power generation at time t; PV rate and η pv Respectively represent the rated power of the photovoltaic module and the overall system efficiency; Ir t Represents the global solar radiation at time t; Ir stc Represents global solar radiation under standard test conditions; α pv Indicates the power temperature coefficient; T cell Indicates the PV cell temperature; T t amb represents the ambient temperature at time t; T stc and T nom Respectively represent the reference battery temperature and the nominal operating battery temperature under standard test conditions;

[0012] The expression of the charging state function of the electric vehicle charging station is:

[0013]

[0014] Where, SoC t and SoC t+1 Respectively represent the charging state of the electric vehicle battery at time t and t+1; C bat Indicates the rated capacity of the electric vehicle battery; η ev,cand η ev,d Respectively represent the charging and discharging efficiency of electric vehicles; P t ev represents the charging power of the electric vehicle at time t; Δt represents the charging time interval;

[0015] The expression of the power and performance coefficient function of the heat pump is:

[0016]

[0017] COP t =a0+a1(T t tes -T t amb )

[0018] Where P t hp and Respectively represent the power and heat output of the heat pump at time t; COP t represents the power demand, heat output and performance coefficient of the heat pump at time t; T t tes and T t amb Respectively represent the heat storage temperature and ambient temperature at time t; a0 and a1 are coefficients;

[0019] The expressions for the hot water tank heat capacity and fixed heat loss function of the thermal energy storage module are:

[0020]

[0021] In the formula, C s =cρV, c, ρ and V represent the heat capacity of the hot water tank, the specific heat capacity of water, the density and the water storage capacity respectively; and They represent the space heating and domestic hot water demands at time t respectively; η d is the heat distribution efficiency; is the fixed heat loss of the hot water tank at time t; R s Represents the thermal resistivity of the hot water tank insulation layer; τ is the time delay constant, Rs = τ / Cs.

[0022] The present invention is further improved in that: in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system:

[0023] The objective function of the operation model of the home energy system is:

[0024]

[0025] J1 is the cost function; P t net c t Δt is the electricity cost during the planning period, which depends on the household’s net active power P t net and time-varying electricity price rate c t ; C t bat The cost of battery degradation during the period of charging an electric vehicle; is the charge / discharge power at any time t; α is the battery degradation cost coefficient;

[0026] The constraints of the operation model of the home energy system include:

[0027] Equality constraints for electric vehicle charging power:

[0028]

[0029] P t ev and are respectively the active and reactive power of the electric vehicle at any time t;

[0030] The lower and upper boundaries of the controlled device:

[0031]

[0032] P t eb represents the power of the electric boiler at time t; represents the space heating at time t; wherein the rated subscript letter represents the maximum operating rated value;

[0033] Temperature range constraints for indoor rooms and hot water tanks:

[0034]

[0035] and Respectively represent the upper and lower limits of the temperature of the hot water tank; T t in represents the temperature of the indoor room at time t; and Respectively represent the upper and lower limits of indoor room temperature.

[0036] Net power limit constraints for household active and reactive power:

[0037]

[0038] in, and are the minimum and maximum net active power of the household, respectively; represents the net reactive power of the household at time t; and are the minimum and maximum net reactive power of the household, respectively;

[0039] Constraints of electric vehicle SOC:

[0040]

[0041] SoC min,depart ≤SoC depart ≤SoC max,depart

[0042] Among them, SoC min and SoC max Respectively represent the minimum and maximum SOC of electric vehicle batteries; SoC depart It is the SOC state of the electric vehicle when it leaves the charging station; SoC min,depart and SoC max,depart They respectively represent the minimum and maximum SOC values ​​when the electric vehicle leaves the charging station.

[0043] The present invention is further improved in that: in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system, the specific solution method includes:

[0044] At time t = 1, the home energy management system HEMS collects data on uncontrolled input variables;

[0045] The home energy management system HEMS updates the temperature variable [T tes ,T in ] and the initial value of SOC, and obtain the net power limit from the community energy management system CEMS;

[0046] Check the electric vehicle connection status; if the electric vehicle is newly connected, the home energy management system HEMS will obtain the electric vehicle data;

[0047] Solve the HEMS optimization problem according to the proposed objective function and constraints, and obtain the solution of the controlled variables;

[0048] The home energy management system HEMS applies the first time slot of the solution of the controlled variables to the solar photovoltaic module EV, heat pump HP, electric boiler EB and space heating SH operations at the current time step;

[0049] The home energy management system HEMS evaluates the home net power at the current and next time step and converts the value [P net,Q net ] Notification from the community energy management system CEMS;

[0050] Move the time range by updating t=t+1 and repeat the solution until the last time step t=N is reached TH .

[0051] The present invention is further improved in that: in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the objective function is:

[0052]

[0053] c l is the cost coefficient; P l ref and are respectively the active and reactive power of line l; P l new and are the actual active and reactive powers of line 1 respectively.

[0054] Constraints:

[0055]

[0056] Among them, P i g is the active power of the generator set at node i; P i d is the active power of the load at node i. is the active power of the generator set at node i; is the active power of the node i load. V i is the voltage amplitude of node i; V j is the voltage amplitude at node j; G ij is the conductance between nodes i and j; B ij is the susceptance between nodes i and j; δ ij is the voltage phase angle between nodes i and j. g max and P g min are the upper and lower limits of the active power of the generator set at node i respectively; and are the upper and lower limits of reactive power of the generator set at node i. i max and V i min are the upper and lower limits of the voltage amplitude at node i respectively; P ij and Q ijare the active and reactive powers of the line between nodes i and j, respectively; is the maximum apparent power of the line between nodes i and j. is the maximum load reduction rate of line l.

