New end-to-end demand response method and system for power distribution system

Abstract

The new end-to-end demand response method and system for distribution systems takes the minimization of the total cost of operation transactions, planning investment and risk of a single market entity as the objective function; the operation transaction model of the market entity is composed of the objective function and the end-to-end demand response transaction constraints of the market entity; the constraints of the operation transaction model of the market entity are constructed according to distributed resource planning and operation data; based on the operation transaction model of the market entity that meets the constraints, the second-order cone relaxation term of the power flow constraint is obtained; the augmented function of the objective function is established by utilizing the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity, solve the problem of constructing the distributed resource planning model, consider the significant difference between the overall decision-making of the new distribution system and the end-to-end demand response, and realize the maximization of benefits under regional energy sharing.

Description

Technical Field

[0001] The present invention belongs to the technical field of novel power system demand response, and in particular relates to a novel end-to-end demand response method and system for a power distribution system taking into account distributed resource planning. Background Art

[0002] The vigorous development of new energy sources and the construction of a new power system are driving a continuous transformation of the energy structure. This new power system features fundamental characteristics: clean and low-carbon, safe and controllable, flexible and efficient, intelligent and user-friendly, and open and interactive. Both centralized development and distributed access to new energy are key forms of achieving high-proportion new energy grid integration. In recent years, with the advancement of information technology, end-to-end demand response has been gradually established and demonstrated in practical engineering applications.

[0003] Existing research on end-to-end demand response encompasses market mechanisms, transaction and operational strategies, and implementation technologies. It has been proposed that end-to-end demand response can promote regional power balance, enhance renewable energy consumption, and reduce electricity costs. However, as a new market mechanism, end-to-end demand response will also have a significant impact on the planning and decision-making of distributed resources. This is especially true given that distributed resources are modular and have shorter investment and construction cycles. Therefore, the impact of market models and pricing mechanisms will be reflected more quickly than with centralized power sources.

[0004] The development of distributed renewable energy has enabled a large number of market participants in new distribution systems to possess power generation capabilities, characterized by small individual capacity, high flexibility, and responsiveness to the market. Consequently, distribution networks are characterized by a diversification of power generation participants and complex market behavior. To adapt to the development of new power systems and electricity markets, the planning of distributed renewable energy must adapt to the requirements of new market mechanisms within distribution networks. However, current distributed resource planning methods mostly focus on the overall perspective of the distribution network, with less attention paid to the impact of the interactive behavior of multiple participants in the new distribution system under end-to-end demand response. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a new end-to-end demand response method and system for a power distribution system taking into account distributed resource planning, which solves the problem of model construction for distributed resource planning of the new power distribution system, considers the significant differences between the overall decision-making of the new power distribution system and the end-to-end demand response method, and realizes the maximization of operation and investment benefits under regional energy sharing.

[0006] The present invention adopts the following technical solutions.

[0007] The present invention proposes a novel end-to-end demand response method for a power distribution system. The novel power distribution system includes multiple market entities, including:

[0008] Taking the minimization of the total cost of operation, transaction, planning investment and risk of a single market entity as the objective function; establishing the end-to-end demand response transaction constraints of the market entity; and constructing the operation and transaction model of the market entity with the objective function and the end-to-end demand response transaction constraints;

[0009] Based on distributed resource planning and operation data, the constraints of the operation and transaction model of market entities are constructed;

[0010] Based on the operation and transaction model of market entities that meet the constraints, the second-order cone relaxation term of the power flow constraint is obtained; the augmented function of the objective function is established by using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

[0011] The objective function satisfies the following relationship:

[0012]

[0013] Where, For market players The objective function, For market players The total cost of running transactions, planning investments and taking risks, For the scene Lower market entities The transaction costs of running For market players The planned investment cost, For market players The risk cost, is the proportional coefficient representing the risk preference of market entities, For the scene The probability of , is the total number of scenes;

[0014] Scenario Lower market entities The operating transaction cost satisfies the following relationship:

[0015]

[0016] Where, For the scene Lower market entities end-to-end demand response costs, For the scene Lower market entities The transmission cost of end-to-end demand response, For the scene Lower market entities electricity transaction costs with distribution network operators;

[0017] Market players Planned investment cost Including market players Cost expenditure and market players of battery energy storage systems Planning investment costs for distributed photovoltaics;

[0018] Market players The cost of risk satisfies the following relationship:

[0019]

[0020]

[0021] Where, is the risk value, is the confidence level, For computing scenarios An auxiliary variable for value at risk, which is a positive number.

[0022] Scenario Lower market entities The end-to-end demand response cost satisfies the following relationship:

[0023]

[0024] Where, For the scene Lower market entities In the period With market players End-to-end demand response price between For market players A collection of market entities with an end-to-end demand response relationship, For the scene Lower market entities In the period With market players End-to-end demand response volume between

[0025] Scenario Lower market entities The transmission fee cost of the end-to-end demand response satisfies the following relationship:

[0026]

[0027]

[0028] Where, For market players With market players End-to-end demand response transmission fee between It is the transmission fee per unit of electricity passing through a unit of electrical distance. For market players With market players The electrical distance between , , For market players Corresponding nodes gather, , For market players Corresponding nodes gather, For nodes and nodes The line impedance between

[0029] Scenario Lower market entities The electricity transaction cost with the distribution network operator satisfies the following relationship:

[0030]

[0031] Where, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, 、 They are market entities in the time period The purchase and sale prices of electricity in power transactions with distribution network operators, For the scene Lower market entities In the period The character state quantity, when Time scene Lower market entities In the period When purchasing electricity from distribution network operators, Time scene Lower market entities In the period Selling electricity from distribution network operators.

