A low-carbon optimal scheduling method for integrated energy systems with load response under the influence of flexible energy pricing

Abstract

This invention discloses a low-carbon optimized scheduling method for an integrated energy system with load response under the influence of flexible energy pricing. The method comprises: utilizing carbon flow tracking to quantify carbon emissions of network nodes in real time; pricing integrated energy based on carbon trading costs and load energy demand to construct an integrated energy pricing model; establishing an integrated demand response model that normalizes the internal price of the integrated energy system using time-of-use electricity prices as a benchmark value; and solving the constructed low-carbon optimized scheduling model for the integrated energy system with load response under the influence of flexible energy pricing. The method proposed in this invention improves the economic efficiency and carbon emission level of the integrated energy system while enhancing the integrated demand response of the integrated energy system.

Description

Technical Field

[0001] The present invention relates to a low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing, and belongs to the field of integrated energy system scheduling. Background Art

[0002] Under the "dual carbon" goals, my country's carbon market has seen a significant increase in activity, spurring carbon reduction efforts across all sectors. Furthermore, the power industry, a key player in carbon trading, has become a key sector for carbon reduction. Carbon trading plays a crucial role in the operation of the power market, not only deeply involved in the market but also significantly influencing its dynamics and trends. Integrated energy systems leverage the complementary nature of different energy sources to organically coordinate and optimize energy generation, transmission and distribution, conversion, storage, and consumption. This allows for the coupled transformation of multiple heterogeneous energy flows, resulting in an integrated energy system that efficiently utilizes and rationally allocates energy. Domestic and international scholars have diverse and controversial views on who should bear the costs of carbon trading. While the primary source of carbon emissions lies on the power generation side, the lack of sufficient coordination and response on the demand side poses significant challenges to the advancement of carbon trading in the power market. To optimize energy management and increase operational profitability, integrated energy systems also crucially need to pay attention to energy market prices. Therefore, the optimal scheduling of an integrated energy system with integrated demand response needs to consider the carbon trading cost sharing of sources and loads and combine the energy production scheduling characteristics of the integrated energy system to formulate a price strategy applicable to the integrated energy system to achieve efficient utilization of system energy and economical low-carbon operation.

[0003] In view of this, the present invention is proposed. Summary of the Invention

[0004] The present invention provides a low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing, which is used to establish a low-carbon optimization scheduling model for an integrated energy system with load response under the influence of flexible energy pricing, and uses an optimistic action-criteria depth algorithm to solve the problem to obtain an optimized operation plan for a regional integrated energy system with load response under the influence of flexible energy pricing.

[0005] The technical solution of the present invention is: according to the first aspect of the present invention, a low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing is provided, including: Step 1, using carbon flow tracking to quantify the carbon emissions of network nodes in real time; Step 2, pricing the integrated energy based on carbon trading costs and load energy demand, and constructing a comprehensive energy price model; Step 3, establishing a comprehensive demand response model with time-of-use electricity prices as the benchmark value to normalize the internal prices of the integrated energy system; Step 4, solving the constructed low-carbon optimization scheduling model for the integrated energy system with load response under the influence of flexible energy pricing.

[0006] The Step 1 includes: S1.1: establishing a carbon trading model based on changes in system carbon emissions; S1.2: establishing a carbon flow tracking node measurement model; S1.3: establishing a carbon flow tracking node cost model for carbon trading participants based on the carbon trading model based on changes in system carbon emissions and the carbon flow tracking node measurement model.

[0007] The carbon flow tracking node cost model for the carbon trading is expressed as:

[0008]

[0009] Where: is the carbon cost of node j at time t; is the power flowing from the i-th branch into node j at time t; ρ i,j is the carbon flow density of branch j connected to node n; is the output power of the unit x connected to node j at time t; e x,j is the carbon emission intensity of unit x connected to node j; I is the number of node branches; X is the number of units connected to the node; is the carbon trading cost of the comprehensive energy system at time t.

[0010] The comprehensive energy price model is expressed as follows:

[0011]

[0012]

[0013] Where: Pricing the electricity of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t the carbon trading costs of obtaining electricity; is the energy demand elasticity price at time t Electricity load demand elasticity in price; Pricing the heat energy of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t Obtain carbon trading costs for heating; is the energy demand elasticity price at time t The demand elasticity of heat load in price.

