Energy-element-based optimization and regulation method for energy utilization system

Abstract

Disclosed in the present invention is an energy-element-based optimization and regulation method for an energy utilization system. The method comprises: on the basis of physical properties and economic attributes of energy elements, constructing an energy utilization right circulation price model based on the energy elements, and determining a price range in energy element circulation; by means of using the price range in energy element circulation and the balance of energy utilization supply and demand as constraint conditions, and using the minimization of user-side energy utilization cost as an objective function, constructing a user-side energy utilization optimization and regulation model based on energy element prices; solving the user-side energy utilization optimization and regulation model, so as to obtain an optimal user-side energy utilization strategy under the minimum user-side energy utilization cost; and establishing a power flow model of a power system network, matching the supply and demand of the energy elements on the basis of the optimal user-side energy utilization strategy and the state of energy element circulation and in combination with constraints of the power system network, so as to determine the actual energy utilization demand of users, and by means of using the minimization of voltage deviation as an objective function, solving the power flow model of the power system network, so as to obtain an optimal power flow distribution of the power system network. Therefore, the unification of heterogeneous energy sources is realized, and the optimization and regulation method for an energy utilization system is implemented.

Description

Energy system optimization and control methods based on energy elements Technical Field

[0001] This invention relates to the field of energy optimization technology, and in particular to a method for optimizing and controlling energy consumption systems based on energy elements. Background Technology

[0002] The user side is the primary source of energy consumption and carbon emissions, and user energy consumption behavior directly impacts the energy efficiency, carbon emissions, and renewable energy carrying capacity of the energy system. A user-driven approach to green and low-carbon energy transformation deserves attention. However, energy systems involve the coupling and transformation of various heterogeneous energy sources such as electricity, heat, cooling, and gas. Due to the lack of a unified benchmark for quantitative evaluation, comparing the actual value of these heterogeneous energy sources is extremely difficult. The energy value of distributed renewable energy and user-side flexible resources differs, and the energy value also varies across different time periods, making calculation and analysis challenging. Failing to reflect these value differences hinders guidance for users to optimize resource allocation. Furthermore, user interaction involves the exchange of energy usage rights and data, and existing electricity / power / time-series information alone is insufficient to support market participants in the circulation of multiple energy categories. These issues prevent the efficient circulation and rational allocation of energy resources, thereby hindering the green and low-carbon transformation of the energy system. Summary of the Invention

[0003] Purpose of the invention: The present invention aims to provide an energy system optimization and control method that uses energy elements as the basic unit for the flow of energy rights.

[0004] Technical solution: The energy system optimization and control method based on energy elements described in this invention includes the following steps:

[0005] (1) Based on the physical and economic attributes of energy elements, construct a price model for the circulation of energy use rights based on energy elements and determine the price range in the circulation of energy elements;

[0006] (2) Using the price range in the circulation of energy elements and the balance between energy supply and demand as constraints, and the minimum energy cost for users as the objective function, a user energy consumption optimization and control model based on energy element prices is constructed.

[0007] (3) Solve the user energy consumption optimization and control model to obtain the optimal user energy consumption strategy under the minimum user energy consumption cost;

[0008] (4) Establish a power system network flow model. Based on the optimal user energy consumption strategy and energy element flow status, and combined with power system network constraints, match the energy element supply and demand to determine the actual energy consumption demand of users. Solve the power system network flow model with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network.

[0009] Furthermore, step (1) is as follows:

[0010] (11) Obtain parameters of energy consumption systems, natural environment parameters, and social environment parameters;

[0011] (12) Based on the energy system parameters, natural environment parameters, and social environment parameters, construct an energy element model E and determine the energy equivalent p of the energy element. eqv ;

[0012] (13) Based on the energy equivalent p of the energy element eqv A price model for the circulation of energy use rights based on energy elements is constructed to determine the price range in the circulation of energy elements.

