Power distribution network benefit evaluation method and device considering multiple markets and considering demand response

Abstract

The invention discloses a power distribution network benefit evaluation method considering multiple markets and considering demand response, and the method comprises the steps: constructing a power distribution network benefit time sequence production simulation model considering multiple markets and demand response, and operating the model to obtain simulation data; constructing a three-dimensional evaluation index model to obtain three-dimensional evaluation index data considering multi-element market and demand response from the simulation data; performing comprehensive benefit scoring on the obtained three-dimensional evaluation index data according to a set scoring model; influence indexes of multiple markets, demand response, new energy consumption, load balance, carbon emission and the like on the benefits of the power distribution network are summarized into three-dimensional indexes of economic benefits, low-carbon benefits and social benefits, so that the benefits of the power distribution network are comprehensively synthesized; more valuable data can be provided for the influence of investment and construction such as new energy and energy storage of the power distribution network on the benefit, and decision-making help is provided for fine investment and participation in the carbon market and the green certificate market of power distribution network enterprises in the future.

Description

Technical Field

[0001] The present invention relates to the production and operation of a distribution network, and in particular to a distribution network benefit evaluation method and device that considers multiple markets and demand response. Background Art

[0002] The growing demand for energy has brought new environmental problems. Reducing carbon emissions and achieving clean energy development have become the common goals of the world today. In order to effectively control carbon emissions, my country has successively proposed a series of policies such as the carbon market and the green certificate market. In recent years, with the continuous development and maturity of the carbon market and the green certificate market, the connection between the carbon market and the green certificate market and the power market is becoming closer. At the same time, under the background of the development of new power systems, more and more distributed new energy sources are connected to the distribution network for local consumption. The distribution network is gradually transitioning to an active network with interactive supply and demand. The proportion of distributed generation (DG), electric vehicles (EV), and energy storage (ES) on the demand side continues to increase. With the continuous expansion of demand-side resources, the status of demand response (DR) has become increasingly important, and the load (flexible load) participating in demand response in a diversified market also affects the benefits of distribution network projects. Therefore, the traditional distribution network evaluation benefit assessment method can no longer meet the current development needs of clean, low-carbon, economical, efficient, flexible and flexible distribution networks. Summary of the invention

[0003] The purpose of the present invention is to solve the above-mentioned problems of the prior art and propose a distribution network benefit evaluation method and device that considers multiple markets and demand response. The specific scheme is as follows:

[0004] A distribution network benefit evaluation method considering multiple markets and demand response, wherein the multiple market power field includes a carbon market and a green certificate market, and the distribution network has at least a plurality of nodes including an energy storage power station, a new energy power station, an electric locomotive charging station and a load, including:

[0005] Construct a distribution network benefit time series production simulation model that considers multiple markets and demand response, and obtain simulation data through simulation operation of the distribution network benefit time series production simulation model;

[0006] Constructing a three-dimensional evaluation index model including the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, the carbon net ratio, the power generation penetration rate of new energy power stations and the power generation utilization rate as low-carbon benefit evaluation indicators, and the average load rate of the power grid, the peak power consumption gap and the valley power consumption surplus as social benefit evaluation indicators, so as to obtain three-dimensional evaluation index data considering multiple markets and demand effects from simulation data;

[0007] The obtained three-dimensional evaluation index data is scored for comprehensive benefits based on the set scoring model.

[0008] Furthermore, the distribution network benefit timing production simulation model includes a multi-market power field profit model, a power grid node profit model, an electricity balance model, and a comprehensive profit model that interacts with each profit model, electricity balance model and a three-dimensional evaluation index model to obtain the three-dimensional evaluation index data.

[0009] Furthermore, the multi-market power field revenue model includes a carbon market quota revenue model and a green certificate market quota revenue model, and the grid node revenue model includes an electric vehicle charging station revenue model, an energy storage power station revenue model, a new energy power station revenue model, and a load response revenue model for load participation in demand response, wherein:

[0010] The carbon market quota revenue model is expressed as:

[0011]

[0012] Where: A quo,t is the given carbon quota; σ is the carbon quota per unit of electricity; A real,t is the actual carbon emission; A ret,t Carbon emissions from repairing and decommissioning distribution network equipment; pgc,t is the carbon quota trading volume; S CET is the basic carbon price; C CET,t is the carbon emission cost or benefit, a value greater than 0 indicates cost, and a value less than 0 indicates benefit;

[0013] The green certificate market quota income model is expressed as:

[0014]

[0015] Where: γ1 and γ2 are the quantitative coefficient of photovoltaic power generation exchange for green certificates and the green certificate quota coefficient respectively; N real,t is the number of green certificates held by power grid enterprises, N quo,t is the specified quota; load,t is the load power consumption; N pgc,t is the green certificate trading volume; S TGC is the green certificate price; C TGC,t It is the cost or benefit of the green certificate. A value greater than 0 indicates benefit, and a value less than 0 indicates cost.