[0014] The comprehensive demand response model is expressed as:

[0015]

[0016]

[0017] Where: is the load response at time t; E is the response elasticity matrix; ΔP t is the initial response at time t; where: e t,t is the elasticity factor of the response elasticity matrix; U t Energy pricing for the integrated energy system at time t; p t is the energy price in the market at time t.

[0018] The Step 4 is specifically as follows:

[0019] Establishing a low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load; wherein, the low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load includes an objective function and constraint conditions;

[0020] The optimistic action-criticism deep algorithm is used to solve the low-carbon optimal scheduling model of the integrated energy system with load response under the influence of flexible energy pricing.

[0021] The objective function is expressed as:

[0022] minC=C Buy +C CET +C IDR +C SCH +C RUN

[0023] Where: C is the total cost of the integrated energy system; C Buy is the energy purchase cost; C CET is the carbon trading cost; C IDR is the system IDR cost; C SCH is the system scheduling cost; C RUN The operating cost of the integrated energy system.

[0024] The constraints include power balance constraints, integrated energy system operation constraints, and equipment capacity constraints.

[0025] According to the second aspect of the present invention, a low-carbon optimization scheduling system for an integrated energy system with load response under the influence of flexible energy pricing is provided, comprising: a module for quantifying carbon emissions of network nodes in real time by utilizing carbon flow tracking; a module for pricing integrated energy based on carbon trading costs and load energy demand, and constructing an integrated energy price model; a module for establishing an integrated demand response model with time-of-use electricity prices as a benchmark value to normalize the internal prices of the integrated energy system; and a module for solving the constructed low-carbon optimization scheduling model for an integrated energy system with load response under the influence of flexible energy pricing.

[0026] The beneficial effects of the present invention are:

[0027] 1) Using carbon flow tracking to measure carbon emissions across the entire system network, more accurately capturing carbon emissions at each node. This method distributes carbon trading costs across both the source and the load, addressing the shortcomings of traditional carbon measurement methods that only consider source-side carbon costs.

[0028] 2) Fully consider the influencing factors of the real-time changing integrated energy market prices and the energy demand of the integrated energy system to provide a basis for integrated energy pricing. In addition, the carbon transaction costs allocated to the load side by carbon flow tracking are formed to form an internal pricing model for the integrated energy system, thereby guiding the integrated load to respond to demand.

[0029] 3) Taking into account carbon emissions and energy demand response, this paper constructs a low-carbon optimized scheduling model for integrated energy systems that responds to loads under the influence of flexible energy pricing. This model is trained offline and optimized online using an optimistic action-criteria algorithm with an optimistic exploration strategy. This model effectively smooths fluctuations in the electrical and thermal loads of energy consumers, shaving peaks and filling valleys. This reduces the operating costs of the integrated energy system and the energy purchase costs of energy consumers, achieving a win-win situation for both the source and the load of the integrated energy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 is a flow chart of the present invention;

[0031] Figure 2 This is the structural diagram of the integrated energy system;

[0032] Figure 3 Scenario 1: Electric load dispatch results and demand response;

[0033] Figure 4 Scenario 1 heat load scheduling results and demand response;

[0034] Figure 5 Scenario 2: Electric load dispatch results and demand response;

[0035] Figure 6 Scenario 2 heat load scheduling results and demand response;

[0036] Figure 7 Scenario 3: Electric load dispatch results and demand response;

[0037] Figure 8 Scenario 3 heat load scheduling results and demand response. DETAILED DESCRIPTION

[0038] To make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should be noted that, in the absence of conflict, the embodiments in this application and the features in the embodiments can be combined with each other in any way.

[0039] Example 1: Figure 1-8 As shown, according to the first aspect of an embodiment of the present invention, a low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing is provided, including: Step 1, using carbon flow tracking to quantify the carbon emissions of network nodes in real time; Step 2, pricing the integrated energy based on the carbon trading cost and load energy demand, and constructing an integrated energy price model; Step 3, establishing a comprehensive demand response model with time-of-use electricity price as the benchmark value to normalize the internal price of the integrated energy system; Step 4, solving the constructed low-carbon optimization scheduling model for the integrated energy system with load response under the influence of flexible energy pricing.