[0013] Furthermore, the energy element model E is E={A,X,R}

[0014] In the formula, A is the set of attributes of energy elements, including the set of physical attributes P and the set of economic attributes W; X is the set of influencing factors; (x1, x2, ..., x n ) represents the influencing factors, namely the influencing factors of the energy system, energy form, natural environment and social environment, respectively, and n is the number of influencing factors; R is the set of mapping relationships between the attribute set A of energy element and the set of influencing factors X; F(X) is the mapping function from X to P; G(X) is the mapping function from X to W.

[0015] Furthermore, the energy equivalent p of an energy element eqv for p eqv ∈P

[0016] In the formula, p eqv H(X) represents the energy equivalent of an energy element, expressed in kWh; p represents the energy equivalent of an energy element. eqv The evaluation function; θ eqv,i S is the energy equivalent conversion function of heterogeneous energy source i; i Let I be the flow rate of heterogeneous energy i, in J; I be the set of heterogeneous energy contained in the energy element; and P be the set of physical properties.

[0017] Furthermore, the set of physical properties P of the energy element is P = F(X) P = (p1, p2, ..., p m ) p m =f m (X)

[0018] In the formula, (p1,p2,...,p m ) represents the physical properties of the energy element; m represents the number of physical properties; f m (·) represents the relationship between set X and physical property p. m The mapping.

[0019] Furthermore, the set of economic attributes W is W = G(X,P) = G * (X) W=(w1,w2,...,w k ) w k =g k (x1,x2,...,x n p1, p2, ..., p m ) = g k * (x1,x2,...,x n )

[0020] In the formula, W is the set of economic attributes; (w1, w2, ..., w k ) represents the economic attribute of an energy element; k represents the quantity of the economic attribute; g k (·) represents the economic attribute w from X. k The mapping.

[0021] Furthermore, the price range in the circulation of energy elements. Solve using the following formula:

[0022] In the formula, r sell / r buy The price of energy provided by the energy provider / receiver, in yuan / kWh; Δw u,sell / Δw u,buy The change in energy efficiency after providing / receiving energy units is expressed in units of yuan (w). c,sell / w c,buy The energy cost is defined from the perspective of the energy provider / receiver, in yuan; r is the lower bound of the price for the energy provider, in yuan; r is the upper bound of the price for the energy receiver, in yuan; U is the set of energy utility values ​​for the energy source; C sell / C buy This is a set of energy expenditure costs from the perspective of the energy provider / receiver.

[0023] Furthermore, in step (2), the user energy consumption optimization and control model, with the energy price range and energy supply and demand balance as constraints and the minimum user energy cost as the objective function, is as follows:

[0024] In the formula, C represents the user's energy cost, t is the control period number, T is the number of control periods, e is the energy element type number, E is the number of energy element types, and Δt is the length of the control period. The power purchased by the user from the grid during time period t. Let these represent the power received by energy element e and the power supplied by energy element e in the energy transfer process, respectively. The price per unit of energy that a user purchases from the grid during time period t. The price per unit of energy received by a user during the energy rights circulation within time period t. Provides the energy price per unit for the circulation of energy rights for users within time period t. For the user's energy demand during time period t, and Separate the user's fixed energy demand and available energy demand during time period t. This represents the maximum adjustable capacity of a user up / down within time period t.

[0025] Furthermore, in step (3), mixed integer linear programming, intelligent algorithms and other methods are used to solve the user energy consumption optimization and control model to obtain the energy element flow situation and the corresponding optimal user energy consumption strategy.

[0026] Furthermore, in step (4), the power system network power flow model is solved with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network:

[0027] In the formula, P t,j Q t,j Inject the active and reactive power of node j during time period t. Let P be the active and reactive power generation at node j during time period t. t,j,l Q t,j,l Let t represent the active and reactive power of the branch from node j to node l at the starting node j during time period t. R represents the actual energy demand of user j at node. i,j X i,j Let be the branch resistance and reactance from node i to node j, respectively; n(j) is the set of first-terminal nodes with node j as the last-terminal node; m(j) is the set of last-terminal nodes with node j as the first-terminal node. Let u be the squared voltage values ​​of node i and node j during time period t. base Δu is the squared value of the reference voltage, and Δu is the total voltage deviation of the system.