[0016] The electric vehicle charging station revenue model is expressed as:

[0017]

[0018] Where: P EVf,t , P EVg,tThey are the peak-time electricity sales of electric vehicle charging stations and the valley-time electricity sales of electric vehicle charging stations; S f,t , S g,t are peak electricity price and valley electricity price respectively; R EVs,t , R EVf,t , R EV,t are the electricity sales revenue, service revenue and total revenue of the electric vehicle charging station, respectively. α1, α2 and α3 are the electricity purchase price, coal carbon emission coefficient and fuel carbon emission coefficient of the electric vehicle charging station, respectively. C EVg,t , C EVy,t , C EVj,t , C EV,t They are the electricity purchase cost of electric vehicle charging stations, the operation and maintenance cost of electric vehicle charging stations, the construction cost of electric vehicle charging stations, and the total cost of electric vehicle charging stations. e , L y , L EV They are power consumption per unit mileage, fuel consumption per unit mileage, and average daily mileage of electric vehicles. ε1 is the proportion of clean energy. B EV,t , A EV,t They are the number of electric vehicle users and the carbon emission reduction contributed by the growth of electric vehicles, respectively. t represents the tth day;

[0019] The energy storage power station revenue model is expressed as:

[0020]

[0021]

[0022] Where: P ES,t0 , k1 are the initial value of energy storage capacity and growth rate respectively, P ES,t is the energy storage capacity on day t, β1 and β2 are the charge and discharge depth and charge and discharge efficiency respectively, R ES,t , C ESk,t , C ESy,t , C ES,t They are the total revenue of the energy storage power station, the expansion cost of the energy storage power station, the operation and maintenance cost of the energy storage power station, and the total cost of the energy storage power station;

[0023] The revenue model of new energy power station is expressed as:

[0024]

[0025]

[0026] Where: P PV,t0 , k2 are the initial value of the photovoltaic power station power generation and the growth rate of the photovoltaic power station power generation, P PV,t , P PVx,tare the photovoltaic power generation on the tth day and the photovoltaic consumption on the tth day, ε2 is the proportion of photovoltaic power, S PV is the photovoltaic grid-connected electricity price, R PV,t , C PVk,t , C PVy,t , C PV,t They are the total revenue of photovoltaic power stations, the expansion cost of photovoltaic power stations, the operation and maintenance cost of photovoltaic power stations, and the total cost of photovoltaic power stations. is the carbon emission factor, A PV,t Carbon emission reduction for photovoltaic power stations;

[0027] The load response benefit model is expressed as:

[0028]

[0029] Where: ΔP fq,t is the electricity consumption gap before the peak of demand response, ΔP gy,t are the power surplus in the pre-valley period of demand response, λ gf,t , gg,t They are the peak time dispatching command for industrial and commercial users and the valley time dispatching command for industrial and commercial users, respectively. EVf,t , EVg,t , EV,t They are the peak-time dispatching command for electric vehicles, the valley-time dispatching command for electric vehicles, and the activeness of electric vehicle response, ΔP g,t , ΔP EV,t are the electricity transferred by industrial and commercial users and electric vehicle users, respectively. λ is the demand response compensation price. C DR,t is the demand response dispatch cost, P ES,t is the energy storage capacity on day t; A DR,t is the carbon emission reduction in response to demand, ΔP' fq,t , ΔP' gy,t They are the peak electricity gap and valley electricity surplus after demand response, respectively.