[0040] Furthermore, the Step 1 includes: S1.1: establishing a carbon trading model based on changes in system carbon emissions; S1.2: establishing a carbon flow tracking node metering model; S1.3: establishing a carbon flow tracking node cost model for carbon trading participants based on the carbon trading model based on changes in system carbon emissions and the carbon flow tracking node metering model.

[0041] Specifically, Step 1 may include:

[0042] S1.1: Establish a carbon trading model based on changes in systemic carbon emissions. To ensure a gradual and orderly reduction in overall emissions, my country has allocated carbon emissions to designated enterprises in the form of free carbon allowances. Enterprises can emit carbon within the balance of their free carbon allowances to meet their daily carbon emission needs. my country's free carbon allowance system is based on a baseline approach and serves to limit corporate carbon emissions. Carbon allowances are determined based on the hourly output of gas-fired units and a weighted factor for carbon emissions from power generation on the source side of the integrated energy system.

[0043]

[0044]

[0045]

[0046] Where: is the carbon quota of the integrated energy system at time t; δ is the carbon emission weighting factor, which is set to 0.39; is the power generation capacity of the gas turbine at time t; is the heating power of the gas boiler at time t; is the actual carbon emissions of the integrated energy system at time t; δ GT,fact is the carbon emission coefficient of the gas turbine unit, which is set to 0.8; δ GB,fact is the carbon emission coefficient of the gas boiler, which is set to 0.8; is the carbon transaction cost of the comprehensive energy system at time t; c CET is the carbon trading price, set at 50 yuan / ton; T is 24 hours.

[0047] S1.2: Establish a carbon flow tracking node metering model. To enhance the responsiveness of demand-side controllable loads in carbon reduction, this paper applies carbon emission flow theory and the concept of node carbon potential to accurately quantify carbon emissions and attribute them to users. Furthermore, by implementing a carbon reduction incentive mechanism, this aims to guide and optimize user electricity usage behavior, thereby contributing to achieving low-carbon goals.

[0048]

[0049] Where: is the carbon flow at node j at time t; is the power flowing from the i-th branch into node j at time t; ρ i,j is the carbon flow density of branch j connected to node n; is the output power of the unit x connected to node j at time t; e x,j is the carbon emission intensity of unit x connected to node j; I is the number of node branches; X is the number of units connected to the node.

[0050] S1.3: Establish a node cost model for carbon flow tracking in carbon trading. Based on the core concept of node carbon potential, a comprehensive user carbon reduction incentive system has been constructed. This system not only covers the management of controllable loads but also incorporates the use of energy storage devices. This system aims to guide users to more actively participate in carbon reduction activities and optimize load electricity consumption through reasonable incentive mechanisms.

[0051]

[0052] Where: is the carbon cost of node j at time t; is the power flowing from the i-th branch into node j at time t; ρ i,j is the carbon flow density of branch j connected to node n; is the output power of the unit x connected to node j at time t; e x,j is the carbon emission intensity of unit x connected to node j; I is the number of node branches; X is the number of units connected to the node; is the carbon trading cost of the comprehensive energy system at time t.

[0053]

[0054] Where: is the carbon transaction cost under carbon flow tracking at time t; p e,c 、p h,c The carbon reduction incentive price for electricity and heat units is set at 0.25 yuan / kW; is the load increase and decrease of node j in response to carbon reduction at time t; is the load increase and decrease of node j’s heat load in response to carbon reduction at time t; are the carbon costs of electricity and heat supply at node j at time t, respectively; J is the number of nodes; is the original electrical load at time t; is the original heat load at time t.

[0055] Furthermore, the Step 2 includes:

[0056] S2.1: Establish a comprehensive energy pricing model. Carbon trading and energy demand are two key factors in the energy pricing process. Energy generated by burning fossil fuels has high carbon emissions, and its price should include the cost of carbon emissions. Considering the subsidy policy for free carbon quotas, only carbon emissions exceeding the carbon quota will be included in carbon flow tracking and incorporated into energy prices. Furthermore, to account for varying energy demand, energy prices that take into account energy consumption fluctuations will now be incorporated into the energy pricing system.

[0057]

[0058] Where: is the demand elasticity price of energy at time t; is the price of electricity in the energy market at time t; is the original electrical load at time t; is the average electric load in a day, set to 2084kW; is the price of heat energy in the market at time t; is the original heat load at time t; is the average heat load in a day, set to 2059.4kW.

