Multi-energy system supply and demand collaborative planning method based on consumer psychology

Abstract

The invention relates to the technical field of energy supply and demand analysis, in particular to a consumer psychology-based multi-energy system supply and demand collaborative planning method, which comprises the following steps of: 1, establishing consumer psychology-based demand response modeling; 2, comprehensively constructing a multi-energy system planning model; and step 3, performing Nash game fair transaction optimization based on the Nash game theory. A demand response uncertainty model based on consumer psychology is established, response behaviors of consumers are described through a piecewise linear function, and uncertainty of flexible energy demand capacity is accurately captured; configuration and operation constraints of multiple energy technologies such as photovoltaic power generation and combined heat and power generation are comprehensively considered, and a multi-energy system design and operation optimization model covering electric power, heat supply and cold supply is constructed; and the Nash game theory is adopted to ensure the balance of the interests of the supplier and the demander, so that the fairness and sustainability of energy transaction are ensured.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy supply and demand analysis, in particular to a multi-energy system supply and demand collaborative planning method based on consumer psychology. BACKGROUND

[0002] With the deepening of global energy structure transformation and smart city construction, the traditional single energy supply mode has been difficult to meet the increasingly complex energy demand of modern communities. As an integrated solution that integrates electricity, heating, cooling and other forms of energy, multi-energy systems can significantly improve energy utilization efficiency and reduce system operation costs through complementary conversion and collaborative optimization of energy, and have become an important technical path to achieve the goal of carbon peak and carbon neutral. Under this background, the community energy market has emerged, optimizing resource allocation through market mechanisms, promoting clean energy consumption and improving the overall economy of the system. However, the community energy market involves multiple stakeholders, including energy suppliers, demand response aggregators, end users, etc. While pursuing their own maximum benefits, conflicts of interest may arise, leading to low market efficiency. In addition, users' energy consumption behavior has strong subjectivity and uncertainty, and traditional deterministic optimization methods are difficult to accurately describe and predict users' true demand response capacity, affecting the optimal configuration and operation strategy of multi-energy systems.

[0003] Specifically, the existing methods have the following shortcomings:

[0004] In terms of demand response modeling, existing researches mostly use deterministic constraints to introduce demand response into model optimization, failing to consider that the flexible energy demand capacity of residents participating in demand response programs is variable, ignoring the influence of consumer psychology on demand response behavior, and unable to effectively identify the willingness of residents to participate in demand response, resulting in ineffective supply and demand interaction;

[0005] In terms of system design optimization, most researches ignore the influence of demand side interaction on multi-energy system design optimization. Energy demand, as an important input data for design optimization model, its change will further affect energy trading and demand response, and the management strategy of demand side flexibility resource is also an important factor affecting energy system design;

[0006] In terms of energy trading strategy, existing researches lack a comprehensive consideration of demand response modeling and multi-energy system design optimization in overall fair energy trading strategy, which cannot handle the interaction between supply and demand, and is prone to unfair trading, affecting the long-term cooperation willingness of all participants. SUMMARY

[0007] (I) Technical problem

[0008] The present application aims to at least solve the problem in the prior art that energy supply distribution needs to be matched with consumer demand for analysis.

[0009] (II) Technical content

[0010] The present application provides a multi-energy system supply and demand coordination planning method based on consumer psychology, comprising the following steps:

[0011] Step one, establish demand response modeling based on consumer psychology;

[0012] Step two, comprehensively build a multi-energy system planning model;

[0013] Step three, Nash game fair trade optimization based on Nash game theory.

[0014] Preferred technical solution one: the demand response modeling based on consumer psychology in step one needs to be generated first; scene generation

[0015] The present application establishes a double-layer optimization architecture containing demand response aggregators and energy retailers; energy retailers affect the flexible demand capacity of users through price signals, and the user response behavior of aggregators in turn affects system design and operation strategy, forming a closed-loop interaction mechanism;

[0016] Multiple uncertainties are handled by using multiple scenario trees, considering multi-time scales of year-season-day-hour, and using K-means clustering algorithm to generate representative energy demand curves, and the scene probability constraints are as follows:

[0017]

[0018] In the formula, Prob sc Indicates the probability of the sc-th scene, sc is the random scene index, and the formula ensures that the sum of all scene probabilities is equal to 1;

[0019] The demand response modeling based on consumer psychology includes an aggregator model and a demand response model;

[0020] Aggregator model:

[0021] The objective function of the demand response aggregator is to minimize the energy cost (EP C) of purchasing electricity, heat and cooling from the energy retailer and the energy cost (EVC) of the electric vehicle interacting with the MES of the energy retailer, defined as the annual total cost (DAC) of the DR aggregator:

[0022]

[0023] In the formula, s is the season, and h is the time.