[0028] Beneficial effects: Compared with the prior art, the significant advantages of this invention are: by optimizing and controlling user energy consumption based on the circulation of energy rights, this invention determines the optimal power flow distribution of the power system, effectively realizes the unified measurement of the value of heterogeneous energy, optimizes the allocation of user energy consumption, and achieves green and low-carbon energy use. Attached Figure Description

[0029] Figure 1 is a schematic diagram of the framework of the energy element model;

[0030] Figure 2 is a schematic diagram of the energy equivalent assessment results of the energy element. Detailed Implementation

[0031] The invention will now be further described with reference to the accompanying drawings.

[0032] The energy system optimization and control method based on energy elements described in this invention includes the following steps:

[0033] (1) Obtain the set of parameters of the influencing factors of energy element.

[0034] Acquire parameters of energy systems such as green energy assets and system source load power; energy form parameters of energy resources such as solar energy, wind energy, biomass energy, and hydropower; natural environmental parameters such as temperature, humidity, light intensity, and wind speed; and social environmental parameters such as national policies, consumer psychology, market demand, and market supply.

[0035] (2) Establish an energy element model.

[0036] An energy element is an energy unit that can be used or traded in an energy system. It is a commodity with both physical and economic attributes. It is a unified description of the physical and economic attributes of the right to use energy in the market. The physical attributes reflect the physical properties of one or more heterogeneous energy sources in the process of optimal allocation and regulation, while the economic attributes reflect its value under a specific environment based on influencing factors and the physical properties of the energy element.

[0037] A standardized energy element model is established as follows: E={A,X,R}

[0038] In the formula, E is a general model of the energy element; where,

[0039] In the formula, A is the set of attributes of energy elements, including the set of physical attributes P and the set of economic attributes W; X is the set of influencing factors; (x1, x2, ..., x n ) represents the influencing factors, namely the influencing factors of the energy system, energy form, natural environment and social environment; n is the number of influencing factors; R is the set of mapping relationships between the attribute set A of energy element and the set of influencing factors X; F(X) is the mapping function from X to P; G(X) is the mapping function from X to W.

[0040] (3) Evaluation of the physical properties of energy elements.

[0041] The physical properties of energy elements are influenced by multiple dimensions of factors, including energy consumption systems, energy forms, natural environment, and social environment, and are a function of influencing factor X.

[0042] A physical property evaluation model for energy elements is established. The physical property P of the energy element describes the specific situation of energy transfer during the energy right circulation process, as follows: P=F(X) P=(p1,p2,...,p m ) pm =f m (X)

[0043] In the formula, P is the set of physical properties; (p1, p2, ..., p m ) represents the physical properties of an energy element, including the energy equivalent, energy structure, and power curve of the traded energy; m represents the number of physical properties; f represents the quantity of physical properties. m (·) represents the relationship between set X and physical property p. m Mapping;

[0044] The energy equivalent conversion function is designed to convert heterogeneous energy sources into a fairly comparable energy equivalent attribute. To provide a unified benchmark, the energy consumed by 1 kW of electrical power continuously for 1 hour is used as one energy equivalent, referred to as the energy equivalent benchmark value. As follows:

[0045] In the formula, p eqv H(X) represents the energy equivalent of an energy element, expressed in kWh; H(X) represents the energy equivalent p. eqv The evaluation function; θ eqv,i S is the energy equivalent conversion function of heterogeneous energy source i; i Let I be the flow rate of heterogeneous energy i, in J; I is the set of heterogeneous energy contained in the energy element.

[0046] (4) Evaluation of the economic attributes of energy elements.