[0059]

[0060]

[0061] Where: Pricing the electricity of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t the carbon trading costs of obtaining electricity; is the energy demand elasticity price at time t Electricity load demand elasticity in price; Pricing the heat energy of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t Obtain carbon trading costs for heating; is the energy demand elasticity price at time t The demand elasticity of heat load in price.

[0062] Furthermore, the Step 3 includes:

[0063] S3.1: Use time-of-use energy prices as a benchmark to normalize the internal prices of the integrated energy system. Time-of-use energy prices are the standard for the energy market and provide a reference for the internal energy prices of the integrated energy system. Normalizing the internal energy prices can serve as an elasticity factor for the system's overall load demand response. The response elasticity matrix is the ratio between the internal energy system pricing and the time-of-use energy prices, serving as the response coefficient.

[0064]

[0065] Where: e t,t is the elasticity factor of the response elasticity matrix; U t The energy price of the integrated energy system at time t; p t is the energy price in the market at time t; E is the response elasticity matrix.

[0066]

[0067]

[0068] Where: is the load reduction amount that can be reduced at time t; is the transferable load at time t; ρ CL is the load reduction factor, set to 0.3; ρ SL is the load transfer coefficient, set to 0.3; is the original electrical load at time t.

[0069] On the basis of comprehensive energy regulation of curtailable load and transferable load, the constraints of available load response model are as follows:

[0070]

[0071]

[0072] Where: In response to electrical load; is the original electrical load at time t; is the load reduction amount that can be reduced at time t; is the load that can be transferred at time t; is the original heat load at time t; Heat is delivered starting at time t; is the heat loss at time t; ξ is the heat loss coefficient, set to 0.2; T is 24 hours.

[0073]

[0074]

[0075] Where: is the heat load response at time t; μ is the irreducible heat load coefficient, which is set to 0.3; is the amount of heat load reduction that can be achieved at time t; e t,t is the element of the response elasticity matrix; ρ h is the heat load reduction factor, set to 0.3; is the original heat load at time t.

[0076]

[0077] Where: In response to heat load; is the original heat load at time t; is the heat load response at time t; is the heat loss at time t.

[0078] S3.2: The elasticity factor of the integrated demand response is formed based on the per-unit price value within the integrated energy system. The integrated energy system has the energy operation characteristics of multi-energy coupling, and the characteristics of energy can be used to achieve the time-shifting effect of the energy demand on the load side. Electric load demand response is divided into electricity price type and incentive type. After analyzing the internal costs of the integrated energy system, the electricity price type demand response can be used to maintain the balance between supply and demand in the integrated energy system. The thermal load relies on sensory judgment in the heating area space and has a certain energy tolerance rate. Therefore, the thermal load itself has a certain adjustment space. In addition, it can also be adjusted by using the thermal energy price. The calculation of the amount of electric load that can be reduced, the amount of load transferred, and the amount of thermal load that can be reduced can be simplified as follows:

[0079]

[0080] Where: is the load response at time t; E is the response elasticity matrix; ΔP t is the initial response at time t.

[0081] Furthermore, the Step 4 is specifically as follows: establishing a low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load; wherein, the low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load includes an objective function and constraints; and using an optimistic action-judgment deep algorithm to solve the constructed low-carbon optimization scheduling model for an integrated energy system with load response under the influence of flexible energy pricing.

[0082] Specifically, Step 4 may include:

[0083] S4.1: A low-carbon optimal dispatch model for the integrated energy system is constructed based on the integrated energy system load demand response and solved using an optimistic action-criteria deep algorithm. Under the integrated energy pricing model, the core objective of the integrated energy system optimal operation model is to achieve economic efficiency across the entire integrated energy network while strictly adhering to system operational constraints. This model minimizes source-side energy purchase costs, carbon trading costs, system IDR costs, system dispatch costs, and integrated energy system operating costs as its objective function.

[0084] minC=C Buy +C CET +C IDR +C SCH +C RUN

[0085] Where: C is the total cost of the integrated energy system; C Buy is the energy purchase cost; C CET is the carbon trading cost; C IDR is the system IDR cost; C SCH is the system scheduling cost; C RUN The operating cost of the integrated energy system.