[0032] Market players The cost expenditure of the battery energy storage system satisfies the following relationship:

[0033]

[0034] Where, For market players The cost of battery energy storage system, For market players The planned investment cost of the battery energy storage system, For market players Operation and maintenance costs of the battery energy storage system;

[0035]

[0036] Where, is the annual value coefficient of the battery energy storage system, For market players The installed capacity of the battery energy storage system, For market players The maximum charge and discharge power of the battery energy storage system, is the unit price of capacity, is the unit price of power;

[0037]

[0038] Where, is the discount rate, is the average service life of the battery energy storage system, is the average service life of PV in years;

[0039]

[0040] Where, is the operation and maintenance rate of the battery energy storage system;

[0041] Market players The planned investment cost of distributed photovoltaics satisfies the following relationship:

[0042]

[0043] Where, For market players The planned investment cost of distributed photovoltaics, is the unit power generation capacity cost of distributed photovoltaics, is the annual value coefficient of distributed photovoltaics;

[0044]

[0045] Where, is the discount rate, is the average service life of distributed photovoltaics.

[0046] End-to-end demand response transaction constraints for market entities, including:

[0047] Scenario Next, market players The total amount of market players participating in end-to-end electricity demand response is equal to The sum of the end-to-end demand response volume with other market players satisfies the following relationship:

[0048]

[0049] Where, For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period With market players The end-to-end demand response between Market players For electricity buyers and market players For electricity sellers, For market players A collection of market entities that have an end-to-end demand response relationship;

[0050] The following constraints exist between buyers and sellers of the same end-to-end electricity demand response:

[0051]

[0052] Where, For the scene Lower market entities In the period With market players The end-to-end demand response volume between For the scene Lower market entities In the period With market players The end-to-end demand response volume between

[0053] The constraints of the market entity's operating transaction model include: the market entity power balance constraint, which satisfies the following relationship:

[0054]

[0055] Where, For the scene Lower market entities In the period The power generation of distributed photovoltaic For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, For the scene Lower market entities In the period The power of the electrical load, For the scene Lower market entities In the period The energy storage charging and discharging power, when When energy storage charging, Time storage energy discharge.

[0056] The constraints of the operational transaction model of market entities include: battery energy storage system planning and operation constraints;

[0057] Battery energy storage system planning and operation constraints include: market entities There is an upper limit constraint on the installed capacity of the battery energy storage system that satisfies the following relationship:

[0058]

[0059] Where, For market players The installed capacity of the battery energy storage system, For market players An upper limit on the installed capacity of battery energy storage systems;

[0060] Scenario Lower market entities In the period The energy storage charging and discharging power constraints satisfy the following relationship:

[0061]

[0062] Where, For market players The maximum charge and discharge power of the battery energy storage system, For the scene Lower market entities In the period The energy storage charging and discharging power, when When energy storage charging, Time storage discharge;

[0063] The state of charge constraints of the battery energy storage system satisfy the following relationship:

[0064]

[0065] Where, For the scene Lower market entities The battery energy storage system is The state of charge during the time period, 、 For market players The upper and lower limits of the state of charge of the battery energy storage system;

[0066] The state of charge of the battery energy storage system during the charging and discharging process satisfies the following relationship:

[0067]

[0068] Where, is the self-discharge rate, For the scene Lower market entities The charge and discharge status of the battery energy storage system, is the charge and discharge time, 、 are the charging efficiency and discharging efficiency, For market players The rated capacity of the battery energy storage system.

[0069] The constraints of the operational transaction model of market entities include: distributed PV planning and operation constraints;

[0070] Distributed PV planning and operation constraints include:

[0071] The upper limit of distributed photovoltaic planning capacity satisfies the following relationship:

[0072]

[0073] Where, For market players Planned investment in photovoltaic power generation capacity, For market players The upper limit of photovoltaic power generation capacity that can be invested and constructed;

[0074] Scenario Lower market entities In the period The distributed photovoltaic power generation power satisfies the following relationship:

[0075]

[0076] Where, For the scene Lower market entities In the period The power generation of distributed photovoltaic For the scene Lower market entities The distributed photovoltaic Light resource coefficient for the time period.

[0077] Under the constraints, the operating transaction model of the market players is solved to obtain the active power and reactive power in the distribution network. Using the solved active power and reactive power, the second-order cone relaxation method is used to process the node voltage, branch current and power flow constraints to obtain the second-order cone relaxation terms of the power flow constraints.

[0078] The second-order cone relaxation method is used to process the node voltage and branch current, and the following relationship is obtained:

[0079]

[0080] Where, For the scene Next Time period branch The current relaxation term, 、 Respectively for scenes Next Time period branch The active power and reactive power, For the scene Next Time period node The voltage relaxation term;

[0081] The second-order cone relaxation term of the power flow constraint is obtained, which satisfies the following relationship:

[0082]

[0083]

[0084]

[0085] Where, 、 Node The lower and upper limits of the quadratic term of the voltage, 、 Branch The lower and upper limits of the quadratic term of the current, To take the two norm.