[0030] Furthermore, the power balance model is expressed as:

[0031]

[0032] Where: δ1 and δ2 are the peak power supply coefficient and the valley power supply coefficient respectively, P fg,t , P gg,t They are the peak power supply of the distribution network and the valley power supply of the distribution network, P gfy,t , P ggy,t They are the peak power consumption of industrial and commercial users, the peak and valley power consumption of industrial and commercial users, P jfy,t , P jgy,t They are the peak electricity consumption of residential users and the valley electricity consumption of residential users, P EVfy,t , PEVgy,t They are the peak power consumption of electric vehicle users and the valley power consumption of electric vehicle users, P fy,t , P gy,t They are the peak and valley power consumption of the distribution network, P RES,t is the energy storage capacity on day t; P PV,t is the photovoltaic power generation on day t, P DG,t is the DG power generation, P CT,t For traditional energy purchases, P ESfg,t is the peak discharge amount of energy storage, P ESgy,t is the charge amount during the energy storage valley, ΔP fq,t is the peak electricity consumption gap, ΔP fy,t , ΔP gy,t They are the surplus electricity consumption during peak hours and off-peak hours respectively.

[0033] Furthermore, the three-dimensional evaluation index model includes an economic benefit model, a low-carbon benefit model and a social benefit model, wherein

[0034] The economic benefit model is expressed as:

[0035]

[0036] Where: S j is the residential electricity price; R s,t , R h,t , R t They are the electricity sales revenue of the distribution network project, the revenue from easing the construction of transmission and distribution, and the total revenue; S CT , S RES are the electricity purchase prices of traditional energy and renewable energy respectively; μ1, μ2, and μ3 are the grid-connection fee per unit of electricity, line loss rate, and reliability rate respectively; C g,t , C w,t , C e,t , C t are the power purchase cost, network loss cost, power outage cost and total cost of the distribution network project respectively; pro,t is the net economic benefit; r ret,t is the input-output ratio.

[0037] The low-carbon benefit model is expressed as:

[0038]

[0039] Where: P' load,t is the load power consumption of the distribution network after demand response; r car,t is the carbon net ratio indicator; r abs,t is the photovoltaic power generation penetration index, r uti,t is the photovoltaic power generation utilization rate.

[0040] The social benefit model is expressed as:

[0041]

[0042]

[0043] Where: r load,t is the average load rate; P' py,t , P' dy,t are the average load and maximum load after demand response; ΔP' fq,t , ΔP' gy,t They are the peak electricity gap and valley electricity surplus after demand response, respectively.

[0044] 1. The distribution network benefit evaluation method considering multiple markets and demand response according to claim 1 is characterized in that: the scoring model is expressed as follows:

[0045]

[0046] Where: V is the comprehensive benefit score, j is the dimension; v j and w j They are the initial score of dimension j and the weight of dimension j respectively.

[0047] The present invention also provides a distribution network benefit evaluation device that considers multiple markets and demand response, including:

[0048] The time-series production simulation module is used to perform simulation runs that consider multiple markets and demand response to obtain simulation data;

[0049] A three-dimensional evaluation index module is used to obtain three-dimensional evaluation index data considering multiple markets and demand effects from simulation data, including the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, the net carbon ratio, the power generation penetration rate of new energy power stations and the power generation utilization rate as low-carbon benefit evaluation indicators, and the average load rate of the power grid, the peak power consumption gap and the valley power consumption surplus as social benefit evaluation indicators;

[0050] The dynamic evaluation module performs comprehensive benefit scoring on the obtained three-dimensional evaluation index data based on the set scoring model.

[0051] The present invention further provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute the method of the present invention.

[0052] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and is characterized in that the computer program implements the steps of the method of the present invention when executed by a processor.

[0053] The solution provided by the present invention has the following beneficial effects:

[0054] The present invention sets three-dimensional evaluation indicators by taking demand response into account and considering the benefits that multiple markets can provide, which can effectively integrate the impact of economy, low carbon and society on the benefits of distribution networks, and dynamically run the simulation through the distribution network benefit time series production simulation model to obtain three-dimensional evaluation indicator data, which can be used to conduct a comprehensive and integrated evaluation of the impact of multiple indicators on distribution network benefits from multiple markets, demand response, new energy consumption, load balance, and flexible loads that facilitate users to participate in demand response. It can provide more valuable data on the impact of new energy construction on the benefits of distribution networks, and provide decision-making assistance for future distribution network companies to refine their investment and how to participate in the carbon market and green certificate market. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a schematic diagram of a process according to Example 5 of the method of the present invention;

[0056] Figure 2 It is a simulation result diagram of the net income index of the method;

[0057] Figure 3 This is a simulation result diagram of the input-output ratio index of the method described in Example 1;

[0058] Figure 4 This is a simulation result diagram of the carbon net ratio index of the method described in Example 1;

[0059] Figure 5 This is a simulation result diagram of the photovoltaic power generation penetration index according to the method described in Example 1;