[0086]

[0087] Where: C Buy is the energy purchase cost; C CET is the carbon trading cost; C IDR is the system IDR cost; C SCH is the system scheduling cost; C RUN Cost of running the integrated energy system; The price of electricity purchased and sold by the integrated energy system from the upper power grid at time t; The integrated energy system purchases and sells electricity from the upper power grid at time t, which is equal to the market energy price at time t; p g The price of gas purchased from the upper power grid for the integrated energy system is set at 2.55 yuan / kJ; The amount of gas purchased by the integrated energy system from the upper power grid at time t; is the carbon trading cost of the comprehensive energy system at time t; p IDR The cost coefficient for invoking comprehensive demand response is set to 0.48 yuan / kW; is the electric load response at time t; is the heat load response at time t; is the load reduction amount that can be reduced at time t; is the load that can be transferred at time t; is the original electrical load at time t; is the original heat load at time t; The average electric load in a day is set to 2084kW; is the average heat load in a day, set to 2059.4kW; c run is the operating cost coefficient, which is set to 0.025 yuan / kW; is the output power of equipment q in the integrated energy system at time t; T is 24 hours; Q is the number of all equipment in the integrated energy system.

[0088] The power balance constraints are as follows:

[0089]

[0090]

[0091] Where: The integrated energy system purchases and sells electricity from the upper power grid at time t; is the photovoltaic output power at time t; is the wind power output at time t; is the electric power of the cogeneration unit at time t; is the electric power of the electric boiler at time t; are the energy storage charging and discharging power at time t respectively; is the heat supply of the gas boiler at time t; is the heat supply of the electric boiler at time t; is the thermal power of the CHP unit at time t; is the charging and discharging power of the heat storage tank at time t; In response to electrical load; In response to heat load.

[0092] The operating constraints of the integrated energy system are as follows:

[0093]

[0094] Where: is the electric power of the cogeneration unit at time t; The power supplied by the gas generator set at time t; is the electric power of the waste heat generator set at time t; is the thermal power of the CHP unit at time t; β t is the heating ratio coefficient of the gas generator set at time t, which is set to 0.4; τ WHB The heating efficiency of the waste heat boiler is set to 0.8; is the heating power of the gas generator set at time t; is the thermal power of the electric boiler at time t; τ EB is the heating efficiency of the electric boiler, set to 0.8; is the heat supply of the electric boiler at time t; is the thermal power of the gas boiler at time t; τ GB is the heating efficiency of the gas boiler, set to 0.8; is the heat supply of the gas boiler at time t; is the power of the gas generator set at time t; is the gas heat conversion coefficient, set to 9.88kW / m 3 ; is the gas consumption at time t; is the waste heat power generation power; α t is the power supply ratio coefficient of the gas generator set, which is set to 0.3; δ ORC The thermoelectric conversion efficiency of the waste heat power generation device is set to 0.8; is the wind power output at time t; is the predicted wind power at time t; Power prediction for photovoltaics; is the photovoltaic output power at time t; β t Heating ratio coefficient of gas unit.

[0095] The equipment capacity constraints are as follows:

[0096]

[0097] Where: The integrated energy system purchases and sells electricity from the upper power grid at time t; is the maximum power of the tie line; is the power of the gas generator set at time t; is the maximum power of the gas unit; is the thermal power of the gas boiler at time t; is the maximum power of the gas boiler; is the thermal power of the electric boiler at time t; is the maximum power of the electric boiler; is the electric power of the waste heat generator set at time t; is the maximum power of the waste heat power generation device; are the energy storage charging and discharging power at time t respectively; The maximum charge and discharge power of the energy storage; is the energy storage capacity at time t; The maximum capacity of electric energy storage; is the heat storage tank charging and discharging power at time t; Maximum charging and discharging power of the heat storage tank; is the capacity of the heat storage tank at time t; The maximum capacity of the heat storage tank.

[0098] A low-carbon optimal scheduling model for an integrated energy system with load response under the influence of flexible energy pricing is solved using an optimistic action-critic deep reinforcement learning algorithm. Optimistic action-critic deep reinforcement learning uses an agent to explore the environment through trial and error. The environment provides feedback, rewarding the agent to correct its action strategy, thereby maximizing the cumulative reward. Therefore, the interaction between the agent and the environment can be described by a Markov decision process, whose decision quantifier consists of five elements: (S, A, R, P, γ). S represents the state space, R represents the reward value, A represents the action space, P represents the state transition probability, and γ represents the discount factor.