[0024] Demand response model:

[0025] The energy demand is divided into flexible demand and inflexible demand, and the flexible demand is further divided into reducible demand, transferable demand and electric vehicle charging demand; based on the psychology of consumers, the demand response modeling is constructed as follows:

[0026]

[0027]

[0028] In the formula, λ + and λ - respectively represent the flexible energy demand response rate, and are the upper and lower bounds of the maximum demand response rate; the parameters m + / m - represent the insensitive interval threshold, when the price difference is less than the threshold, the consumer has no response; n + / n - represent the response interval threshold, and the response rate reaches saturation after exceeding the threshold; Δp represents the price difference, defined as the difference between the market reference price and the energy retailer transaction price p;

[0029] Linearization, the linear expression of the piecewise function (λ∈[λ + ,λ - ]) is:

[0030] λ=λ max w3+λ max w4 (6)

[0031] Δp=mw2+nw3+w4 (7)

[0032]

[0033] In the formula, wherein w1, w2, w3, w4 are continuous variables, used for linear representation of the piecewise function, z1, z1, z1 are binary variables, used to control different segmented intervals; the parameters m and n are threshold parameters, which ensure the accuracy of linearization through constraint conditions, λ max can be obtained based on demonstration project data, field research, etc.

[0034] The preferred technical solution two: the step two covers the construction of the design and operation optimization model of the multi-energy system of electricity, heat supply and cold supply, which includes the construction of the multi-energy system planning model, specifically as follows:

[0035] Retailer model:

[0036] The energy retailer objective function is to maximize the annual total profit of the energy retailer (EAP):

[0037]

[0038] where CAPEX, FC and MC represent capital expenditure, fuel cost and maintenance cost, respectively;

[0039] Related constraints:

[0040] Consider photovoltaic, combined heat and power, boiler, electric refrigeration, absorption refrigeration and other energy technologies;

[0041] The energy balance constraints are as follows:

[0042]

[0043] In the power balance, E PV represents the photovoltaic power generation, E CHP represents the combined heat and power generation, E cha / disc_EV represents the charging and discharging of electric vehicles, E im and E ex represent the electricity purchased from and sold to the grid, E ec represents the power consumption of the electric refrigerator, Q ele_DR represents the power demand after demand response; in the heat supply balance, Q heat_CHP and Q heat_boi represent the heat supply of combined heat and power and boiler, Q heat_ac represents the heat consumption of the absorption refrigerator, Q heat_DR represents the heat demand after demand response; in the cooling supply balance, Q cool_ec and Q cool_ac represent the cooling supply of the electric refrigerator and absorption refrigerator, Q cool_DR represents the cooling demand after demand response;

[0044] The operation constraints are as follows:

[0045]

[0046] where η represents the equipment efficiency, and NG represents the natural gas consumption; for the photovoltaic system, A PV represents the photovoltaic panel installation area, η PV represents the photovoltaic conversion efficiency varying with time and weather conditions, and SRI represents the solar radiation index.

[0047] Preferred technical solution three: the specific process of the Nash game fair trade optimization in step three is:

[0048] Establish a Nash game model to achieve benefit balance:

[0049]

[0050] Solve by linearization method:

[0051]

[0052] In the formula, Nash product function, u1 represents the cost function of demand response aggregator, u2 represents the profit function of energy retailer, U and L represent upper and lower limits respectively; is a parameter.

[0053] (Three) technical effects

[0054] The above structure makes the scheme have the following beneficial effects:

[0055] 1. Demand response modeling method based on consumer psychology: Establish a demand response uncertainty model based on consumer psychology, describe the response behavior of consumers through piecewise linear function, and accurately capture the uncertainty of flexible energy demand capacity;

[0056] 2. Comprehensive multi-energy system optimization architecture: Build a multi-energy system design and operation optimization model covering electricity, heating and cooling, and comprehensively consider the configuration and operation constraints of photovoltaic power generation, combined heat and power and other energy technologies, and realize complementary supply of multi-energy;

[0057] 3. Fair game trading mechanism: Nash game theory is adopted to ensure the balance of interests of supply and demand, avoid the problem that one party's interests are sacrificed in traditional methods, and ensure the fairness and sustainability of energy trading. DETAILED DESCRIPTION

[0058] The accompanying drawings are used to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, used to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0059] Figure 1 is the system supply and demand coordination structure diagram of the present scheme. DETAILED DESCRIPTION

[0060] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments; based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0061] Please refer to Figure 1 , the multi-energy system supply and demand coordination planning method based on consumer psychology includes the following steps:

[0062] Step one: scenario generation

[0063] The present application establishes a double-layer optimization architecture comprising a demand response aggregator and an energy retailer; the energy retailer influences the flexible demand capacity of users through price signals, and the user response behavior of the aggregator influences the system design and operation strategy in turn, forming a closed-loop interaction mechanism;

[0064] Multiple uncertainties are handled by using multiple scenario trees, considering multiple time scales of year-season-day-hour, and using K-means clustering algorithm to generate representative energy demand curves, and the scenario probability constraints are as follows

[0065]

[0066] In the formula, Prob sc represents the probability of the sc-th scenario, sc is the random scenario index, and the formula ensures that the sum of all scenario probabilities is equal to 1;

[0067] Step two: demand response modeling based on consumer psychology

[0068] 2.1 Aggregator model

[0069] The objective function of the demand response aggregator is to minimize the energy cost (EP C) of purchasing electricity, heat and cooling from the energy retailer and the energy cost (EVC) of the electric vehicle interacting with the energy retailer MES, defined as the annual total cost (DAC) of the DR aggregator:

[0070]

[0071] In the formula, s is the season, and h is the time.

[0072] 2.2 Demand response model

[0073] The energy demand is divided into flexible demand and inflexible demand, and the flexible demand is further divided into reducible demand, transferable demand and electric vehicle charging demand; based on consumer psychology, the demand response modeling is constructed as follows:

[0074]

[0075]

[0076] In the formula, λ + and λ - respectively represent the flexible energy demand response rate, and are the upper and lower bounds of the maximum demand response rate; the parameters m + / m - represent the insensitive interval threshold, when the price difference is less than the threshold, the consumer has no response; n + / n -represents the response interval threshold beyond which the response rate reaches saturation; Δp represents the price difference, defined as the market reference price the difference between the transaction price p and the energy retailer's price;

[0077] linearization process, the linear representation of the piecewise function (λ∈[λ + ,λ-]) is:

[0078] λ=λ max w3+λ max w4 (6)

[0079] Δp=mw2+nw3+w4 (7)

[0080]

[0081] wherein, w1, w2, w3, w4 are continuous variables for the linear representation of the piecewise function, z1, z1, z1 are binary variables for controlling different piecewise intervals; parameters m and n are threshold parameters, which ensure the accuracy of linearization through constraint conditions, λ max can be obtained based on demonstration project data, field research, etc.

[0082] Step three: multi-energy system planning model

[0083] 3.1 Retailer model

[0084] The energy retailer's objective function is to maximize the annual total profit of the energy retailer (EAP):

[0085]

[0086] wherein, CAPEX, FC and MC represent investment cost, fuel cost and maintenance cost, respectively;

[0087] 3.2 Related constraints

[0088] Consider photovoltaic, combined heat and power, boiler, electric refrigeration, absorption refrigeration and other energy technologies;

[0089] The energy balance constraint is as follows:

[0090]

[0091]

[0092] wherein, in the power balance, E PV represents the photovoltaic power generation, E CHP represents the combined heat and power generation, E cha / disc_EV represents the charging and discharging of electric vehicles, E im and E exThey represent the amount of electricity purchased from and sold to the grid, E ec Indicates the power consumption of the electric refrigerator, E ele_DR Indicates the electricity demand after demand response; in the heat balance, Q heat_CHP and Q heat_boi Represent the heat supply of cogeneration and boiler respectively, Q heat_ac Indicates the heat consumption of the absorption chiller, Q heat_DR represents the heating demand after demand response; in the cooling balance, Q cool_ec and Q cool_ac Represent the cooling capacity of the electric refrigerator and absorption refrigerator respectively, Q cool_DR represents the cooling demand after demand response;

[0093] The operating constraints are as follows:

[0094]

[0095] In the formula, η represents the equipment efficiency, NG represents its natural gas consumption; for photovoltaic systems, A PV represents the photovoltaic panel installation area, η PV It represents the photovoltaic conversion efficiency that varies with time and meteorological conditions, and SRI represents the solar radiation index;

[0096] Step 5: Nash game fair transaction optimization

[0097] Establishing Nash game model to achieve interest balance:

[0098]

[0099] Solve using the linearization method:

[0100]

[0101] Where, is the Nash product function, u1 represents the cost function of the demand response aggregator, u2 represents the profit function of the energy retailer, U and L represent the upper and lower bounds respectively; As a parameter.

[0102] Unless otherwise expressly specified or limited, the terms "disposed," "installed," "connected," and "connected" should be understood broadly. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediary; and internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this application.

[0103] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A multi-energy system supply and demand collaborative planning method based on consumer psychology, characterized by: The steps include: Step 1: Establish a demand response model based on consumer psychology; Step 2: Comprehensively construct a multi-energy system planning model; Step three: optimize the fair transaction of Nash game based on Nash game theory.