[0060] Figure 6 This is a simulation result diagram of photovoltaic power generation utilization index according to the method described in Example 1;

[0061] Figure 7 This is a simulation result diagram of the average load rate index of the method described in Example 1;

[0062] Figure 8 This is a simulation result diagram of the peak power consumption gap index of the method described in Example 1;

[0063] Fig. 9 This is a diagram showing the simulation results of the valley electricity consumption gap index according to the method described in Example 1. DETAILED DESCRIPTION

[0064] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0065] Example 1

[0066] A distribution network benefit time-series production simulation model driven by system dynamics is constructed. Taking into account multiple markets and demand response, the distribution network benefit time-series production simulation model includes multiple market power field revenue models, power grid node revenue models, electricity balance models, and a comprehensive revenue model that interacts with each revenue model, electricity balance model and three-dimensional evaluation index model to obtain the three-dimensional evaluation index data, so as to obtain simulation data through the simulation operation of the distribution network benefit time-series production simulation model.

[0067] A three-dimensional evaluation index model is constructed, which includes taking the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, taking the carbon net ratio, power generation penetration rate of new energy power stations and power generation utilization rate as low-carbon benefit evaluation indicators, and taking the average load rate of the power grid, peak power gap and valley power surplus as social benefit evaluation indicators, so as to obtain three-dimensional evaluation index data from simulation data.

[0068] The obtained three-dimensional evaluation index data is scored for comprehensive benefits based on the set scoring model.

[0069] Example 2

[0070] This embodiment is further designed on the basis of embodiment 1 in that the multi-market power field revenue model in this embodiment includes a carbon market quota revenue model and a green certificate market quota revenue model, and the grid node revenue model includes an electric vehicle charging station revenue model, an energy storage power station revenue model, a new energy power station revenue model, and a load response revenue model for load participation in demand response. Among them:

[0071] The revenue model of electric vehicle charging stations is expressed as:

[0072]

[0073] Where: P EVf,t , P EVg,t They are the peak-time electricity sales of electric vehicle charging stations and the valley-time electricity sales of electric vehicle charging stations; S f,t , S g,t are peak and valley electricity prices respectively; R EVs,t , R EVf,t , R EV,t are the electricity sales revenue, service revenue and total revenue of the electric vehicle charging station, respectively. α1, α2 and α3 are the electricity purchase price, coal carbon emission coefficient and fuel carbon emission coefficient of the electric vehicle charging station, respectively. EVg,t , C EVy,t , C EVj,t , C EV,t They are the electricity purchase cost of electric vehicle charging stations, the operation and maintenance cost of electric vehicle charging stations, the construction cost of electric vehicle charging stations, and the total cost of electric vehicle charging stations.e , L y , L EV They are power consumption per unit mileage, fuel consumption per unit mileage, and average daily mileage of electric vehicles. ε1 is the proportion of clean energy. B EV,t , A EV,t They are the number of electric vehicle users and the carbon emission reduction contributed by the growth of electric vehicles, and t is the number of days.

[0074] The revenue model of energy storage power station is expressed as:

[0075]

[0076]

[0077] Where: P ES,t0 , k1 are the initial value of energy storage capacity and growth rate respectively, P ES,t is the energy storage capacity on day t, β1 and β2 are the charge and discharge depth and charge and discharge efficiency respectively, R ES,t , C ESk,t , C ESy,t , C ES,t They are the total revenue of the energy storage power station, the expansion cost of the energy storage power station, the operation and maintenance cost of the energy storage power station, and the total cost of the energy storage power station.

[0078] The revenue model of new energy power station is expressed as:

[0079]

[0080]

[0081] Where: P PV,t0 , k2 are the initial value of the photovoltaic power station power generation and the growth rate of the photovoltaic power station power generation, P PV,t , P PVx,t are the photovoltaic power generation on the tth day and the photovoltaic consumption on the tth day, ε2 is the proportion of photovoltaic power, S PV is the photovoltaic grid-connected electricity price, R PV,t , C PVk,t , C PVy,t , C PV,t They are the total revenue of photovoltaic power stations, the expansion cost of photovoltaic power stations, the operation and maintenance cost of photovoltaic power stations, and the total cost of photovoltaic power stations. is the carbon emission factor, A PV,t The carbon emission reduction of photovoltaic power stations.