[0047] An assessment model for the economic attributes of energy elements is established. The economic attributes of energy elements are influenced by multiple dimensions, including energy consumption systems, energy forms, natural environment, and social environment, as well as by physical attributes. Therefore, the economic attributes of energy elements are described as functions of influencing factors and physical attributes, and transformed into functions of influencing factor X, as follows: W = G(X, P) = G * (X) W=(w1,w2,...,w k ) w k =g k (x1,x2,...,x n p1, p2, ..., p m ) = g k * (x1,x2,...,x n )

[0048] In the formula, W is the set of economic attributes; (w1, w2, ..., w k ) represents the economic attributes of an energy element, including equivalent value, energy utility, economic benefits, and expenditure costs; k represents the quantity of economic attributes; g represents the quantity of economic attributes. k (·) represents the transition from set X to economic attribute w. kThe mapping; considering that set P is also a function of X, we introduce P = F(X) and construct G. * (·) and g k * (·) Directly describes the functional relationship between W and X.

[0049] (5) Establish a pricing model for the circulation of energy use rights based on energy elements.

[0050] The initial price generation model is constructed as follows: w p,sell ∈W,w p,buy ∈W

[0051] In the formula, w p,sell / w p,buy The economic benefit of the energy provider / receiver, in yuan; r sell / r buy The price of energy provided by the energy provider / receiver, in yuan / kWh; p eqv The energy equivalent of this energy element is expressed in kWh.

[0052] Considering that the equivalent value of energy units is affected by multiple economic attributes, a cost analysis method is used to equate the total value of energy units to the energy unit costs for the energy unit recipients / providers, establishing a cost-benefit equilibrium model for calculating the equivalent value of energy units, as follows: w v,sell ∈W,Δw u,sell ∈W,w c,sell ∈W w v,buy ∈W,Δw u,buy ∈W,w c,buy ∈W

[0053] In the formula, w v,sell / w v,buy The benchmark price of energy equivalent per kWh from the perspective of energy provider / receiver; p eqv w v,sell and p eqv w v,buy Δw represents the consumption surplus (profit) of the energy provider / receiver, respectively, in yuan; u,sell / Δw u,buy The change in energy efficiency after providing / receiving energy elements; element; w c,sell / w c,buy The energy expenditure cost from the perspective of the energy provider / receiver is represented by the unit "yuan"; U is the set of energy utility values ​​of the energy unit; C sell / C buy This is a set of energy expenditure costs from the perspective of the energy provider / receiver.

[0054] The price range in the circulation of energy elements is determined as follows:

[0055] In the formula, r is the lower bound of the price for the energy provider, in yuan; r is the upper bound of the price for the energy receiver, in yuan; obviously, the price is determined if and only if... Only then can energy elements circulate, and at this time, the price range offered by both parties is... Otherwise, the flow will fail, and both parties will need to select a new energy source for the flow.

[0056] (6) Establish a user energy consumption optimization and control model based on energy element prices;

[0057] Users' energy needs are met through a combination of purchasing electricity from the grid and various energy-element-based energy consumption rights trading mechanisms. Therefore, the user's energy consumption optimization scheduling model is as follows:

[0058] In the formula, C represents the user's energy cost, t is the control period number, T is the number of control periods, e is the energy element type number, E is the number of energy element types, and Δt is the length of the control period. The power purchased by the user from the grid during time period t. Let these represent the power received by energy element e and the power supplied by energy element e in the energy transfer process, respectively. The price per unit of energy that a user purchases from the grid during time period t. The price per unit of energy received by a user during the energy rights circulation within time period t. Provides the energy price per unit for the circulation of energy rights for users within time period t. For the user's energy demand during time period t, and Separate the user's fixed energy demand and available energy demand during time period t. This represents the maximum adjustable capacity of a user up / down within time period t.

[0059] Based on the above scheduling model, users adjust the operating status of energy-consuming equipment by changing their own energy consumption behavior according to market information such as grid electricity price and energy consumption rights circulation price, with the goal of minimizing energy consumption cost. In turn, they adjust the available energy demand and obtain the user's optimal energy consumption strategy under the minimum energy consumption cost, thus achieving energy consumption strategy optimization.