[0099] r t =-φC t +ε

[0100] Where: φ is the coefficient of the total operating cost of the system; ε is the parameter for the reward function to regress to a positive value; C t is the target cost function at time t; r t is the reward function at time t.

[0101]

[0102]

[0103] Where: A t Action space for IES operation; The integrated energy system purchases and sells electricity from the upper power grid at time t; is the wind power output at time t; is the photovoltaic output power at time t; is the electric power of the cogeneration unit at time t; is the electric power of the electric boiler at time t; are the energy storage charging and discharging power at time t respectively; is the thermal power of GB at time t; is the thermal power of EB at time t; is the thermal power of the CHP unit at time t; is the charging and discharging power of the heat storage tank at time t; In response to electrical load; is the response heat load; δ is the time difference error, which refers to the difference between the exploration strategy and the target strategy; μ E is the mean of the probability distribution of the exploration strategy; Σ E To explore the covariance of the policy probability distribution, Σ T =Σ E , the mean and covariance obtained in this formula should satisfy the KL constraint, making the algorithm update more stable; is the Q function neural network in the state action pair (s t ,a t The upper limit of Q value under ) is fitted by a linear function according to Taylor's theorem; s t is the cross-sectional state at time t; a t is the cross-sectional action at time t; S t It is the IES running state space; are the original electric and thermal loads at time t respectively; They are respectively the wind power and photovoltaic output predicted at time t; represents the KL constraint; is a normal distribution; μ, Σ are the mean and variance of the normal distribution respectively; is subject to the mean μ T , variance Σ T Normal distribution of μ T ,Σ T are the covariances representing the mean of the current target strategy and the probability distribution of the exploration strategy respectively; for Obey normal distribution; a is the cross-sectional action;

[0104]

[0105] Where: π E and π T The covariance of is the same, that is: Σ T =Σ E , but their means are different, which makes π E The exploration will not sample on both sides of the mean of the target strategy with the same probability, thus avoiding repeated exploration; a is the cross-section action; Q UB is the Q function neural network in the state action pair (s t ,a t ) under the upper limit of Q value; s t is the cross-sectional state at time t; a t is the cross-sectional action at time t; Local strategy when representing a new state; Represents Q UB Find the partial differential with respect to a; π E and π T They represent the optimistic exploration strategy and the optimistic exploration strategy of the maximum local strategy respectively;

[0106] Furthermore, to analyze the impact of the proposed model on the carbon emissions and economic benefits of the integrated energy system under the spot and long-term carbon emission trading markets, this paper sets up three scenarios for comparative analysis, namely:

[0107] Scenario 1: Under the influence of energy demand and carbon trading, energy demand affects the internal pricing of the integrated energy system, and the integrated energy system optimizes scheduling by forming a response elasticity matrix based on time-of-use electricity prices;

[0108] Scenario 2: Under the influence of carbon trading, internal pricing is implemented in the integrated energy system, and the optimal scheduling of the integrated energy system is formed based on the response elasticity matrix based on the time-of-use electricity price;

[0109] Scenario 3: Without considering the impact of energy demand on the internal pricing of the integrated energy system under the influence of carbon trading, the integrated energy system is optimized for scheduling based on the response elasticity matrix formed by time-of-use electricity prices;

[0110] According to the above three scenarios, the scheduling results of the integrated energy system are analyzed.

[0111] In order to verify the effectiveness and applicability of the proposed model, an operation model of the integrated energy system was planned, e.g. Figure 2 As shown. The integrated energy system's electricity and heat energy complement each other significantly, meeting the diverse energy utilization needs of various loads. This invention constructs an integrated energy system architecture with price-driven demand response, in which electricity is stably supplied by the upper-level power grid. Coal purchased by the integrated energy system is primarily used to operate the combined heat and power (CHP) unit and coal-fired boiler, while the remaining electricity is sold to the upper-level power grid for profit. The combined heat and power (CHP) unit, electric boiler (EB), and gas boiler (GB) in the integrated energy system are energy-coupled devices that meet the energy requirements of the integrated load through heat-to-electricity conversion. The CHP unit consists of a low-temperature waste heat power generation unit based on an organic Rankine cycle, a gas-fired unit (GB), and a waste heat boiler (WHB). This configuration achieves thermal and electrical decoupling in the integrated energy system, achieving efficient energy utilization and adaptability to different operating conditions of the integrated energy system. Furthermore, the EB unit can be responsible for absorbing wind power (WT) and photovoltaic (PV) power and providing some heating. This invention uses a 24-hour cycle and a 1-hour step size for the solution. The equipment parameters are shown in Table 1. The transaction price between the integrated energy system and the energy market adopts time-of-use energy price, as shown in Table 2.