[0082] The load response benefit model is expressed as:

[0083]

[0084] Where: ΔP fq,tis the electricity consumption gap before the peak of demand response, ΔP gy,t are the power surplus in the pre-valley period of demand response, λ gf,t , gg,t They are the peak time dispatching command for industrial and commercial users and the valley time dispatching command for industrial and commercial users, respectively. EVf,t , EVg,t , EV,t They are the peak-time dispatching command for electric vehicles, the valley-time dispatching command for electric vehicles, and the activeness of electric vehicle response, ΔP g,t , ΔP EV,t are the electricity transferred by industrial and commercial users and electric vehicle users, respectively. λ is the demand response compensation price. C DR,t is the demand response dispatch cost, P ES,t is the energy storage capacity on day t; A DR,t is the carbon emission reduction in response to demand, ΔP' fq,t They are the peak electricity gap and valley electricity surplus after demand response, respectively.

[0085] The carbon market quota revenue model is expressed as:

[0086]

[0087] Where: σ is the carbon quota per unit of electricity; A ret,t Carbon emissions from repairing and decommissioning distribution network equipment; pgc,t is the carbon quota trading volume; S CET is the basic carbon price; C CET,t is the carbon emission cost or benefit. A value greater than 0 indicates a cost, and a value less than 0 indicates a benefit.

[0088] The green certificate market quota income model is expressed as:

[0089]

[0090] Where: γ1 and γ2 are the quantitative coefficient of photovoltaic power generation exchange for green certificates and the green certificate quota coefficient respectively; P load,t is the load power consumption; N pgc,t is the green certificate trading volume; S TGC is the green certificate price; C TGC,t It is the cost or benefit of the green certificate. A value greater than 0 indicates benefit, and a value less than 0 indicates cost.

[0091] Example 3

[0092] This embodiment is a further design based on the embodiment 2, in which the power balance model in this embodiment adopts the following expression:

[0093]

[0094] Where: δ1 and δ2 are the peak power supply coefficient and the valley power supply coefficient respectively, P fg,t , P gg,t They are the peak power supply of the distribution network and the valley power supply of the distribution network, P gfy,t , P ggy,t They are the peak power consumption of industrial and commercial users, the peak and valley power consumption of industrial and commercial users, P jfy,t , P jgy,t They are the peak electricity consumption of residential users and the valley electricity consumption of residential users, P EVfy,t , P EVgy,t They are the peak power consumption of electric vehicle users and the valley power consumption of electric vehicle users, P fy,t , P gy,t They are the peak and valley power consumption of the distribution network, P RES,t is the energy storage capacity on day t; P PV,t is the photovoltaic power generation on day t, P DG,t is the DG power generation, P CT,t For traditional energy purchases, P ESfg,t is the peak discharge amount of energy storage, P ESgy,t is the charge amount during the energy storage valley, ΔP fq,t is the peak electricity consumption gap, ΔP fy,t , ΔP gy,t They are the surplus electricity consumption during peak hours and off-peak hours respectively.

[0095] Example 4

[0096] This embodiment is based on embodiment 3. The models of embodiments 2 and 3 are simulated and run under the drive of the power system software to obtain corresponding simulation data. The further design of this embodiment is to set a three-dimensional evaluation index model to obtain three-dimensional evaluation index data that considers multiple markets and demand responses from the acquired simulation data through the three-dimensional evaluation index model. The three-dimensional evaluation index data includes the following three dimensions: economic benefits, low-carbon benefits, and social benefits. The indicators corresponding to each dimension are as follows:

[0097] Economic benefit evaluation indicators: net income of distribution network, input-output ratio of distribution network;

[0098] Low-carbon benefit evaluation indicators: net carbon ratio of the distribution network, power penetration rate and power utilization rate of new energy power stations; social benefit evaluation indicators: average load rate of the distribution network, peak power gap and valley power surplus of the distribution network.

[0099] The three-dimensional evaluation index model includes economic benefit model, low-carbon benefit model and social benefit model.

[0100] The economic benefit model is expressed as:

[0101]

[0102] Where: S j is the residential electricity price; R s,t , R h,t , R t They are the electricity sales revenue of the distribution network project, the revenue from easing the construction of transmission and distribution, and the total revenue; S CT , S RES are the electricity purchase prices of traditional energy and renewable energy respectively; μ1, μ2, and μ3 are the grid-connection fee per unit of electricity, line loss rate, and reliability rate respectively; C g,t , C w,t , C e,t , C t are the power purchase cost, network loss cost, power outage cost and total cost of the distribution network project respectively; pro,t is the net economic benefit; r ret,t is the input-output ratio.