[0057] The present invention is further improved in that: in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the specific solution method includes:

[0058] At time t = 1, the net power demand for the current and next advance interval is obtained from each household in the community grid;

[0059] Run the power flow calculation for the current interval t and then follow the following sub-algorithm: If there is a voltage sag or branch overload, then: solve the LSO problem and re-run the HEMS schedule optimization; If there is no voltage sag or branch overload, use the existing net power limit;

[0060] Run the power flow calculation at the next advance interval t+1 and then follow the following sub-algorithm: If voltage sag or overload occurs, then: solve the LSO problem;

[0061] Let t = t + 1, and repeat the solution until the last time step t = N is reached TH .

[0062] In a second aspect, the present invention provides an energy control device for an energy community, comprising:

[0063] The first optimization module is used to solve the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function, and obtain the first operation plan of each household energy system; control each household energy system in the community power grid to operate according to the first operation plan;

[0064] Alternatively, the second optimization module is used to solve the operation model of each household energy system pre-constructed in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; control each household energy system in the community power grid to operate according to the second operation plan;

[0065] Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

[0066] In a third aspect, the present invention provides an electronic device, including a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the energy control method of the energy community.

[0067] In a fourth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the energy control method of the energy community is implemented.

[0068] In order to achieve the above object, the present invention adopts the following technical solution:

[0069] The present invention provides an energy control method for an energy community, comprising: solving the operation model of each household energy system pre-constructed in the community power grid with the lowest operation cost J1 of each household energy system as the objective function, and obtaining the first operation plan of each household energy system; controlling each household energy system in the community power grid to operate according to the first operation plan; or solving the operation model of each household energy system pre-constructed in the community power grid with the lowest operation cost J2 of the community power grid as the objective function, and obtaining the second operation plan of each household energy system; controlling each household energy system in the community power grid to operate according to the second operation plan; wherein the operation model of the household energy system is composed of the photovoltaic power generation and battery temperature function of the solar photovoltaic module, the charging state function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function. The present invention not only realizes the economic operation of a single household energy system through a multi-level optimization strategy, but also performs global optimization at the community level, thereby reducing the operation cost of the overall energy community. At the same time, the present invention can coordinate the collaborative utilization of various energy forms, further improve the overall efficiency and sustainability of the integrated energy system, support the integration and interoperability of distributed energy, and promote the efficient allocation and collaborative utilization of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 The figure is a schematic diagram of the structure of an energy control system of an energy community according to an embodiment of the present invention.

[0072] Figure 2 The figure is a flow chart of an energy control method for an energy community according to an embodiment of the present invention.

[0073] Figure 3 This is a household operation cost diagram of the winter optimization algorithm according to an embodiment of the present invention.

[0074] Figure 4 This is a household operating cost diagram of the summer optimization algorithm according to an embodiment of the present invention.

[0075] Figure 5 The figure is a flow chart of an energy control method for an energy community according to an embodiment of the present invention.

[0076] Figure 6 The figure is a schematic diagram of the structure of an energy control device for an energy community according to an embodiment of the present invention.

[0077] Figure 7 The figure is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0078] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0079] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0080] See also Figure 1 and Figure 2 As shown, an embodiment of the present invention provides an energy control method for an energy community, comprising the following steps:

[0081] Step 1: Construct an operation model of the home energy system, which consists of solar photovoltaic modules, electric vehicle charging stations (EV Charging Stations, referred to as EVCS) and residential heating systems; the residential heating system includes heat pump modules and thermal energy storage systems. The specific model is as follows:

[0082] 1) The expression of photovoltaic power generation and battery temperature function of solar photovoltaic module is:

[0083]

[0084] Where P t pv Represents the photovoltaic power generation at time t; PV rate and η pv Respectively represent the rated power (kW) of the photovoltaic module and the overall system efficiency; Ir t Represents the global solar radiation at time t (kW / ㎡); Ir stcIt indicates the global solar radiation under standard test conditions (STC), usually in kW / ㎡. pv Indicates power temperature coefficient (1 / ℃); T cell Indicates the PV cell temperature (°C); T t amb represents the ambient temperature at time t; T stc and T nom They represent the reference battery temperature and the nominal operating battery temperature under standard test conditions (STC), respectively. The relevant PV parameter settings used are as follows: pv =0.0037, T stc =25℃、T nom =41°C.

[0085] 2) The expression of the charging state function of the electric vehicle charging station is:

[0086]

[0087] Where, SoC t and SoC t+1 Respectively represent the state of charge (SOC) of the electric vehicle battery at time t and t+1; C bat Indicates the rated capacity of the electric vehicle battery (kWh); η ev,c and η ev,d Respectively represent the charging and discharging efficiency of electric vehicles; P t ev It represents the charging power of the electric vehicle at time t; Δt represents the charging time interval.

[0088] 3) The expression of the heat pump power and performance coefficient function is:

[0089]

[0090] COP t =a0+a1(T t tes -T t amb )

[0091] Where P t hp and Respectively represent the power and heat output of the heat pump (HP) at time t. t The coefficient of performance (COP) represents the power demand and heat output of the heat pump HP at time t. t tes and T tamb They represent the heat storage temperature and the ambient temperature at time t, respectively. In addition, a0 and a1 are coefficients, and their values ​​are 4.8547 and -0.0427, respectively.