[0112] Table 1. Operating parameters of integrated energy system

[0113]

[0114] Table 2 Time-of-use energy prices in the energy market

[0115]

[0116] The operating costs and carbon emissions of the integrated energy system are shown in Table 3.

[0117] Table 3 Operation costs of integrated energy systems under scenarios 1-3

[0118] Scenario Carbon trading cost / yuan Scheduling cost / yuan Total operating cost / yuan 1 9481 4592 35650 2 10181 4873 38034 3 9829 5630 37360

[0119] The operational results in Table 3 show that Scenario 1 reduces carbon trading costs, scheduling costs, and total operating costs compared to Scenario 2 and Scenario 3. Scenario 1's carbon trading costs are 7.38% lower than Scenario 2 and 3.67% lower than Scenario 3. Scenario 1's total operating costs are 6.89% lower than Scenario 2 and 4.80% lower than Scenario 3.

[0120] analyze Figure 3 It can be seen that under the influence of energy demand and carbon trading, the response electricity price of the integrated energy system has been significantly adjusted compared to the time-of-use electricity price. The response electricity price within the integrated energy system is relatively low during the period of 1:00-8:00. Driven by the response electricity price, the electricity load has significantly increased in power consumption, which makes effective use of the surplus power generation of the integrated energy system. The response electricity price within the integrated energy system is at a relatively high level during the period of 8:00-12:00. At the same time, this period is also a peak period of electricity consumption. Driven by higher electricity prices, users' power consumption is greatly reduced, so that the power system of the integrated energy system will not be overloaded, ensuring the reliable power supply of the system. The period of 19:00-21:00 is another peak period of electricity consumption. Under the higher response price, the load has a significant reduction effect. After the previous peak of electricity consumption, the response electricity price also dropped rapidly. Since the electricity demand is relatively small in the middle of the night, the load has only increased slightly. Analysis Figure 4 It can be seen that since the heat consumption period is generally distributed during the day, the response price is higher during the day, which guides the heat load response. Since the energy necessity of the heat load is relatively high, the response amplitude of the heat load is not very large.

[0121] analyze Figure 5-6 As can be seen, energy consumption significantly increased between midnight and 8:00 AM and between 9:00 PM and midnight, driven by lower integrated energy prices. In the other two periods, higher integrated energy prices led to a reduction in energy load. Compared to the integrated energy response in Scenario 1, this scenario exhibited a smaller integrated energy response. From 12:00 PM to 4:00 PM, the integrated energy system's energy load barely responded, leaving energy demand at a high level. Load reduction was not effective, significantly increasing the pressure on the integrated energy system's energy supply.

[0122] analyze Figure 7-8It can be seen that the use of comprehensive energy is basically the same as the original comprehensive energy load. Considering the response price of energy demand, the effect of guiding the comprehensive energy demand response is obviously not high, and the response amount of comprehensive energy is small, which is quite different from Scenario 1 and Scenario 2.

[0123] By applying the above technical solution, it can be seen that the present invention flexibly defines the internal energy price of comprehensive energy based on carbon trading and energy demand, normalizes the internal price as a response elasticity factor, and constructs an integrated energy system optimization model with flexible pricing to guide comprehensive demand response; simulation also further verifies that the proposed method improves the economy and carbon emission level of the integrated energy system while improving the comprehensive demand response of the integrated energy system.

[0124] According to the second aspect of an embodiment of the present invention, a low-carbon optimization scheduling system for an integrated energy system with load response under the influence of flexible energy pricing is provided, comprising: a module for quantifying carbon emissions of network nodes in real time using carbon flow tracking; a module for pricing integrated energy based on carbon trading costs and load energy demand, and constructing an integrated energy price model; a module for establishing an integrated demand response model with time-of-use electricity prices as a benchmark value to normalize the internal prices of the integrated energy system; and a module for solving the constructed low-carbon optimization scheduling model for an integrated energy system with load response under the influence of flexible energy pricing.