[0086] Using the Lp-Box ADMM method, the scene Lower market entities In the period Character status Perform linear transformation;

[0087] For market players , the augmented function of the objective function is expressed as:

[0088]

[0089] Where, For market players The objective function, For the scene Lower market entities In the period Total amount of participation in end-to-end electricity demand response The Lagrangian relaxation term of For the scene Lower market entities In the period Character status The Lagrangian relaxation term after linearization of the transformation is achieved by the Lp-Box ADMM method;

[0090] Based on the role state quantity of the market subject after linear transformation, the augmented function is linearly decomposed.

[0091] The present invention also proposes a novel end-to-end demand response system for power distribution system, comprising:

[0092] A model building module is used to minimize the total cost of operating transactions, planning investment, and risk of a single market entity as the objective function; establish end-to-end demand response transaction constraints for the market entity; and construct an operating transaction model for the market entity based on the objective function and the end-to-end demand response transaction constraints;

[0093] The constraint establishment module is used to build constraints on the operation and transaction model of market entities based on distributed resource planning and operation data;

[0094] The solution module is used to obtain the second-order cone relaxation term of the power flow constraint based on the operation and transaction model of the market entities that meet the constraints; the augmented function of the objective function is established by using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

[0095] The present invention also provides a terminal, comprising a processor and a storage medium; the storage medium is used to store instructions; and the processor is used to operate according to the instructions to execute steps of the method.

[0096] The present invention also relates to a computer-readable storage medium having a computer program stored thereon, which implements the steps of the method when the program is executed by a processor.

[0097] The beneficial effects of the present invention are that, compared with the prior art, at least the method proposed in the present invention collects the actual characteristics of the new distribution system under distributed resources, and performs refined modeling of end-to-end demand response based on the actual characteristics of the new distribution system, and designs a corresponding distributed solution algorithm; in the context of the development of distributed resource planning needs and end-to-end demand response power transactions of multiple subjects in the new distribution system, considering the objective function of minimizing the comprehensive cost including operation transactions, planning investment, risks, etc., constructs a new distribution system demand response model and studies distributed efficient solution algorithms, stimulates distributed resource planning investment, improves the level of new energy consumption, and realizes end-to-end demand response of the new distribution system under distributed resource planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0098] Figure 1 This is a flow chart of a novel end-to-end demand response method for a power distribution system taking into account distributed resource planning proposed by the present invention;

[0099] Figure 2 A market entity illumination resource coefficient diagram of a novel end-to-end demand response method for a power distribution system under distributed resource planning in an embodiment of the present invention;

[0100] Figure 3 A graph showing average load levels of market entities in a novel end-to-end demand response method for a power distribution system under distributed resource planning in an embodiment of the present invention;

[0101] Figure 4 A comparison chart of total costs for market entities of a novel end-to-end demand response method for a power distribution system under distributed resource planning in an embodiment of the present invention;

[0102] Figure 5 A comparison diagram of BESS power generation capacity planning for market entities in a novel end-to-end demand response method for distribution systems under distributed resource planning in an embodiment of the present invention;

[0103] Figure 6 This is a comparison diagram of photovoltaic power generation capacity planning by market entities in a novel end-to-end demand response method for a distribution system under distributed resource planning in an embodiment of the present invention. DETAILED DESCRIPTION

[0104] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0105] The present invention proposes a new end-to-end demand response method for a power distribution system taking into account distributed resource planning. The new power distribution system includes multiple market entities, such as Figure 1 Shown, including:

[0106] Step 1: Take the minimization of the total cost of operation transactions, planning investment and risk of a single market entity as the objective function; establish the end-to-end demand response transaction constraints of the market entity; and establish the operation transaction model of the market entity based on the objective function and the end-to-end demand response transaction constraints.

[0107] Specifically, in step 1, the actual characteristics of the new distribution system under distributed resources are collected, and the end-to-end demand response is refined modeled based on the actual characteristics of the new distribution system, including:

[0108] Step 1.1: Take the minimization of the total cost of operating transactions, planning investments, and risk of a single market entity as the objective function, satisfying the following relationship:

[0109]

[0110] Where, For market players The objective function, For market players The total cost of running transactions, planning investments and taking risks, For the scene Lower market entities The transaction costs of running For market players The planned investment cost, For market players The risk cost, is the proportional coefficient representing the risk preference of market entities, For the scene The probability of , is the total number of scenes;

[0111] Specifically, the scene Lower market entities The operational transaction costs include: end-to-end demand response costs , end-to-end demand response transmission fee costs , electricity transaction costs with distribution network operators , satisfying the following relationship:

[0112]

[0113] Where, For the scene Lower market entities end-to-end demand response costs, For the scene Lower market entities The transmission cost of end-to-end demand response, For the scene Lower market entities electricity transaction costs with distribution network operators;

[0114] Among them, the scene Lower market entities The end-to-end demand response cost is the sum of the transaction costs in each period, which satisfies the following relationship:

[0115]