[0092] 4) Thermal energy storage system

[0093] Thermal energy storage systems (TES) are used as heating buffers in residential buildings and are charged by HP. There are two types of TES, namely active thermal storage and passive thermal storage. The widely used active thermal storage in households are hot water tanks and electric boilers EB, while passive thermal storage can be represented by the thermal mass inertia of the building. In this case, the active TES is a hot water tank and is heated by a heat pump HP. In addition, the hot water tank and / or electric boiler EB also provide thermal energy for the thermal needs of space heating (SH) and domestic hot water (DHW). The TES model is based on the equilibrium heat transfer equation as follows:

[0094]

[0095] In the formula, C s =cρV, c, ρ and V represent the heat capacity of the hot water tank, the specific heat capacity of water, the density and the water storage capacity respectively. and They represent the space heating and domestic hot water demands at time t, respectively; ηd is the heat distribution efficiency, which represents the heat loss in pipes and heat exchangers. is the fixed heat loss of the hot water tank at time t. R s represents the thermal resistivity of the hot water tank insulation layer. A time delay constant (τ) is introduced to account for the energy loss caused by heat transfer from the heat storage material to the surrounding environment through the hot water tank insulation layer, where Rs = τ / Cs. Assuming Rs is constant, the water temperature in the hot water tank is completely uniform. By solving the differential problem of the above equation, the dynamic model of temperature in TES can be written as follows:

[0096]

[0097] Where, T t tes and are the heat storage temperatures of the hot water tank at time t and t+1 respectively. The initial state of the hot water tank temperature is the starting temperature,

[0098] Step 2: Propose an explicit optimization model and algorithm based on model predictive control (MPC) for the home energy management system (HEMS).

[0099] 1) The objective function of the household energy system operation optimization problem is:

[0100]

[0101] T H is the total time period; J1 is the cost function; P t net c t Δt is the electricity cost during the planning period, which depends on the household’s net active power P t net and time-varying electricity price rate c t ; The cost of battery degradation during the period of charging an electric vehicle; is the charge / discharge power at any time t, in kVA. α is the battery degradation cost coefficient introduced to avoid unnecessary charging and discharging.

[0102] 2) The constraints of the optimization problem of household energy system operation are:

[0103] Equality constraints for electric vehicle charging power:

[0104]

[0105] P t ev and are the active and reactive powers of the electric vehicle at any time t, respectively.

[0106] The lower and upper boundaries of the controlled device:

[0107]

[0108] P t eb represents the power of the electric boiler at time t; represents the space heating at time t.

[0109] Among them, the rated subscript letter indicates its maximum operating rating. In this case, the operating power of electric vehicle charging, heat pump HP and electric boiler EB, and heat input of space heating SH cannot exceed their rated capacity. The minimum and maximum subscript letters, as shown in the equation.

[0110] Temperature range constraints for indoor rooms and hot water tanks:

[0111]

[0112] and Respectively represent the upper and lower limits of the temperature of the hot water tank; T t in represents the temperature of the indoor room at time t; and Respectively represent the upper and lower limits of indoor room temperature.

[0113] Net power limit constraints for household active and reactive power:

[0114]

[0115] and are the minimum and maximum net active power of the household, respectively; represents the net reactive power of the household at time t; and are the minimum and maximum net reactive power of the household respectively.

[0116] Constraints of electric vehicle SOC:

[0117]

[0118] SoC min,depart ≤SoC depart ≤SoC max,depart

[0119] SoC min and SoC max Respectively represent the minimum and maximum SOC of electric vehicle batteries; SoC depart It is the SOC state of the electric vehicle when it leaves the charging station; SoC min,depart and SoC max,depart They respectively represent the minimum and maximum SOC values ​​when the electric vehicle leaves the charging station.

[0120] 3) The specific optimization algorithm is as follows:

[0121] (1) At time t = 1, the home energy management system HEMS collects data on uncontrolled input variables, including the base load (P base ,Q base ), P pv ,Q pv ,T amb , Ir and electricity price. At the same time, each variable data contains the current time step and planning horizon (T H ) on the predicted value.

[0122] (2) The home energy management system HEMS updates the temperature variable [T tes ,T in ] and the initial value of SOC, and obtain the net power limit from the community energy management system CEMS (Community Energy Management System).

[0123] (3) Checking the EV connection status: If the EV is newly connected, the home energy management system HEMS obtains EV data, including charging rated power, battery capacity, initial SOC, minimum required SOC, and estimated departure time.

[0124] (4) Solve the optimization problem of the home energy management system (HEMS) based on the objective function and constraints, and obtain the solution of the controlled variables.

[0125] (5) The home energy management system HEMS only applies the first time slot of the control solution to the EV, HP, EB, and SH operations at the current time step.

[0126] (6) The home energy management system HEMS evaluates the home net power at the current and next time step and converts the value [P net ,Q net ]Notify the community energy management system CEMS.

[0127] (7) Move the time range by updating t = t + 1 and repeating steps (1)–(7) until the last time step t = N is reached. TH .

[0128] The present invention proposes a real-time optimization algorithm for a home energy management system HEMS to dispatch the energy system using a decentralized optimizer. The net demand power is then notified to the central aggregator or grid operator during the planning step. MPC is a real-time optimization method that is often used to deal with uncertainty. MPC is an iterative optimization method for a finite time horizon. MPC evaluates the objective function and constraints at each time step over the entire time horizon, producing an optimal solution that is achieved only in the current time slot. In subsequent iterations, MPC updates the initial values ​​in the dynamic model based on the actual state parameters of the previous run, and repeats the optimization process for the remaining time steps. Based on the MPC concept, the proposed optimization algorithm for the home energy management system HEMS is presented in Algorithm 1.

[0129] Step 3. Develop a practical coordination algorithm based on AC optimal power flow to coordinate the CEMS operation scheme of multi-home energy management systems HEMS in the community power grid for the second stage energy management.

[0130] If the grid encounters system overload or unacceptable voltage drop, the grid operator will ask users to reduce the demand power of existing operations. Load shedding optimization (LSO) is an effective way to deal with this situation, providing users with the best load point to maintain grid security. In this case, LSO applies optimal power flow (OPF) technology to minimize load shedding while considering grid security constraints. The objective function of load shedding optimization is based on AC-OPF and can be expressed as follows:

[0131] Objective function:

[0132]

[0133] Constraints:

[0134]

[0135] The specific construction method is as follows:

[0136] 1) At time t=1, the net power demand for the current and next advance intervals is obtained from each household in the community grid.

[0137] 2) Run LF for the current interval (t), and then follow the following sub-algorithm:

[0138] If there is voltage drop or branch overload, then: LSO problem solved. HEMS re-operation schedule optimized.