[0103] Low-carbon benefit model. Taking into account factors such as carbon net ratio, power generation penetration rate and photovoltaic power generation utilization rate, it is expressed as:

[0104]

[0105] Where: P' load,t is the load power consumption of the distribution network after demand response; r car,t is the carbon net ratio indicator; r abs,t is the photovoltaic power generation penetration index, r uti,t is the photovoltaic power generation utilization rate.

[0106] The social benefit model takes into account the average load rate r load,t , peak power shortage and valley power surplus factors, which are expressed as:

[0107]

[0108] Where: r load,t is the average load rate; P' py,t , P' dy,t are the average load and maximum load after demand response, respectively.

[0109] Example 5

[0110] This embodiment is a further design based on Embodiment 4, and the scoring model set in this embodiment is as follows:

[0111]

[0112] Where: V is the comprehensive benefit score, j is the dimension; v j and w j They are the initial score of dimension j and the weight of dimension j respectively.

[0113] This embodiment provides the scoring criteria shown in Table 1 below, setting 5 scores, and each dimension of the three-dimensional indicators corresponds to multiple indicators, and the adaptation range is set according to the above scores. The evaluation score of the corresponding dimension can be obtained by comparing the acquired three-dimensional evaluation indicator data with the adaptation range, and the comprehensive benefit score of the distribution network node is obtained by weighted processing according to the above scoring model.

[0114] Table 1

[0115]

[0116] Example 6

[0117] This embodiment provides a distribution network benefit evaluation device of the present invention that considers multiple markets and takes demand response into account, including:

[0118] The time-series production simulation module is used to perform simulation runs that consider multiple markets and demand response to obtain simulation data;

[0119] A three-dimensional evaluation index module is used to obtain three-dimensional evaluation index data considering multiple markets and demand effects from simulation data, including the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, the net carbon ratio, the power generation penetration rate of new energy power stations and the power generation utilization rate as low-carbon benefit evaluation indicators, and the average load rate of the power grid, the peak power consumption gap and the valley power consumption surplus as social benefit evaluation indicators;

[0120] The dynamic evaluation module performs comprehensive benefit scoring on the obtained three-dimensional evaluation index data based on the set scoring model.

[0121] Application Examples

[0122] The timing simulation model of the present invention is carried out in the following four different scenarios with "day" as the time unit for 365 days of dynamic simulation operation, and the simulation data of 8 indicators (second level) corresponding to the three-dimensional (first level) evaluation indicators are obtained, such as Figure 2-9 .

[0123] Scenario 1: No participation in the diversified market and no demand response;

[0124] Scenario 2: Participate in multiple markets only;

[0125] Scenario 3: Demand response only;

[0126] Scenario 4 (typical scenario): Participating in multiple markets and implementing demand response.

[0127] The simulation of the medium-term distribution network involves a series of investments in photovoltaic power plants, energy storage plants and electric vehicle charging stations.

[0128] In terms of economic benefits, it can be seen that implementing demand response while participating in multiple markets can also obtain relatively high net benefits and input-output ratios; in terms of carbon net ratio, it can be seen that implementing demand response while participating in multiple markets can significantly reduce the carbon net ratio; in terms of photovoltaic power generation penetration rate, participating in multiple markets can increase photovoltaic power generation penetration rate, while implementing demand response will reduce the value of this indicator; photovoltaic power generation utilization rate is similar to the average load rate, and implementing demand response can increase photovoltaic power generation utilization rate and average load rate; in terms of peak electricity consumption gap, it can be seen that implementing demand response can significantly reduce peak electricity consumption gap and valley electricity surplus.

Claims

1. A distribution network benefit evaluation method considering multiple markets and demand response, wherein the multiple market power market includes a power market, a carbon market and a green certificate market, and the distribution network has at least a plurality of nodes including an energy storage power station, a new energy power station, an electric vehicle charging station and a load, characterized in that: Construct a distribution network benefit time series production simulation model that considers multiple markets and demand response, and obtain simulation data through simulation operation of the distribution network benefit time series production simulation model; Constructing a three-dimensional evaluation index model including the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, the carbon net ratio, the power generation penetration rate of new energy power stations and the power generation utilization rate as low-carbon benefit evaluation indicators, and the average load rate of the power grid, the peak power consumption gap and the valley power consumption surplus as social benefit evaluation indicators, so as to obtain three-dimensional evaluation index data considering multiple markets and demand effects from simulation data; The obtained three-dimensional evaluation index data is scored for comprehensive benefits based on the set scoring model.