[0116] Where, For the scene Lower market entities end-to-end demand response costs, For the scene Lower market entities In the period With market players End-to-end demand response price between For market players A collection of market entities with an end-to-end demand response relationship, For the scene Lower market entities In the period With market players End-to-end demand response volume between

[0117] Scenario where market players need to pay transmission fees to distribution network operators for end-to-end demand response Lower market entities The transmission fee cost of the end-to-end demand response satisfies the following relationship:

[0118]

[0119] Where, For market players With market players Transmission charges for end-to-end demand response between

[0120]

[0121] Where, It is the transmission fee per unit of electricity passing through a unit of electrical distance. For market players With market players The electrical distance between , , For market players Corresponding nodes gather, , For market players Corresponding nodes gather, For nodes and nodes The line impedance between

[0122] Scenario Lower market entities The electricity transaction cost with the distribution network operator satisfies the following relationship:

[0123]

[0124] Where, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, 、 The market entities in the period The purchase and sale prices of electricity in power transactions with distribution network operators, For the scene Lower market entities In the period The character state quantity, when Time scene Lower market entities In the period When purchasing electricity from distribution network operators, Time scene Lower market entities In the period Selling electricity from distribution network operators;

[0125] The risk cost is described by the Conditional Value at Risk (CVaR), which is defined as The confidence interval is higher than the weighted average of the Value at Risk (VaR); market entities The cost of risk satisfies the following relationship:

[0126]

[0127]

[0128] Where, is the value at risk, is the confidence level, For computing scenarios An auxiliary variable for value at risk, which is a positive number.

[0129] Specifically, market entities Planned investment cost Including market players Cost expenditure and market players of BESS The planned investment cost of distributed photovoltaics; among which,

[0130]

[0131] Where, For market players For the cost of BESS, For market players The planned investment cost of BESS, For market players Operation and maintenance costs of BESS;

[0132] Among them, market entities The planned investment cost of BESS satisfies the following relationship:

[0133]

[0134] Where, is the BESS annual value coefficient, For market players For the installed capacity of BESS, For market players The maximum charge and discharge power of BESS, is the unit price of capacity, is the unit price of power;

[0135] in, Satisfies the following relationship:

[0136]

[0137] Where, is the discount rate, is the average service life of BESS, is the average service life of PV in years;

[0138] Market players The operation and maintenance cost of BESS satisfies the following relationship:

[0139]

[0140] Where, is the operation and maintenance rate of BESS;

[0141] The planned investment cost of distributed photovoltaics by market entities satisfies the following relationship:

[0142]

[0143] Where, For market players The planned investment cost of distributed photovoltaics, is the unit power generation capacity cost of distributed photovoltaics, is the annual value coefficient of distributed photovoltaics;

[0144]

[0145] Where, is the discount rate, is the average service life of distributed photovoltaics.

[0146] Step 1.2: Establish end-to-end demand response transaction constraints for market entities;

[0147] Scenario Next, market players The total amount of market players participating in end-to-end electricity demand response is equal to The sum of the end-to-end demand response volume with other market players satisfies the following relationship:

[0148]

[0149] Where, For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period With market players The end-to-end demand response between Market players For electricity buyers and market players For electricity sellers, For market players A collection of market entities that have an end-to-end demand response relationship;

[0150] The following constraints exist between buyers and sellers of the same end-to-end electricity demand response:

[0151]

[0152] Where, For the scene Lower market entities In the period With market players The end-to-end demand response volume between For the scene Lower market entities In the period With market players The end-to-end demand response volume between

[0153] Step 1.3: The operating transaction model of the market entity is constructed based on the objective function and the end-to-end demand response transaction constraints.

[0154] Step 2: Based on the distributed resource planning and operation data, construct the constraints of the operation transaction model of the market entities.

[0155] Specifically, step 2 includes:

[0156] Distributed resource planning and operation data include: planning and operation data of market entities, planning and operation data of battery energy storage systems, and planning and operation data of distributed photovoltaics;

[0157] The constraints of the market entity's operation and transaction model include: market entity power balance constraints, BESS planning and operation constraints, and distributed photovoltaic planning and operation constraints;

[0158] 1) The power balance constraint of market entities satisfies the following relationship:

[0159]

[0160] Where, For the scene Lower market entities In the period The power generation of distributed photovoltaic For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, For the scene Lower market entities In the period The power of the electrical load, For the scene Lower market entities In the period The energy storage charging and discharging power, when When energy storage charging, Time storage discharge;

[0161] The market-agent power balance constraint proposed in this paper centers on the distributed resource planning and operation of market entities. It incorporates dynamic parameters such as end-to-end demand response transaction volume, energy storage charging and discharging power, and distributed photovoltaic output among these entities into the model. Through mathematical modeling and algorithmic optimization, it achieves system-level coordinated control. The "market entities" proposed in this paper are not abstract economic entities, but rather distributed resource entities with technical attributes such as power generation capacity, energy storage equipment, and load demand. Their behavior (such as transaction volume and investment decisions) directly influences system operation through relevant technical constraints.