[0139] If there is no voltage drop or branch overload then: Do nothing (use existing net power limit).

[0140] 3) Run LF at the next advance interval (t+1), and then follow the following sub-algorithm:

[0141] If voltage sag or overload occurs, then: Resolve LSO issue. Provide new net power limit for HEMS optimization in next interval.

[0142] 4) Set t = t + 1, and repeat steps 1) to 4) until the last time step t = N is reached. TH .

[0143] The present invention develops a joint simulation framework by implementing the optimization algorithm and simulation scenarios in a Python environment and modeling the power grid in PowerFactory-DIgSILENT to connect Algorithm 1 and Algorithm 2 together.

[0144] Among them, the collaborative simulation framework consists of the following four main processes:

[0145] 1) At the beginning of the simulation (t=1), the indoor temperature, heat storage temperature, electric vehicle battery SOC and time-varying input data (ambient temperature, solar irradiation, electricity price, base load and DHW demand) are initialized and updated.

[0146] 2) Each HEMS runs the optimization (Algorithm 1) and collects the net power of the households. Then, the net power data of all households are sent from the Python environment to PowerFactory through the API for power flow calculation (LF). Based on Algorithm 2, LF is performed twice for the first and second time slots.

[0147] 3) Once the LF results are obtained, the grid security on both time slots is evaluated. If there is any voltage drop or overload on the grid, PowerFactory will perform load shedding optimization for the unsafe time slots.

[0148] 4) The new net power limits for all households are obtained from the PowerFactory and updated for the next iteration in the following way. If there is any unsafe load flow in the first time slot, the HEMS must rerun the optimization with the new net power limit before proceeding to the next iteration. In addition, if there is any unsafe load flow in the second time slot, the new power limit in that time slot is used as the limiting constraint for the next iteration.

[0149] The embodiment of the present invention provides an energy control method for an energy community in combination with a specific example, including: the proposed method is evaluated using a modified IEEE European low voltage (LV) test feeder with a 116-bus radial distribution topology. The grid topology represents a typical unbalanced low voltage distribution system used in Europe, with a primary transformer voltage of a constant 1.00 pu and a standard bus voltage range of 0.90-1.10 pu. The grid is connected to an 11 kV voltage network through an 800 kVA distribution transformer to supply power to 55 households.

[0150] All 55 buildings are assumed to be of the same model. The building envelope consists of four walls and a roof with a window area of ​​5.5 m2, where the solar heat gain coefficient (SHGC) is set to 0.583. All households have an electric car and a 5 kW photovoltaic system is installed. The base load profiles of the 55 households are randomly obtained from the electricity demand profiles generated in the study with a resolution of 15 minutes time step. The profiles are generated using the Widen-Markov chain random method, which includes two main steps. 1) The occupancy activity pattern of each household member is generated; 2) The activity pattern is combined with the appliance parameters and input daylight data to generate the electricity demand of each household.

[0151] There are four models of electric vehicles: Tesla Model 3, Volkswagen ID.3, Renault Zoe and Dacia Spring, which are randomly selected for each household with penetration rates of 40%, 25%, 20% and 15% respectively. These percentages are adjusted based on data on electric vehicle models sold in Europe in 2021 to simulate a variety of electric vehicle models in the grid. All electric vehicles are assumed to have the same charge / discharge efficiency of 0.95. The minimum departure SOC is set to 0.80 to ensure that the energy is sufficient for daily travel.

[0152] The energy flexibility of the household is evaluated using cost savings (CS) and flexibility factor (FF). The peak electricity rate refers to the period from 8 am to 8 pm (peak period), and the rest of the period is the off-peak rate (off-peak period). Then, the proposed method is compared with the energy flexibility index of each household under the base case under the hourly electricity price in winter and summer.

[0153] The operating costs of each household are compared between the base case and the proposed optimization algorithm in winter and summer. Figure 3 and Figure 4 In the figure, the left bar graph shows the base case results without cooperative control, and the right bar graph shows the proposed cooperative method. The results show that in both seasons, especially in winter, the operating costs of all households using the energy control method of the energy community proposed by the present invention are significantly reduced compared to the base case.

[0154] The average weekly electricity bill for all households using the base case and the proposed method is approximately SEK 2431.0 and SEK 1559.7 in winter, respectively, and approximately SEK 102.6 and SEK 48.1 in summer, respectively. In addition, some houses with excess PV and V2G generation capacity can generate income by selling electricity to the grid (at negative cost) during the summer week. This shows that there is a clear correlation between operating costs and household energy system scheduling. At the same time, the MPC method based on the optimization algorithm proposed in the present invention can reduce household costs by shifting energy consumption from periods of high electricity prices to periods of low electricity prices.

[0155] See also Figure 5 As shown, the present invention provides an energy control method for an energy community, comprising:

[0156] S1, solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function, and obtaining the first operation plan of each household energy system; controlling each household energy system in the community power grid to operate according to the first operation plan;

[0157] S2, or, solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function, and obtaining the second operation plan of each household energy system; controlling each household energy system in the community power grid to operate according to the second operation plan;

[0158] Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

[0159] In a specific implementation, the expression of the photovoltaic power generation and battery temperature function of the solar photovoltaic module is:

[0160]

[0161] Where P t pv Represents the photovoltaic power generation at time t; PV rate and η pv Respectively represent the rated power of the photovoltaic module and the overall system efficiency; Ir t Represents the global solar radiation at time t; Ir stc Represents global solar radiation under standard test conditions; α pv Indicates the power temperature coefficient; T cell Indicates the PV cell temperature; T t amb represents the ambient temperature at time t; T stc and T nom Respectively represent the reference battery temperature and the nominal operating battery temperature under standard test conditions;

[0162] The expression of the charging state function of the electric vehicle charging station is:

[0163]