[0060] (7) A method for calculating the optimal power flow of a power system based on the circulation of energy consumption rights;

[0061] Various energy users solve their own energy consumption optimization scheduling models to obtain their optimal energy consumption strategies and energy element flow states. The energy consumption rights circulation platform matches the energy element supply and demand within the system to the greatest extent possible based on power system network constraints, and solves the power flow model with the goal of minimizing voltage deviation based on the actual energy demand of each user after matching. The simplified power system power flow optimization model is as follows:

[0062] In the formula, P t,j Q t,j Inject the active and reactive power of node j during time period t. Let P be the active and reactive power generation at node j during time period t. t,j,l Q t,j,l Let t represent the active and reactive power of the branch from node j to node l at the starting node j during time period t. R represents the actual energy demand of user j at node. i,j X i,j Let be the branch resistance and reactance from node i to node j, respectively; n(j) is the set of first-terminal nodes with node j as the last-terminal node; m(j) is the set of last-terminal nodes with node j as the first-terminal node. Let u be the squared voltage values ​​of node i and node j during time period t. base Δu is the squared value of the reference voltage, and Δu is the total voltage deviation of the system.

[0063] Based on the above model, the optimal power flow distribution of the power system can be obtained by adjusting behaviors such as power generation; thereby reducing the voltage deviation of the power system, improving power quality, and reducing operating costs while promoting the flow of energy rights.

[0064] To verify the effectiveness and rationality of the energy system optimization and control method based on energy elements proposed in this invention, a 10kV distribution network was selected for verification. Two comparison methods were set up to conduct a comparative analysis of the distribution network operation and user energy costs under the three methods.

[0065] Based on the actual load data of a certain 10kV distribution network, photovoltaic power of 1.2MW, 1.6MW and 1.3MW was installed at nodes 3, 7 and 12 respectively. Nodes 6 and 10 are energy-consuming entities formed by the aggregation of a large number of small users.

[0066] Design two comparative operating scenarios:

[0067] (1) Surplus electricity connected to the grid:

[0068] The electricity supply sources for energy-consuming entities are divided into grid-purchased electricity and self-generated photovoltaic power. Electricity trading is not allowed between energy-consuming entities, and the price of surplus electricity fed into the grid is 0.391 yuan / kWh.

[0069] (2) Surplus electricity fed into the grid + demand response scenario:

[0070] The electricity supply sources for energy-consuming entities are divided into grid-purchased electricity and self-generated photovoltaic power. Electricity trading is not allowed between energy-consuming entities, and the on-grid price for surplus electricity is 0.391 yuan / kWh. The power grid company conducts demand response interactions with some energy-consuming entities during two periods: 11:00-12:00 and 17:30-18:30, focusing on two types of demand response: renewable energy consumption and peak shaving. The renewable energy consumption subsidy is 0.6 yuan / kWh, and the peak shaving subsidy is 3 yuan / kWh.

[0071] The physical properties of the energy element in this case system are evaluated based on the energy element model. In this case, the physical property is the energy equivalent of the energy element. The evaluation results are shown in Figure 2.

[0072] The economic attributes of energy elements are evaluated. Taking the economic attribute evaluation results of photovoltaic energy elements during the period of 11:30-11:45 as an example, the lower limit of each price range in the table below is the unit cost price of the energy element seller, and the upper limit of the range is the unit energy efficiency equivalent price (i.e., the highest bid) of the energy element buyer.

[0073] Based on the above assessment results of the physical and economic attributes of energy elements, each energy user optimizes its own energy consumption behavior. After the system's energy consumption rights are matched, iterative optimization is carried out based on the energy consumption rights matching situation within the entire system. After the iteration is completed, the energy consumption rights circulation results are shown in the table below. The table illustrates the energy consumption rights circulation volume and circulation price among users at each node.