[0162] 2) BESS planning and operation constraints:

[0163] Market players There is an upper limit constraint on the installed capacity of BESS, which satisfies the following relationship:

[0164]

[0165] Where, For market players Upper limit on the installed capacity of BESS;

[0166] Scenario Lower market entities In the period The energy storage charging and discharging power constraints satisfy the following relationship:

[0167]

[0168] Where, For market players Maximum charge and discharge power for BESS;

[0169] The state of charge constraint of BESS satisfies the following relationship:

[0170]

[0171] Where, For the scene Lower market entities The BESS The state of charge during the time period, 、 For market players The upper and lower limits of the state of charge of the BESS;

[0172] The state of charge during the BESS charging and discharging process satisfies the following relationship:

[0173]

[0174] Where, is the self-discharge rate, For the scene Lower market entities The charge and discharge state of the BESS, is the charge and discharge time, 、 are the charging efficiency and discharging efficiency, For market players The rated capacity of the BESS.

[0175] The BESS operation and planning constraints (such as capacity, charge / discharge power, and state of charge) proposed in this paper are not applied in isolation. Instead, they are deeply coupled with end-to-end demand response transactions, distributed photovoltaic planning, and risk-cost models among market participants. Multi-agent collaborative optimization is achieved through the alternating direction multiplier method (ADMM) and second-order cone relaxation. Existing single parameter constraints for energy storage are not directly adaptable to the dynamic multi-agent interactions in scenarios with a high proportion of new energy. This paper, through technical integration and algorithm fusion, quantifies the behavior of market participants into energy storage operational boundary conditions.

[0176] 3) Distributed photovoltaic planning and operation constraints:

[0177] The distributed PV planning and operation constraints proposed in this paper present novel technical implications. The "capacity ceiling for market participants" in these constraints is not only limited by physical conditions but also dynamically correlated with their end-to-end demand response trading strategies (such as electrical distance in the calculation of transmission fee costs) and risk appetite. This multi-dimensional coupled constraint design enables PV power planning to adapt to changing market trading patterns.

[0178] Considering the limited space and investment capacity of market players in the distribution network, there is an upper limit to the planned capacity of distributed photovoltaics, which satisfies the following relationship:

[0179]

[0180] Where, For market players Planned investment in photovoltaic power generation capacity, For market players The upper limit of photovoltaic power generation capacity that can be invested and constructed;

[0181] Scenario Lower market entities In the period The distributed photovoltaic power generation power satisfies the following relationship:

[0182]

[0183] Where, For the scene Lower market entities The distributed photovoltaic Light resource coefficient of the time period;

[0184] By building a distributed resource planning model and operation model for market players, the comprehensive cost of power planning and operation of market players can be reduced.

[0185] Step 3: Based on the operation and transaction model of the market entities that meet the constraints, the second-order cone relaxation term of the power flow constraint is obtained; the augmented function of the objective function is established using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

[0186] As the number of market entities increases, conventional calculation methods are difficult to meet efficiency requirements. Therefore, the present invention adopts the second-order cone relaxation method and the alternating direction multiplier method to design a corresponding distributed solution algorithm. Step 3 includes:

[0187] Step 3.1: Under the constraints, solve the market entity's operating transaction model to obtain the active power and reactive power in the distribution network. Using the solved active power and reactive power, use the second-order cone relaxation method to process the node voltage, branch current, and power flow constraints to obtain the second-order cone relaxation terms of the power flow constraints.

[0188] The second-order cone relaxation method is used to process the node voltage and branch current, and the following relationship is obtained:

[0189]

[0190] Where, For the scene Next Time period branch The current relaxation term, 、 Respectively for scenes Next Time period branch The active power and reactive power of For the scene Next Time period node The voltage relaxation term;

[0191] The node voltage relaxation term and branch current relaxation term are used to replace the node voltage quadratic term and branch current quadratic term respectively, satisfying the following relationship:

[0192]

[0193] Where, For the scene Next Time period node The voltage quadratic term, For the scene Next Time period branch The quadratic term of the current;

[0194] The power flow constraint of the distribution network is transformed into a second-order cone relaxation term of the power flow constraint, which satisfies the following relationship:

[0195]

[0196]

[0197]

[0198] Where, 、 Node The lower and upper limits of the quadratic term of the voltage, 、 Branch The lower and upper limits of the quadratic term of the current, To take the two norm;

[0199] Step 3.2, use the alternating direction multiplication method to Lower market entities In the period Character status Perform linear transformation;

[0200] Specifically, the alternating direction multiplier method includes but is not limited to the Lp-Box ADMM method. In the embodiment, the Lp-BoxADMM method is a non-limiting and preferred choice;

[0201] Using the Lp-Box ADMM method, the scene Lower market entities In the period Character status Perform the following linear transformation:

[0202]

[0203]

[0204]

[0205] Where, Respectively for scenes Lower market entities In the period The first, second and third auxiliary variables appearing in the transformation process, is the value set of the second auxiliary variable, A set of values ​​for the third auxiliary variable.