[0164] Where, SoC t and SoC t+1 Respectively represent the charging state of the electric vehicle battery at time t and t+1; C bat Indicates the rated capacity of the electric vehicle battery; η ev,c and η ev,d Respectively represent the charging and discharging efficiency of electric vehicles; P t ev represents the charging power of the electric vehicle at time t; Δt represents the charging time interval;

[0165] The expression of the power and performance coefficient function of the heat pump is:

[0166]

[0167] COP t =a0+a1(T t tes -T t amb )

[0168] Where P t hp and Respectively represent the power and heat output of the heat pump at time t; COP t represents the power demand, heat output and performance coefficient of the heat pump at time t; T t tes and T t amb Respectively represent the heat storage temperature and ambient temperature at time t; a0 and a1 are coefficients;

[0169] The expressions for the hot water tank heat capacity and fixed heat loss function of the thermal energy storage module are:

[0170]

[0171] In the formula, C s =cρV, c, ρ and V represent the heat capacity of the hot water tank, the specific heat capacity of water, the density and the water storage capacity respectively; and They represent the space heating and domestic hot water demands at time t respectively; η d is the heat distribution efficiency; is the fixed heat loss of the hot water tank at time t; R s Represents the thermal resistivity of the hot water tank insulation layer; τ is the time delay constant, Rs = τ / Cs.

[0172] In a specific implementation, in the step of solving the operation model of each household energy system pre-constructed in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system:

[0173] The objective function of the operation model of the home energy system is:

[0174]

[0175] J1 is the cost function; P t net c t Δt is the electricity cost during the planning period, which depends on the household’s net active power P t net and time-varying electricity price rate c t ; The cost of battery degradation during the period of charging an electric vehicle; is the charge / discharge power at any time t; α is the battery degradation cost coefficient;

[0176] The constraints of the operation model of the home energy system include:

[0177] Equality constraints for electric vehicle charging power:

[0178]

[0179] P t ev and are the active and reactive powers of the electric vehicle at any time t, respectively.

[0180] The lower and upper boundaries of the controlled device:

[0181]

[0182] P t eb represents the power of the electric boiler at time t; represents the space heating at time t.

[0183] Among them, the rated subscript letter indicates the maximum operating rating;

[0184] Temperature range constraints for indoor rooms and hot water tanks:

[0185]

[0186] and Respectively represent the upper and lower limits of the temperature of the hot water tank; T t in represents the temperature of the indoor room at time t; and Respectively represent the upper and lower limits of indoor room temperature.

[0187] Net power limit constraints for household active and reactive power:

[0188]

[0189] in, and are the minimum and maximum net active power of the household, respectively; represents the net reactive power of the household at time t; and are the minimum and maximum net reactive power of the household respectively.

[0190] Constraints of electric vehicle SOC:

[0191]

[0192] SoC min，depart ≤SoC depart ≤SoC max，depart

[0193] Among them, SoC min and SoC max Respectively represent the minimum and maximum SOC of electric vehicle batteries; SoC depart It is the SOC state of the electric vehicle when it leaves the charging station; SoC min,depart and SoC max,depart They respectively represent the minimum and maximum SOC values ​​when the electric vehicle leaves the charging station.

[0194] In a specific implementation, in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system, the specific solution method includes:

[0195] S11, at time t=1, the home energy management system HEMS collects data on uncontrolled input variables;

[0196] S12, the home energy management system HEMS updates the temperature variable [T tes ,T in ] and the initial value of SOC, and obtain the net power limit from the community energy management system CEMS;

[0197] S13, checking the connection status of the electric vehicle; if the electric vehicle is newly connected, the home energy management system HEMS will obtain the electric vehicle data;

[0198] S14, solving the HEMS optimization problem according to the proposed objective function and constraints, and obtaining the solution of the controlled variables;

[0199] S15, the home energy management system HEMS applies the first time slot of the solution of the controlled variables to the solar photovoltaic module EV, the heat pump HP, the electric boiler EB and the space heating SH operations at the current time step;

[0200] S16, the home energy management system HEMS evaluates the home net power at the current and next time step and converts the value [P net ,Q net ] Notification from the community energy management system CEMS;

[0201] S17, move the time range by updating t=t+1, and repeat steps S11-S17 until the last time step t=N is reachedTH .

[0202] In a specific implementation, in the step of solving the operation model of each home energy system pre-constructed in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each home energy system, the objective function is:

[0203]

[0204] c l is the cost coefficient; P l ref and are respectively the active and reactive power of line l; P l new and are the actual active and reactive powers of line 1 respectively.

[0205] Constraints:

[0206]

[0207] P i g is the active power of the generator set at node i; P i d is the active power of the load at node i. is the active power of the generator set at node i; is the active power of the node i load. V i is the voltage amplitude of node i; V j is the voltage amplitude at node j; G ij is the conductance between nodes i and j; B ij is the susceptance between nodes i and j; δ ij is the voltage phase angle between nodes i and j. g max and P g min are the upper and lower limits of the active power of the generator set at node i respectively; and are the upper and lower limits of reactive power of the generator set at node i. i max and V i min are the upper and lower limits of the voltage amplitude at node i respectively; P ij and Q ij are the active and reactive powers of the line between nodes i and j, respectively; is the maximum apparent power of the line between nodes i and j. is the maximum load reduction rate of line l.

[0208] In a specific implementation, in the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the specific solution method includes:

[0209] S21. At time t=1, obtain the net power demand of the current and next advance interval from each household in the community grid;

[0210] S22. Run the power flow calculation for the current interval t, and then follow the following sub-algorithm: If there is a voltage drop or branch overload, then: solve the LSO problem and re-run the HEMS schedule optimization; If there is no voltage drop or branch overload, use the existing net power limit;

[0211] S23. Run the power flow calculation at the next advance interval t+1, and then follow the following sub-algorithm: If voltage drop or overload occurs, then: solve the LSO problem;

[0212] S24, set t = t + 1, and repeat steps S21-S24 until the last time step t = N is reached TH .