2. The method for evaluating the benefits of a distribution network taking into account multiple markets and demand response according to claim 1, characterized in that: The distribution network benefit sequential production simulation model includes a multi-market revenue model, a grid node revenue model, an electricity balance model, and a comprehensive revenue model that interacts with each revenue model, electricity balance model and a three-dimensional evaluation index model to obtain the three-dimensional evaluation index data.

3. The method for evaluating the benefits of a distribution network taking into account multiple markets and demand response according to claim 2, characterized in that: The multi-market revenue model includes a carbon market quota revenue model and a green certificate market quota revenue model. The grid node revenue model includes an electric vehicle charging station revenue model, an energy storage power station revenue model, a new energy power station revenue model, and a load response revenue model for load participation in demand response, wherein: The carbon market quota revenue model is expressed as: Where: A quo,t is the given carbon quota; σ is the carbon quota per unit of electricity; P CT,t Purchase electricity for traditional energy sources; is the carbon emission factor; A real,t is the actual carbon emission; A ret,t Carbon emissions from repairing and decommissioning distribution network equipment; DR,t A is the amount of carbon reduction in response to demand; PV,t A is the carbon emission reduction of photovoltaic power station; EV,t are the carbon emission reductions contributed by EV growth; A pgc,t is the carbon quota trading volume; S CET is the basic carbon price; C CET,t is the carbon emission cost or benefit, a value greater than 0 indicates cost, and a value less than 0 indicates benefit; The green certificate market quota income model is expressed as: Where: γ1 and γ2 are the quantitative coefficient of photovoltaic power generation exchange for green certificates and the green certificate quota coefficient respectively; P PV,t is the photovoltaic power generation on day t; P load,t is the load power consumption; N real,t is the number of green certificates held by power grid enterprises, N quo,t is the specified quota; N pgc,t is the green certificate trading volume; S TGC is the green certificate price; C TGC,t It is the cost or benefit of green certificate. Its value greater than 0 indicates benefit, and less than 0 indicates cost. The electric vehicle charging station revenue model is expressed as: Where: P EVf,t , P EVg,t They are the peak-time electricity sales of electric vehicle charging stations and the valley-time electricity sales of electric vehicle charging stations; S f,t , S g,t are peak electricity price and valley electricity price respectively; R EVs,t , R EVf,t , R EV,t are the electricity sales revenue, service revenue and total revenue of the electric vehicle charging station, respectively. α1, α2 and α3 are the electricity purchase price, coal carbon emission coefficient and fuel carbon emission coefficient of the electric vehicle charging station, respectively. C EVg,t , C EVy,t , C EVj,t , C EV,t They are the electricity purchase cost of electric vehicle charging stations, the operation and maintenance cost of electric vehicle charging stations, the construction cost of electric vehicle charging stations, and the total cost of electric vehicle charging stations. e , L y , L EV They are power consumption per unit mileage, fuel consumption per unit mileage, and average daily mileage of electric vehicles. ε1 is the proportion of clean energy. B EV,t , A EV,t They are the number of electric vehicle users and the carbon emission reduction contributed by the growth of electric vehicles, and t represents the tth day; The energy storage power station revenue model is expressed as: Where: P ES,t0 , k1 are the initial value of energy storage capacity and growth rate respectively, P ES,t is the energy storage capacity on day t, β1 and β2 are the charge and discharge depth and charge and discharge efficiency respectively, R ES,t , C ESk,t , C ESy,t , C ES,t They are the total revenue of the energy storage power station, the expansion cost of the energy storage power station, the operation and maintenance cost of the energy storage power station, and the total cost of the energy storage power station; The revenue model of new energy power station is expressed as: Where: P PV,t0 , k2 are the initial value of the photovoltaic power station power generation and the growth rate of the photovoltaic power station power generation, P PV,t , P PVx,t are the photovoltaic power generation on the tth day and the photovoltaic consumption on the tth day, ε2 is the proportion of photovoltaic power, S PV is the photovoltaic grid-connected electricity price, R PV,t , C PVk,t , C PVy,t , C PV,t They are the total revenue of photovoltaic power stations, the expansion cost of photovoltaic power stations, the operation and maintenance cost of photovoltaic power stations, and the total cost of photovoltaic power stations. is the carbon emission factor, A PV,t Carbon emission reduction for photovoltaic power stations; The load response benefit model is expressed as: Where: ΔP fq,t is the electricity consumption gap before the peak of demand response, ΔP gy,t are the power surplus in the pre-valley period of demand response, λ gf,t , gg,t They are the peak time dispatching command for industrial and commercial users and the valley time dispatching command for industrial and commercial users, respectively. EVf,t , EVg,t , EV,t They are the peak-time dispatching command for electric vehicles, the valley-time dispatching command for electric vehicles, and the activeness of electric vehicle response, ΔP g,t , ΔP EV,t are the electricity transferred by industrial and commercial users and electric vehicle users, respectively. λ is the demand response compensation price. C DR,t is the demand response dispatch cost, P ES,t is the energy storage capacity on day t; A DR,t is the carbon emission reduction in response to demand, ΔP' fq,t They are the peak electricity gap and valley electricity surplus after demand response, respectively.