[0206] Step 3.3, determine the augmentation function of the objective function;

[0207] Therefore, for market players , the augmented function of the objective function in step 1 is expressed as:

[0208]

[0209] Where, For market players The objective function, For the scene Lower market entities In the period Total amount of participation in end-to-end electricity demand response The Lagrangian relaxation term of For the scene Lower market entities In the period Character status The Lagrangian relaxation term after linearization of the transformation is achieved by the Lp-Box ADMM method;

[0210]

[0211] Where W is the scene set; For the scene Lower market entities In the period With market players The first The end-to-end demand response price at the iteration, For the scene Lower market entities In the period With market players The first End-to-end demand response transaction volume at iteration 1, For the scene Lower market entities In the period With market players The first End-to-end demand response transaction volume at iteration 1, For the scene Lower market entities In the period With market players The end-to-end demand response volume between is a parameter;

[0212]

[0213] Where, 、 、 Respectively for scenes Lower market entities In the period No. The character state quantity is The first, second and third equality constraints that appear during the linear transformation are The dual variable of 、 、 are three positive penalty factors, 、 、 Respectively for scenes Lower market entities In the period No. The first, second, and third auxiliary variables that appear in the transformation at iteration .

[0214] Step 3.4: Based on the role state of the market entity after linear transformation, perform linear decomposition on the augmented function to satisfy the following relationship:

[0215]

[0216]

[0217]

[0218] Where, Used to indicate the value of the independent variable when the function reaches its minimum value in its domain;

[0219]

[0220]

[0221]

[0222]

[0223] Where, To take the positive function.

[0224] In step 3.5, the augmented function is solved under the second-order cone relaxation of the power flow constraint to obtain the end-to-end demand response of each market player.

[0225] The augmented function in the embodiment introduces multi-scenario and multi-period Lagrangian relaxation terms and couples the second-order cone relaxation terms of the distribution network power flow constraints. This directly reflects the spatiotemporal distribution characteristics, multi-agent game relationships, and nonlinear constraint characteristics of distributed resources in the new distribution system, and can effectively respond to the diverse needs of demand response. Based on the role state quantity of the market subject after linear transformation, the augmented function is linearly decomposed, and the original problem generated by the operation transaction model of the market subject that meets the constraints is decomposed into multiple sub-problems. Information exchange and distributed iterative solutions are carried out between the sub-problems. This not only solves the computational efficiency problem in the large-scale market subject scenario, but also realizes collaborative optimization among multiple subjects through the information interaction mechanism.

[0226] Through analysis of the examples, the augmented functions of this application excel in terms of iterative convergence speed, solution optimality, and computational stability. The second-order cone relaxation method effectively reduces the computational complexity of non-convex constraints, while the ADMM algorithm significantly shortens the solution time through parallel computing. The end-to-end demand response of each market participant obtained has been practically applied in new power distribution systems, enabling the evaluation of distributed resource planning results and the cost-effectiveness of market participants.

[0227] The IEEE 33-node distribution network test system is used as the research object, and the research cycle of the system is set to one year. The distribution network is set to contain 10 market entities that can perform end-to-end demand response of the new distribution system, located at nodes 1-10, and the rest are ordinary market entities. Node 33 is the node where the distribution network operator is located. The peak, flat and valley electricity prices purchased by market entities from the distribution network operator are 0.9 yuan / kWh, 0.6 yuan / kWh and 0.4 yuan / kWh respectively, and the electricity price sold to the distribution network operator is 0.1 yuan / kWh. The solar resource coefficient of each market entity is as follows: Figure 2 As shown, the average load level is Figure 3 As shown in the figure, the full-power charging and discharging duration of BESS is 2.5h, the unit price of capacity is 1000 yuan / kWh, the unit price of power is approximately 0, the charging and discharging efficiency is 90%, the service life is 15 years, the operation and maintenance rate is 2%, and the discount rate is 5%; the upper limits of the distributed photovoltaic and BESS power generation capacity planning of a single market entity are 100kW and 50kW respectively, the planned investment cost of distributed photovoltaic is 1200 yuan / kW, and the service life is 25 years; the upper and lower limit values ​​of the distribution network node voltage are 1.05 and 0.95 respectively; the risk preference coefficient of the market entity is ;

[0228] By implementing the method of the present invention according to the above parameter settings, the cost and planning results of multiple entities in the new distribution system can be compared as shown in Table 1, and the cost comparison of market entities under the condition of end-to-end demand response or not can be obtained. Figure 4 As shown in the figure, the market players’ BESS power generation capacity planning is compared. Figure 5 As shown, the results of distributed photovoltaic capacity planning by market entities are as follows Figure 6 As shown;

[0229] Table 1 Comparison of various costs and planning results of multiple entities in the new distribution system

[0230]

[0231] As can be seen from Table 1, end-to-end demand response significantly reduces the operating costs of market entities while reducing total costs, while effectively increasing the planned capacity of photovoltaic resources within the distribution network.

[0232] from Figure 4 It can be seen that end-to-end demand response reduces the total operating costs of market players; Figure 5 It can be seen that in the case of end-to-end demand response, power sharing reduces the differences in resources and load levels among market players in the new distribution system, making the demands of market players on BESS closer. Figure 6 As can be seen in the figure, end-to-end demand response promotes the planning and investment of distributed photovoltaics by multiple entities in the new distribution system. The constructed distributed resource planning model of the new distribution system and the refined modeling of end-to-end demand response are evaluated and analyzed to form a new distribution system end-to-end demand response method under distributed resource planning. The distributed solution algorithm provided by the present invention can effectively realize the planning and investment incentives of distributed resources, weaken the impact of the inherent resource and load level differences of market entities on investment and operation decisions, and significantly reduce the comprehensive costs of market entities at the planning and operation levels.