[0213] In a specific embodiment, the following steps are also included:

[0214] The power grid is modeled in PowerFactory-DIgSILENT, and a joint simulation framework is built to connect the steps of optimizing the operation of each household energy system with the lowest operating cost J1 as the objective function, and the steps of optimizing the operation of each household energy system in the community power grid with the lowest operating cost J2 as the objective function;

[0215] The joint simulation framework includes:

[0216] 1) At the beginning of the simulation, t = 1, the indoor temperature, heat storage temperature, electric vehicle battery SOC and time-varying input data are initialized and updated;

[0217] 2) Each home energy management system HEMS takes the lowest operating cost J1 as the objective function, performs optimized operation and collects the net power of the home; then, the net power data of all homes are sent from the Python environment to PowerFactory through the API for power flow calculation; based on the lowest operating cost J2 of each home energy system in the community power grid, the system is optimized and two power flow calculations are performed for the first and second time slots;

[0218] 3) Once the power flow calculation results are obtained, the grid security on the two time slots is evaluated; if there is any voltage drop or overload on the grid, PowerFactory will optimize the load shedding for the unsafe time slots;

[0219] 4) The new net power limits for all homes are obtained from the PowerFactory and updated for use in the next iteration in the following way; if there is any unsafe load flow in the first time slot, the home energy management system HEMS re-runs the optimization using the new net power limit before proceeding to the next iteration; if there is any unsafe load flow in the second time slot, the new power limit in that time slot is used as the limiting constraint for the next iteration.

[0220] See also Figure 6 As shown, an embodiment of the present invention provides an energy control device for an energy community, including:

[0221] The first optimization module is used to solve the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function, and obtain the first operation plan of each household energy system; control each household energy system in the community power grid to operate according to the first operation plan;

[0222] Alternatively, the second optimization module is used to solve the operation model of each household energy system pre-constructed in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; control each household energy system in the community power grid to operate according to the second operation plan;

[0223] Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

[0224] In a specific embodiment, it also includes a joint simulation module, which is used to model the power grid in PowerFactory-DIgSILENT, build a joint simulation framework, and connect the steps of optimizing the operation of each household energy system with the lowest operating cost J1 as the objective function and the steps of optimizing the operation of each household energy system in the community power grid with the lowest operating cost J2 as the objective function.

[0225] In a specific implementation, the joint simulation framework includes:

[0226] 1) At the beginning of the simulation, t = 1, the indoor temperature, heat storage temperature, electric vehicle battery SOC and time-varying input data are initialized and updated;

[0227] 2) Each home energy management system HEMS takes the lowest operating cost J1 as the objective function, performs optimized operation and collects the net power of the home; then, the net power data of all homes are sent from the Python environment to PowerFactory through the API for power flow calculation; based on the lowest operating cost J2 of each home energy system in the community power grid, the system is optimized and two power flow calculations are performed for the first and second time slots;

[0228] 3) Once the power flow calculation results are obtained, the grid security on the two time slots is evaluated; if there is any voltage drop or overload on the grid, PowerFactory will optimize the load shedding for the unsafe time slots;

[0229] 4) The new net power limits for all homes are obtained from the PowerFactory and updated for use in the next iteration in the following way; if there is any unsafe load flow in the first time slot, the home energy management system HEMS re-runs the optimization using the new net power limit before proceeding to the next iteration; if there is any unsafe load flow in the second time slot, the new power limit in that time slot is used as the limiting constraint for the next iteration.

[0230] See also Figure 7 As shown, an embodiment of the present invention provides an electronic device 100 for implementing an energy control method for an energy community; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.

[0231] The memory 101 can be used to store the computer program 103, and the processor 102 implements the steps of the energy control method of an energy community described in the embodiment by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0232] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0233] The memory 101 in the electronic device 100 stores a plurality of instructions to implement an energy control method for an energy community, and the processor 102 can execute the plurality of instructions to implement:

[0234] Taking the lowest operating cost J1 of each household energy system as the objective function, solving the operating model of each household energy system pre-built in the community power grid, and obtaining the first operating plan of each household energy system; controlling each household energy system in the community power grid to operate according to the first operating plan;

[0235] Alternatively, the operation model of each household energy system pre-constructed in the community power grid is solved with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; each household energy system in the community power grid is controlled to operate according to the second operation plan;

[0236] Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

[0237] If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0238] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0239] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0240] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0241] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0242] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An energy control method for an energy community, characterized in that: include: Taking the lowest operating cost J1 of each household energy system as the objective function, solving the operating model of each household energy system pre-built in the community power grid, and obtaining the first operating plan of each household energy system; controlling each household energy system in the community power grid to operate according to the first operating plan; Alternatively, the operation model of each household energy system pre-constructed in the community power grid is solved with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; each household energy system in the community power grid is controlled to operate according to the second operation plan; Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

2. The energy control method of the energy community according to claim 1, characterized in that: The expression of the photovoltaic power generation and battery temperature function of the solar photovoltaic module is: Where P t pv Represents the photovoltaic power generation at time t; PV rate and η pv Respectively represent the rated power of the photovoltaic module and the overall system efficiency; Ir t Represents the global solar radiation at time t; Ir stc Represents global solar radiation under standard test conditions; α pv Indicates the power temperature coefficient; T cell Indicates the PV cell temperature; T t amb represents the ambient temperature at time t; T stc and T nom Respectively represent the reference battery temperature and the nominal operating battery temperature under standard test conditions; The expression of the charging state function of the electric vehicle charging station is: Where, SoC t and SoC t+1 Respectively represent the charging state of the electric vehicle battery at time t and t+1; C bat Indicates the rated capacity of the electric vehicle battery; η ev,c and η ev,d Respectively represent the charging and discharging efficiency of electric vehicles; P t ev represents the charging power of the electric vehicle at time t; Δt represents the charging time interval; The expression of the power and performance coefficient function of the heat pump is: Where P t hp and Respectively represent the power and heat output of the heat pump at time t; COP t represents the power demand, heat output and performance coefficient of the heat pump at time t; T t tes and T t amb Respectively represent the heat storage temperature and ambient temperature at time t; a0 and a1 are coefficients; The expressions for the hot water tank heat capacity and fixed heat loss function of the thermal energy storage module are: In the formula, C s =cρV, c, ρ and V represent the heat capacity of the hot water tank, the specific heat capacity of water, the density and the water storage capacity respectively; and They represent the space heating and domestic hot water demands at time t, respectively; η d is the heat distribution efficiency; is the fixed heat loss of the hot water tank at time t; R s Represents the thermal resistivity of the hot water tank insulation layer; τ is the time delay constant, Rs = τ / Cs.