[0074] Based on the above results of energy consumption rights circulation, and by solving for the optimal power flow based on the distribution network parameters, the optimal operating condition of the distribution network system can be obtained. The distribution network operating conditions for the three scenarios are shown in the table below:

[0075] From the perspective of purchasing electricity from the upper-level power grid, the amount of electricity purchased by the upper-level power grid is inversely proportional to the photovoltaic absorption rate. Therefore, the amount of electricity purchased by the power grid is the least in the scenario of this invention. In the scenario of surplus electricity grid connection + demand response, the power grid has adjusted the energy consumption behavior to a certain extent by adopting demand response interaction, so the amount of electricity purchased by the power grid is also reduced to a certain extent compared with the scenario of surplus electricity grid connection.

[0076] From the perspective of grid profitability, since the grid cannot profit from the buying and selling price difference of photovoltaic power in this invention's scenario, and can only collect grid access fees, its profitability is significantly lower compared to the other two scenarios. In the surplus power to the grid + demand response scenario, grid profitability also decreases due to the higher demand response subsidy price.

[0077] From the perspective of the average voltage of the distribution network, in the scenario of this invention, photovoltaic prices are low at midday, incentivizing energy users to shift some of their load from the peak period of 17:00-21:00 to midday, thus reducing energy costs, lowering peak load on the distribution network, and to some extent improving the voltage level of the distribution network. In the scenario of surplus power fed into the grid + demand response, the grid's demand response behavior also achieves peak shaving and valley filling to some extent. However, since midday and evening are periods of high electricity prices, energy users lack the incentive to actively adjust their load, resulting in a slight increase in the average voltage level of the distribution network compared to the scenario of surplus power fed into the grid.

[0078] In summary, this invention enables accurate assessment of energy element attributes and reasonable pricing of energy use rights circulation, reduces user energy costs, fully explores and releases the potential of flexible resources on the user side, provides an effective way for a large number of widely distributed small-scale energy users to participate in market circulation, significantly improves resource liquidity, promotes efficient allocation and value circulation of user-side resources, and ultimately achieves green and low-carbon energy use.

Claims

1. A method for optimizing and controlling energy consumption systems based on energy elements, characterized in that, Includes the following steps: (1) Based on the physical and economic attributes of energy elements, construct a price model for the circulation of energy use rights based on energy elements and determine the price range in the circulation of energy elements; (2) Using the price range in the circulation of energy elements and the balance between energy supply and demand as constraints, and the minimum energy cost for users as the objective function, a user energy consumption optimization and control model based on energy element prices is constructed. (3) Solve the user energy consumption optimization and control model to obtain the optimal user energy consumption strategy under the minimum user energy consumption cost; (4) Establish a power system network flow model. Based on the optimal user energy consumption strategy and energy element flow status, and combined with power system network constraints, match the energy element supply and demand, determine the actual energy consumption demand of users, and solve the power system network flow model with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network. Step (1) is as follows: Obtain parameters of energy consumption systems, natural environment parameters, and social environment parameters; Based on the parameters of the energy system, natural environment, and social environment, an energy element model E is constructed, and the energy equivalent p of the energy element is determined. eqv ; Based on the energy equivalent p of the energy element eqv A price model for the circulation of energy use rights based on energy elements is constructed to determine the price range in the circulation of energy elements. Price range in energy element circulation Solve using the following formula: In the formula, r sell / r buy The price of energy provided by the energy provider / receiver, in yuan / kWh; Δw u,sell / Δw u,buy The change in energy efficiency after providing / receiving energy elements is expressed in elements; w c,sell / w c,buy The energy cost is expressed in yuan from the perspective of the energy provider / receiver; r is the lower bound of the price for the energy provider, in yuan. U represents the upper bound of the price for the energy receiver, expressed in yuan; U is the set of energy utility values ​​for the energy unit; C sell / C buy This is a set of energy expenditure costs from the perspective of the energy provider / receiver.