[0233] The present invention also proposes a novel end-to-end demand response system for power distribution system, comprising:

[0234] A model building module is used to minimize the total cost of operating transactions, planning investment, and risk of a single market entity as the objective function; establish end-to-end demand response transaction constraints for the market entity; and construct an operating transaction model for the market entity based on the objective function and the end-to-end demand response transaction constraints;

[0235] The constraint establishment module is used to build constraints on the operation and transaction model of market entities based on distributed resource planning and operation data;

[0236] The solution module is used to obtain the second-order cone relaxation term of the power flow constraint based on the operation and transaction model of the market entities that meet the constraints; the augmented function of the objective function is established by using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

[0237] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0238] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punched card or raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0239] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0240] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, the state information of the computer-readable program instructions is used to personalize an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), so that the electronic circuit can execute the computer-readable program instructions, thereby implementing various aspects of the present disclosure.

[0241] 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 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 by the scope of protection of the claims of the present invention.

Claims

1. A novel end-to-end demand response method for a power distribution system, wherein the novel power distribution system includes multiple market entities and is characterized in that: include: The objective function is to minimize the total cost of operation, transaction, planning investment and risk of a single market entity; establish the end-to-end demand response transaction constraints of the market entity; and construct the operation and transaction model of the market entity with the objective function and the end-to-end demand response transaction constraints. The objective function satisfies the following relationship: Where, For market players The objective function, For market players The total cost of running transactions, planning investments and taking risks, For the scene Lower market entities The transaction costs of running For market players The planned investment cost, For market players The risk cost, is the proportional coefficient representing the risk preference of market entities, For the scene The probability of , is the total number of scenes; Scenario Lower market entities The transaction cost of running satisfies the following relationship: Where, For the scene Lower market entities end-to-end demand response costs, For the scene Lower market entities The transmission cost of end-to-end demand response, For the scene Lower market entities electricity transaction costs with distribution network operators; Market players Planned investment cost Including market players Cost expenditure and market players of battery energy storage systems Planning and investment costs for distributed photovoltaics; market players The cost of risk satisfies the following relationship: Where, is the value at risk, is the confidence level, For computing scenarios The auxiliary variable of the value at risk is a positive number; Based on distributed resource planning and operation data, the constraints of the operation and transaction model of market entities are constructed; Based on the operation and transaction model of market entities that meet the constraints, the second-order cone relaxation term of the power flow constraint is obtained; the augmented function of the objective function is established by using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

2. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: Scenario Lower market entities The end-to-end demand response cost satisfies the following relationship: Where, For the scene Lower market entities In the period With market players End-to-end demand response price between For market players A collection of market entities with an end-to-end demand response relationship, For the scene Lower market entities In the period With market players End-to-end demand response volume between Scenario Lower market entities The transmission fee cost of the end-to-end demand response satisfies the following relationship: Where, For market players With market players End-to-end demand response transmission fee between It is the transmission fee per unit of electricity passing through a unit of electrical distance. For market players With market players The electrical distance between , , For market players Corresponding nodes gather, , For market players Corresponding nodes gather, For nodes and nodes The line impedance between Scenario Lower market entities The electricity transaction cost with the distribution network operator satisfies the following relationship: Where, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, 、 They are market entities in the time period The purchase and sale prices of electricity in power transactions with distribution network operators, For the scene Lower market entities In the period The character state quantity, when Time scene Lower market entities In the period When purchasing electricity from distribution network operators, Time scene Lower market entities In the period Selling electricity from distribution network operators.

3. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: Market players The cost expenditure of the battery energy storage system satisfies the following relationship: Where, For market players The cost of battery energy storage system, For market players The planned investment cost of the battery energy storage system, For market players Operation and maintenance costs of the battery energy storage system; Where, is the annual value coefficient of the battery energy storage system, For market players The installed capacity of the battery energy storage system, For market players The maximum charge and discharge power of the battery energy storage system, is the unit price of capacity, is the unit price of power; Where, is the discount rate, is the average service life of the battery energy storage system, is the average service life of PV in years; Where, is the operation and maintenance rate of the battery energy storage system; Market players The planned investment cost of distributed photovoltaics satisfies the following relationship: Where, For market players The planned investment cost of distributed photovoltaics, is the unit power generation capacity cost of distributed photovoltaics, is the annual value coefficient of distributed photovoltaics; Where, is the discount rate, is the average service life of distributed photovoltaics.

4. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: End-to-end demand response transaction constraints for market entities, including: Scenario Next, market players The total amount of market players participating in end-to-end electricity demand response is equal to The sum of the end-to-end demand response volume with other market players satisfies the following relationship: Where, For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period With market players The end-to-end demand response between Market players For electricity buyers and market players For electricity sellers, For market players A collection of market entities that have an end-to-end demand response relationship; The following constraints exist between buyers and sellers of the same end-to-end electricity demand response: Where, For the scene Lower market entities In the period With market players The end-to-end demand response volume between For the scene Lower market entities In the period With market players The end-to-end demand response volume between 5. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: The constraints of the market entity's operating transaction model include: the market entity power balance constraint, which satisfies the following relationship: Where, For the scene Lower market entities In the period The power generation of distributed photovoltaic For the scene Lower market entities In the period The total amount of participants in end-to-end electricity demand response, For the scene Lower market entities In the period Electricity trading volume with distribution network operators, For the scene Lower market entities In the period The power of the electrical load, For the scene Lower market entities In the period The energy storage charging and discharging power, when When energy storage charging, Time storage energy discharge.

6. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: The constraints of the operational transaction model of market entities include: battery energy storage system planning and operation constraints; Battery energy storage system planning and operation constraints include: market entities There is an upper limit constraint on the installed capacity of the battery energy storage system that satisfies the following relationship: Where, For market players The installed capacity of the battery energy storage system, For market players An upper limit on the installed capacity of battery energy storage systems; Scenario Lower market entities In the period The energy storage charging and discharging power constraints satisfy the following relationship: Where, For market players The maximum charge and discharge power of the battery energy storage system, For the scene Lower market entities In the period The energy storage charging and discharging power, when When energy storage charging, Time storage discharge; The state of charge constraints of the battery energy storage system satisfy the following relationship: Where, For the scene Lower market entities The battery energy storage system is The state of charge during the time period, 、 For market players The upper and lower limits of the state of charge of the battery energy storage system; The state of charge of the battery energy storage system during the charging and discharging process satisfies the following relationship: Where, is the self-discharge rate, For the scene Lower market entities The charge and discharge status of the battery energy storage system, is the charge and discharge time, 、 are the charging efficiency and discharging efficiency, For market players The rated capacity of the battery energy storage system.

7. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: The constraints of the operational transaction model of market entities include: distributed PV planning and operation constraints; Distributed PV planning and operation constraints include: The upper limit of distributed photovoltaic planning capacity satisfies the following relationship: Where, For market players Planned investment in photovoltaic power generation capacity, For market players The upper limit of photovoltaic power generation capacity that can be invested and constructed; Scenario Lower market entities In the period The distributed photovoltaic power generation power satisfies the following relationship: Where, For the scene Lower market entities In the period The power generation of distributed photovoltaic For the scene Lower market entities The distributed photovoltaic Light resource coefficient for the time period.

8. The novel end-to-end demand response method for power distribution system according to claim 1, characterized in that: Under the constraints, the operating transaction model of the market players is solved to obtain the active power and reactive power in the distribution network. Using the solved active power and reactive power, the second-order cone relaxation method is used to process the node voltage, branch current and power flow constraints to obtain the second-order cone relaxation terms of the power flow constraints. The second-order cone relaxation method is used to process the node voltage and branch current, and the following relationship is obtained: Where, For the scene Next Time period branch The current relaxation term, 、 Respectively for scenes Next Time period branch The active power and reactive power of For the scene Next Time period node The voltage relaxation term; The second-order cone relaxation term of the power flow constraint is obtained, which satisfies the following relationship: Where, 、 Node The lower and upper limits of the quadratic term of the voltage, 、 Branch The lower and upper limits of the quadratic term of the current, To take the two norm.

9. The novel end-to-end demand response method for power distribution system according to claim 8, characterized in that: Using the Lp-Box ADMM method, the scene Lower market entities In the period Character status Perform linear transformation; For market players , the augmented function of the objective function is expressed as: Where, For market players The objective function, For the scene Lower market entities In the period Total amount of participation in end-to-end electricity demand response The Lagrangian relaxation term of For the scene Lower market entities In the period Character status The Lagrangian relaxation term after linearization of the transformation is achieved by the Lp-Box ADMM method; Based on the role state quantity of the market subject after linear transformation, the augmented function is linearly decomposed.

10. A new end-to-end demand response system for power distribution system, characterized by: include: The model building module is used to minimize the total cost of operation transactions, planning investment and risk of a single market entity as the objective function; establish the end-to-end demand response transaction constraints of the market entity; and form the operation transaction model of the market entity with the objective function and the end-to-end demand response transaction constraints; wherein the objective function satisfies the following relationship: Where, For market players The objective function, For market players The total cost of running transactions, planning investments and taking risks, For the scene Lower market entities The transaction costs of running For market players The planned investment cost, For market players The risk cost, is the proportional coefficient representing the risk preference of market entities, For the scene The probability of , is the total number of scenes; Scenario Lower market entities The transaction cost of running satisfies the following relationship: Where, For the scene Lower market entities end-to-end demand response costs, For the scene Lower market entities The transmission cost of end-to-end demand response, For the scene Lower market entities electricity transaction costs with distribution network operators; Market players Planned investment cost Including market players Cost expenditure and market players of battery energy storage systems Planning and investment costs for distributed photovoltaics; market players The cost of risk satisfies the following relationship: Where, is the risk value, is the confidence level, For computing scenarios The auxiliary variable of the value at risk is a positive number; The constraint establishment module is used to build constraints on the operation and transaction model of market entities based on distributed resource planning and operation data; The solution module is used to obtain the second-order cone relaxation term of the power flow constraint based on the operation and transaction model of the market entities that meet the constraints; the augmented function of the objective function is established by using the Lagrangian relaxation term of the system end-to-end demand response and the Lagrangian relaxation term of the role state quantity of the market entity after linear transformation; the augmented function is solved under the second-order cone relaxation term of the power flow constraint to obtain the end-to-end demand response of each market entity.

11. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 9.

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

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