[0125] The specific embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the knowledge of ordinary technicians in this field without departing from the scope of the present invention.

Claims

1. A low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing, characterized by: include: Step 1: Use carbon flow tracking to quantify the carbon emissions of network nodes in real time; Step 2: Price the comprehensive energy based on carbon trading costs and load energy demand, and build a comprehensive energy price model; Step 3: Establish a comprehensive demand response model for the internal price of the integrated energy system using the time-of-use electricity price as the benchmark value; Step 4: Solve the constructed low-carbon optimal dispatch model of the integrated energy system with load response under the influence of flexible energy pricing; Step 1 includes: S1.1: Establish a carbon trading model based on changes in system carbon emissions; S1.2: Establish a carbon flow tracking node measurement model; S1.3: Based on the carbon trading model based on system carbon emissions changes and the carbon flow tracking node measurement model, establish a carbon flow tracking node cost model for carbon trading participation; The carbon flow tracking node cost model for the carbon trading is expressed as: Where: is the carbon cost of node j at time t; is the power flowing from the i-th branch into node j at time t; ρ j,j is the carbon flow density of branch j connected to node n; is the output power of the unit x connected to node j at time t; e x,j is the carbon emission intensity of unit x connected to node j; I is the number of node branches; X is the number of units connected to the node; is the carbon trading cost of the integrated energy system at time t; is the carbon transaction cost under carbon flow tracking at time t; p e,c 、p h,c Incentive prices for carbon reduction for electricity and heat units; is the load increase and decrease of node j in response to carbon reduction at time t; is the load increase and decrease of node j’s heat load in response to carbon reduction at time t; are the carbon costs of electricity and heat supply at node j at time t, respectively; J is the number of nodes; is the original electrical load at time t; is the original heat load at time t; The comprehensive energy price model is expressed as follows: Where: Pricing the electricity of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t the carbon trading costs of obtaining electricity; is the energy demand elasticity price at time t Electricity load demand elasticity in price; Pricing the heat energy of the integrated energy system at time t; is the carbon transaction cost traced by carbon flow at time t Obtain carbon trading costs for heating; is the energy demand elasticity price at time t Heat load demand elasticity in price; The comprehensive demand response model is expressed as: Where: is the load response at time t; E is the response elasticity matrix; ΔP t is the initial response at time t; where: e t,t is the elasticity factor of the response elasticity matrix; U t Energy pricing for the integrated energy system at time t; p t is the energy price in the market at time t.

2. The low-carbon optimization scheduling method for integrated energy systems with load response under the influence of flexible energy pricing according to claim 1 is characterized in that: The Step 4 is specifically as follows: Establishing a low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load; wherein, the low-carbon optimization scheduling model for an integrated energy system based on the comprehensive demand response of the integrated energy system load includes an objective function and constraint conditions; The optimistic action-criticism deep algorithm is used to solve the low-carbon optimal scheduling model of the integrated energy system with load response under the influence of flexible energy pricing.

3. The low-carbon optimization scheduling method for integrated energy systems with load response under the influence of flexible energy pricing according to claim 2 is characterized in that: The objective function is expressed as: minC=C Buy +C CET +C IDR +C SCH +C RUN Where: C is the total cost of the integrated energy system; C Buy is the energy purchase cost; C CET is the carbon trading cost; C IDR is the system IDR cost; C SCH is the system scheduling cost; C RUN The operating cost of the integrated energy system.

4. The low-carbon optimization scheduling method for an integrated energy system with load response under the influence of flexible energy pricing according to claim 3 is characterized in that: The constraints include power balance constraints, integrated energy system operation constraints, and equipment capacity constraints.

5. A low-carbon optimization scheduling system for an integrated energy system that implements load response under the influence of flexible energy pricing according to the method of claim 1, characterized in that: include: A module for quantifying the carbon emissions of network nodes in real time using carbon flow tracking; A module for pricing comprehensive energy based on carbon trading costs and load energy demand, and building a comprehensive energy price model; A module for establishing a comprehensive demand response model with time-of-use electricity prices as the benchmark value to normalize the internal price of the integrated energy system; A module used to solve a low-carbon optimal dispatch model for integrated energy systems that considers load response under the influence of flexible energy pricing.

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