3. The energy control method of the energy community according to claim 1, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system: The objective function of the operation model of the home energy system is: J1 is the cost function; P t net c t Δt is the electricity cost during the planning period, which depends on the household’s net active power P t net and time-varying electricity price rate c t ; The cost of battery degradation during the period of charging an electric vehicle; is the charge / discharge power at any time t; α is the battery degradation cost coefficient; The constraints of the operation model of the home energy system include: Equality constraints for electric vehicle charging power: P t ev and Q t ev are the active and reactive powers of the electric vehicle at time t, respectively; The lower and upper boundaries of the controlled device: P t eb represents the power of the electric boiler at time t; represents the space heating at time t; Among them, the rated subscript letter indicates the maximum operating rating; Temperature range constraints for indoor rooms and hot water tanks: and Respectively represent the upper and lower limits of the temperature of the hot water tank; T t in represents the temperature of the indoor room at time t; and Respectively represent the upper and lower limits of the indoor room temperature; Net power limit constraints for household active and reactive power: in, and are the minimum and maximum net active power of the household, respectively; represents the net reactive power of the household at time t; and are the minimum and maximum net reactive power of the household, respectively; Constraints of electric vehicle SOC: SoC min,depart ≤SoC depart ≤SoC max,depart Among them, SoC min and SoC max Respectively represent the minimum and maximum SOC of electric vehicle batteries; SoC depart It is the SOC state of the electric vehicle when it leaves the charging station; SoC min,depart and SoC max,depart They respectively represent the minimum and maximum SOC values ​​when the electric vehicle leaves the charging station.

4. The energy control method of the energy community according to claim 3, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system, the specific solution method includes: At time t = 1, the home energy management system HEMS collects data on uncontrolled input variables; The home energy management system HEMS updates the temperature variable [T tes ,T in ] and the initial value of SOC, and obtain the net power limit from the community energy management system CEMS; Check the electric vehicle connection status; if the electric vehicle is newly connected, the home energy management system HEMS will obtain the electric vehicle data; Solve the HEMS optimization problem according to the objective function and constraints, and obtain the solution of the controlled variables; The home energy management system HEMS applies the first time slot of the solution of the controlled variables to the solar photovoltaic module EV, heat pump HP, electric boiler EB and space heating SH operations at the current time step; The home energy management system HEMS evaluates the home net power at the current and next time step and converts the value [P net ,Q net ] Notification from the community energy management system CEMS; The time range is moved by updating t=t+1, and the solution is repeated until the last time step t=N is reached. TH .

5. The energy control method of the energy community according to claim 1, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the objective function is: c l is the cost coefficient; P l ref and are respectively the active and reactive power of line l; P l new and are the actual active and reactive power of line l respectively; Constraints: Among them, P i g is the active power of the generator set at node i; P i d is the active power of the load at node i; is the active power of the generator set at node i; is the active power of the load at node i; V i is the voltage amplitude of node i; V j is the voltage amplitude at node j; G ij is the conductance between nodes i and j; B ij is the susceptance between nodes i and j; δ ij is the voltage phase angle between nodes i and j; and are the upper and lower limits of the active power of the generator set at node i respectively; and are the upper and lower limits of reactive power of the generator set at node i; V i max and V i min are the upper and lower limits of the voltage amplitude at node i respectively; P ij and Q ij are the active and reactive powers of the line between nodes i and j, respectively; is the maximum apparent power of the line between nodes i and j; is the maximum load reduction rate of line l.

6. The energy control method of the energy community according to claim 5, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the specific solution method includes: At time t = 1, the net power demand for the current and next advance interval is obtained from each household in the community grid; Run the power flow calculation for the current interval t and then follow the following sub-algorithm: If there is a voltage sag or branch overload, then: solve the LSO problem and re-run the HEMS schedule optimization; If there is no voltage sag or branch overload, use the existing net power limit; Run the power flow calculation at the next advance interval t+1 and then follow the following sub-algorithm: If voltage sag or overload occurs, then: solve the LSO problem; Let t = t + 1, and repeat the solution until the last time step t = N is reached TH .

7. The energy control device of the energy community is characterized by: include: The first optimization module is used to solve the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function, and obtain the first operation plan of each household energy system; control each household energy system in the community power grid to operate according to the first operation plan; Alternatively, the second optimization module is used to solve the operation model of each household energy system pre-constructed in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system; control each household energy system in the community power grid to operate according to the second operation plan; Among them, the operation model of the home energy system is composed of the photovoltaic power generation and battery temperature functions of the solar photovoltaic module, the charging status function of the electric vehicle charging station and the residential heating system function; the residential heating system function includes the power and performance coefficient function of the heat pump, the heat capacity of the hot water tank of the thermal energy storage module and the fixed heat loss function.