2. The energy system optimization and control method based on energy elements according to claim 1, characterized in that, The energy element model E is E={A,X,R} In the formula, A is the set of attributes of energy elements, including the set of physical attributes P and the set of economic attributes W; X is the set of influencing factors; (x1, x2, ..., x... n ) represents the influencing factors, namely the influencing factors of the energy system, energy form, natural environment and social environment, respectively, and n is the number of influencing factors; R is the set of mapping relationships between the attribute set A of energy element and the set of influencing factors X; F(X) is the mapping function from X to P; G(X) is the mapping function from X to W.

3. The energy system optimization and control method based on energy elements according to claim 2, characterized in that, Energy equivalent p of an energy element eqv for p eqv ∈P In the formula, p eqv H(X) represents the energy equivalent of an energy element, expressed in kWh; p represents the energy equivalent of an energy element. eqv The evaluation function; θ eqv,i S is the energy equivalent conversion function of heterogeneous energy source i; i Let I be the flow rate of heterogeneous energy i, in J; I be the set of heterogeneous energy contained in the energy element; and P be the set of physical properties.

4. The energy system optimization and control method based on energy elements according to claim 3, characterized in that, The set of physical properties P of energy elements is P = F(X) P = (p1, p2, ..., p m ) p m =f m (X) In the formula, (p1,p2,...,p m ) represents the physical properties of the energy element; m represents the number of physical properties; f m (·) represents the relationship between set X and physical property p. m The mapping.

5. The energy system optimization and control method based on energy elements according to claim 4, characterized in that, The set of economic attributes W is W = G(X,P) = G * (X) W=(w1,w2,...,w k ) w k =g k (x1,x2,...,x n p1, p2, ..., p m ) = g k * (x1,x2,...,x n ) In the formula, W is the set of economic attributes; (w1, w2, ..., w k ) represents the economic attribute of an energy element; k represents the quantity of the economic attribute; g k (·) represents the economic attribute w from X. k The mapping.

6. The energy system optimization and control method based on energy elements according to claim 1, characterized in that, In step (2), the user energy consumption optimization and control model, with the energy price range and energy supply and demand balance as constraints, and the minimum user energy cost as the objective function, is as follows: In the formula, C represents the user's energy cost, t is the control period number, T is the number of control periods, e is the energy element type number, E is the number of energy element types, and Δt is the control period length. The power purchased by the user from the grid during time period t. Let these represent the power received by energy element e and the power supplied by energy element e in the energy transfer process, respectively. The price per unit of energy that a user purchases from the grid during time period t. The price per unit of energy received by a user during the energy rights circulation within time period t. Provides the energy price per unit for the circulation of energy rights for users within time period t. For the user's energy demand during time period t, and Separate the user's fixed energy demand and available energy demand during time period t. This represents the maximum adjustable capacity of a user up / down within time period t.

7. The energy system optimization and control method based on energy elements according to claim 1, characterized in that, In step (3), mixed integer linear programming, intelligent algorithms and other methods are used to solve the user energy consumption optimization and control model to obtain the energy element flow and the corresponding optimal user energy consumption strategy.

8. The energy system optimization and control method based on energy elements according to claim 1, characterized in that, In step (4), the power system network power flow model is solved with the minimum voltage deviation as the objective function to obtain the optimal power flow distribution of the power system network: In the formula, P t,j Q t,j Inject the active and reactive power of node j during time period t. Let P be the active and reactive power generation at node j during time period t. t,j,l Q t,j,l Let t represent the active and reactive power of the branch from node j to node l at the starting node j during time period t. R represents the actual energy demand of user j at node. i,j X i,j Let be the branch resistance and reactance from node i to node j, respectively; n(j) is the set of first-terminal nodes with node j as the last-terminal node; m(j) is the set of last-terminal nodes with node j as the last-terminal node. Let u be the squared voltage values ​​of node i and node j during time period t. base Δu is the squared value of the reference voltage, and Δu is the total voltage deviation of the system.

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