2. The method for collaborative planning of supply and demand of multi-energy systems based on consumer psychology according to claim 1 is characterized by: In step 1, scenario generation needs to be performed before modeling the demand response based on consumer psychology; The scene generation process is as follows: Establish a two-tier optimization architecture consisting of demand response aggregators and energy retailers. Energy retailers influence users' flexible demand capacity through price signals, and aggregators' user response behavior in turn influences system design and operation strategies, forming a closed-loop interaction mechanism. A multi-scenario tree is used to handle multiple uncertainties, considering multiple time scales of year, season, day, and hour. A K-means clustering algorithm is used to generate a representative energy demand curve. The scenario probability constraints are as follows: Where Prob sc represents the probability of the scth scene, where sc is the random scene index. This formula ensures that the sum of all scene probabilities is equal to 1.

3. The method for collaborative planning of supply and demand of multi-energy systems based on consumer psychology according to claim 2 is characterized by: In step 1, the demand response modeling based on consumer psychology includes the aggregator model and the demand response model, specifically: Aggregator Model: The objective function of the demand response aggregator is to minimize the energy cost (EPC) of purchasing electricity, heating, and cooling from energy retailers and the energy cost (EVC) of two-way interaction between electric vehicles and the energy retailer’s MES, which is defined as the DR aggregator’s annual total cost (DAC): Where s is the season and h is the time; Demand Response Model: Energy demand is divided into flexible demand and non-flexible demand. Flexible demand is further divided into curtailable demand, transferable demand, and electric vehicle charging demand. Based on consumer psychology, the demand response model is constructed as follows: Where λ + and λ - They represent the flexible energy demand response rate, and is the upper and lower bounds of the maximum demand response rate; parameter m + / m - Indicates the insensitive interval threshold. When the price difference is less than the threshold, consumers will not respond. + / n - represents the response interval threshold, after which the response rate reaches saturation; Δp represents the price difference, which is defined as the market reference price The difference between the transaction price p with the energy retailer; Linearization processing, piecewise function (λ∈[λ + ,λ-]) is expressed as follows: λ=λ max w3+λ max w4 (6) Δp=mw2+nw3+w4 (7) Where w1, w2, w3, w4 are continuous variables used for linear representation of piecewise functions, z1, z1, z1 are binary variables used to control different segmentation intervals; parameters m and n are threshold parameters, which ensure the accuracy of linearization through constraints, and λ max It can be obtained based on demonstration project data, field surveys, etc.

4. The method for collaborative planning of supply and demand of multi-energy systems based on consumer psychology according to claim 3 is characterized by: The construction of the multi-energy system design and operation optimization model covering electricity, heating and cooling in step 2 includes the construction of a multi-energy system planning model, as follows; Retailer Model: The energy retailer's objective function is to maximize the energy retailer's total annual profit (EAP): Among them, CAPEX, FC, and MC represent investment cost, fuel cost, and maintenance cost, respectively; Related constraints: Consider various energy technologies such as photovoltaics, combined heat and power, boilers, electric refrigeration, and absorption refrigeration; The energy balance constraints are as follows: In the power balance, E PV Represents photovoltaic power generation, E CHP Indicates the power generation of combined heat and power, E cha / disc_EV Indicates the charging and discharging amount of the electric vehicle, E im and E ex They represent the amount of electricity purchased from and sold to the grid, E ec Indicates the power consumption of the electric refrigerator, E ele_DR Indicates the electricity demand after demand response; in the heat balance, Q heat_CHP and Q heat_boi Represent the heat supply of cogeneration and boiler respectively, Q heat_ac Indicates the heat consumption of the absorption chiller, Q heat_DR represents the heating demand after demand response; in the cooling balance, Q cool_ec and Q cool_ec Represent the cooling capacity of the electric refrigerator and absorption refrigerator respectively, Q cool_DR represents the cooling demand after demand response; The operating constraints are as follows: In the formula, η represents the equipment efficiency, NG represents its natural gas consumption; for photovoltaic systems, A PV represents the photovoltaic panel installation area, η PV It represents the photovoltaic conversion efficiency that changes with time and meteorological conditions, and SRI represents the solar radiation index.

5. The method for collaborative planning of supply and demand of multi-energy systems based on consumer psychology according to claim 4 is characterized by: The specific process of optimizing the fair transaction of the Nash game described in step 3 is as follows: Establishing Nash game model to achieve interest balance: Solve using the linearization method: Where, is the Nash product function, u1 represents the cost function of the demand response aggregator, u2 represents the profit function of the energy retailer, U and L represent the upper and lower bounds respectively; As a parameter.

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