8. The energy control device for the energy community according to claim 7, characterized in that: The expression of the photovoltaic power generation and battery temperature function of the solar photovoltaic module is: Where P t pv Represents the photovoltaic power generation at time t; PV rate and η pv Respectively represent the rated power of the photovoltaic module and the overall system efficiency; Ir t Represents the global solar radiation at time t; Ir stc Represents global solar radiation under standard test conditions; α pv Indicates the power temperature coefficient; T cell Indicates the PV cell temperature; T t amb represents the ambient temperature at time t; T stc and T nom Respectively represent the reference battery temperature and the nominal operating battery temperature under standard test conditions; The expression of the charging state function of the electric vehicle charging station is: Where, SoC t and SoC t+1 Respectively represent the charging state of the electric vehicle battery at time t and t+1; C bat Indicates the rated capacity of the electric vehicle battery; η ev,c and η ev,d Respectively represent the charging and discharging efficiency of electric vehicles; P t ev represents the charging power of the electric vehicle at time t; Δt represents the charging time interval; The expression of the power and performance coefficient function of the heat pump is: Where P t hp and Respectively represent the power and heat output of the heat pump at time t; COP t represents the power demand, heat output and performance coefficient of the heat pump at time t; T t tes and T t amb Respectively represent the heat storage temperature and ambient temperature at time t; a0 and a1 are coefficients; The expressions for the hot water tank heat capacity and fixed heat loss function of the thermal energy storage module are: In the formula, C s =cρV, c, ρ and V represent the heat capacity of the hot water tank, the specific heat capacity of water, the density and the water storage capacity respectively; and They represent the space heating and domestic hot water demands at time t, respectively; η d is the heat distribution efficiency; is the fixed heat loss of the hot water tank at time t; R s Represents the thermal resistivity of the hot water tank insulation layer; τ is the time delay constant, Rs = τ / Cs.

9. The energy control device for an energy community according to claim 7, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system: The objective function of the operation model of the home energy system is: J1 is the cost function; P t net c t Δt is the electricity cost during the planning period, which depends on the household’s net active power P t net and time-varying electricity price rate c t ; The cost of battery degradation during the period of charging an electric vehicle; is the charge / discharge power at any time t; α is the battery degradation cost coefficient; The constraints of the operation model of the home energy system include: Equality constraints for electric vehicle charging power: P t ev and are respectively the active and reactive power of the electric vehicle at any time t; The lower and upper boundaries of the controlled device: P t eb represents the power of the electric boiler at time t; represents the space heating at time t; wherein the rated subscript letter represents the maximum operating rated value; Temperature range constraints for indoor rooms and hot water tanks: and Respectively represent the upper and lower limits of the temperature of the hot water tank; T t in represents the temperature of the indoor room at time t; and Respectively represent the upper and lower limits of the indoor room temperature; Net power limit constraints for household active and reactive power: in, and are the minimum and maximum net active power of the household, respectively; represents the net reactive power of the household at time t; Q minnet and Q maxnet are the minimum and maximum net reactive power of the household, respectively; Constraints of electric vehicle SOC: SoC min,depart ≤SoC depart ≤SoC max,depart Among them, SoC min and SoC max Respectively represent the minimum and maximum SOC of electric vehicle batteries; SoC depart It is the SOC state of the electric vehicle when it leaves the charging station; SoC min,depart and SoC max,depart They respectively represent the minimum and maximum SOC values ​​when the electric vehicle leaves the charging station.

10. The energy control device for the energy community according to claim 9, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest operation cost J1 of each household energy system as the objective function to obtain the first operation plan of each household energy system, the specific solution method includes: At time t = 1, the home energy management system HEMS collects data on uncontrolled input variables; The home energy management system HEMS updates the temperature variable [T tes ,T in ] and the initial value of SOC, and obtain the net power limit from the community energy management system CEMS; Check the electric vehicle connection status; if the electric vehicle is newly connected, the home energy management system HEMS will obtain the electric vehicle data; Solve the HEMS optimization problem according to the objective function and constraints, and obtain the solution of the controlled variables; The home energy management system HEMS applies the first time slot of the solution of the controlled variables to the solar photovoltaic module EV, heat pump HP, electric boiler EB and space heating SH operations at the current time step; The home energy management system HEMS evaluates the home net power at the current and next time step and converts the value [P net ,Q net ] Notification from the community energy management system CEMS; Move the time range by updating t=t+1 and repeat the solution until the last time step t=N is reached TH .

11. The energy control device for an energy community according to claim 7, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the objective function is: c l is the cost coefficient; P l ref and are respectively the active and reactive power of line l; P l new and are the actual active and reactive powers of line l respectively; Constraints: Among them, P i g is the active power of the generator set at node i; P i d is the active power of the load at node i; is the active power of the generator set at node i; is the active power of the load at node i; V i is the voltage amplitude of node i; V j is the voltage amplitude at node j; G ij is the conductance between nodes i and j; B ij is the susceptance between nodes i and j; δ ij is the voltage phase angle between nodes i and j; and are the upper and lower limits of the active power of the generator set at node i respectively; and are the upper and lower limits of reactive power of the generator set at node i; V i max and V i min are the upper and lower limits of the voltage amplitude at node i respectively; P ij and Q ij are the active and reactive powers of the line between nodes i and j, respectively; is the maximum apparent power of the line between nodes i and j; is the maximum load reduction rate of line l.

12. The energy control device for the energy community according to claim 11, characterized in that: In the step of solving the operation model of each household energy system pre-built in the community power grid with the lowest community power grid operation cost J2 as the objective function to obtain the second operation plan of each household energy system, the specific solution method includes: At time t = 1, the net power demand for the current and next advance interval is obtained from each household in the community grid; Run the power flow calculation for the current interval t and then follow the following sub-algorithm: If there is a voltage sag or branch overload, then: solve the LSO problem and re-run the HEMS schedule optimization; If there is no voltage sag or branch overload, use the existing net power limit; Run the power flow calculation at the next advance interval t+1 and then follow the following sub-algorithm: If voltage sag or overload occurs, then: solve the LSO problem; Let t = t + 1, and repeat the solution until the last time step t = N is reached TH .

13. An electronic device, characterized in that: The method comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the energy control method for an energy community as claimed in any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the energy control method for the energy community according to any one of claims 1 to 6 is implemented.

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