4. The method for evaluating the benefits of a distribution network taking into account multiple markets and demand response according to claim 2, characterized in that: The power balance model is expressed as: Where: δ1 and δ2 are the peak power supply coefficient and the valley power supply coefficient respectively, P fg,t , P gg,t They are the peak power supply of the distribution network and the valley power supply of the distribution network, P gfy,t , P ggy,t They are the peak power consumption of industrial and commercial users, the peak and valley power consumption of industrial and commercial users, P jfy,t , P jgy,t They are the peak electricity consumption of residential users and the valley electricity consumption of residential users, P EVfy,t , P EVgy,t They are the peak power consumption of electric vehicle users and the valley power consumption of electric vehicle users, P fy,t , P gy,t They are the peak and valley power consumption of the distribution network, P RES,t is the energy storage capacity on day t; P PV,t is the photovoltaic power generation on day t, P DG,t is the DG power generation, P CT,t For traditional energy purchases, P ESfg,t is the peak discharge amount of energy storage, P ESgy,t is the charge amount during the energy storage valley, ΔP fq,t is the peak electricity consumption gap, ΔP fy,t , ΔP gy,t They are the surplus electricity consumption during peak hours and off-peak hours respectively.

5. The method for evaluating the benefits of a distribution network taking into account multiple markets and demand response according to claim 1, characterized in that: The three-dimensional evaluation index model includes an economic benefit model, a low-carbon benefit model and a social benefit model. The economic benefit model is expressed as: Where: S j is the residential electricity price; R s,t , R h,t , R t They are the electricity sales revenue of the distribution network project, the revenue from easing the construction of transmission and distribution, and the total revenue; S CT , S RES are the electricity purchase prices of traditional energy and renewable energy respectively; μ1, μ2, and μ3 are the grid-connection fee per unit of electricity, line loss rate, and reliability rate respectively; C g,t , C w,t , C e,t , C t are the power purchase cost, network loss cost, power outage cost and total cost of the distribution network project; pro,t is the net economic benefit; r ret,t is the input-output ratio; The low-carbon benefit model is expressed as: Where: P' load,t is the load power consumption of the distribution network after demand response; car,t is the carbon net ratio indicator; r abs,t is the photovoltaic power generation penetration index, r uti,t is the photovoltaic power generation utilization rate; The social benefit model is expressed as: Where: r load,t is the average load rate; P' py,t , P' dy,t are the average load and maximum load after demand response, respectively.

6. The method for evaluating the benefits of a distribution network taking into account multiple markets and demand response according to claim 1, characterized in that: The scoring model is expressed as follows: Where: V is the comprehensive benefit score, j is the dimension; v j and w j They are the initial score of dimension j and the weight of dimension j respectively.

7. A distribution network benefit evaluation device considering multiple markets and demand response, characterized in that: include: The time-series production simulation module is used to perform simulation runs that consider multiple markets and demand response to obtain simulation data; A three-dimensional evaluation index module is used to obtain three-dimensional evaluation index data considering multiple markets and demand effects from simulation data, including the net income and input-output ratio of the distribution network as economic benefit evaluation indicators, the net carbon ratio, the power generation penetration rate of new energy power stations and the power generation utilization rate as low-carbon benefit evaluation indicators, and the average load rate of the power grid, the peak power consumption gap and the valley power consumption surplus as social benefit evaluation indicators; The dynamic evaluation module performs comprehensive benefit scoring on the obtained three-dimensional evaluation index data based on the set scoring model.

8. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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