Energy system optimization method and device based on offline domain and online domain optimization

Abstract

The invention discloses an energy system optimization method and device based on offline domain and online domain optimization, and the method comprises the steps: constructing a state sensing module which is used for monitoring the operation state and environment change of a multi-energy system in real time, and providing instant data support for the optimization decision of an RIES system and a CIES system; a security-economy optimization module is constructed, and an energy scheduling and resource allocation scheme optimization model is constructed in combination with a Stackelberg game and a cooperative game strategy; constructing an off-line domain generation module, constructing a matrix form of the energy scheduling and resource allocation scheme optimization model based on a multi-parameter planning theory, analyzing correlation of scenes, iterations and model parameters in the CIES system, and generating a scene domain, an iteration domain and a model domain of an optimization problem based on a critical region; the method comprises the following steps: constructing an online domain optimization module, matching a corresponding region, solving through a linear equation when identifying that the running state of the current CIES system is in a certain critical region, and determining an energy scheduling and resource allocation scheme based on a linear equation solving result of the online domain optimization module.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy systems, and in particular to an energy system optimization method and device based on offline domain and online domain optimization. Background Art

[0002] Regional Integrated Energy Systems (RIES) integrate various energy sources (including electricity, heat, and natural gas) within a region to promote coordinated planning and optimized operation of energy subsystems. Community Integrated Energy Systems (CIES) focus on optimizing energy utilization within the community, improving energy efficiency, and ensuring reliable and secure energy supply at a more localized scale. These two systems are critical to improving overall energy efficiency, reducing environmental impact, and ensuring stable operation of energy systems.

[0003] RIES and CIES are operated by different entities, each of which makes optimal decisions based on different objectives. Game theory is widely used to solve multi-agent, multi-objective optimization problems. The interaction between regional operators and CIES is usually modeled as a leader-follower strategy. References [1] and [2] proposed a Stackelberg game strategy based on KKT conditions to solve the two-tier pricing model between the Distribution System Operator (DSO) and CIES, and a unique equilibrium solution was proved in reference [3]. In addition, energy trading in RIES usually involves multiple CIES. From the perspective of cooperative game theory, Shapley value is used for CIES optimization modeling. [4][5] , and from a non-cooperative perspective, the Stackelberg game solves the problem of unequal trade [6][7] However, current hybrid game strategies often simplify energy operators to pure grids, ignoring the role of gas and heat networks. In addition, if RIES fails, load shedding can be mitigated by joint optimization of RIES and CIES. [8] These issues have not yet been incorporated into existing energy trading strategies.

[0004] The hybrid game framework has matured to a large extent, but the computational complexity of the solution algorithm remains a significant bottleneck. Distributed algorithms such as the Alternating Direction Method of Multipliers (ADMM) are often used to solve cooperative and non-cooperative games in hybrid game problems involving multilinear models. [9]Traditionally, the leader-follower game model is solved by converting it into a single-level problem by applying the KKT condition

[10] However, this process can be time-consuming and memory-intensive, especially in the case of large models. As a result, researchers are increasingly turning to heuristic algorithms

[11] . Although these methods are promising, they are usually heavily dependent on many hyperparameters and are prone to getting stuck in local optima. Some methods combine optimization techniques with heuristic algorithms. For example, reference

[12] proposed a hybrid algorithm that uses a genetic algorithm nested in a quadratic programming. Although this approach solves the local optimality problem, it still suffers from a heavy computational time burden.

[0005] References

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[0008] [3]Zafar S, Blavette A, Camilleri G, et al. Decentralized optimal management of a large-scale EV fleet: optimality and computational complexity comparison between an Adaptive MAS and MILP. International Journal of Electrical Power & Energy Systems, 2023,147:108861.

[0009] [4]Cao M,Shao C,Hu B,et al.Reliability tracing of the integratedenergy system using the improved shapley value.Energy,2022,260:124997.

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[0013] [8]Yan M,He Y,Shahidehpour M,et al.Coordinated regional-districtoperation of integrated energy systems for resilience enhancement in naturaldisasters.IEEE Transactions on Smart Grid,2018,10(5):4881-4892.

[0014] [9]Zhao B,Duan P,Fen M,et al.Optimal operation of distributionnetworks and multiple community energy prosumers based on mixed gametheory.Energy,2023,278:128025.

[0015]

[10] Lei Z,Liu M,Shen Z.Analysis of strategic bidding of a DERaggregator in energy markets through the Stackelberg game model with themixed-integer lower-level problem.International Journal of Electrical Power&Energy Systems,2023,152:109237.

[0016]

[11] Liang Z,Mu L.Multi-agent low-carbon optimal dispatch of regionalintegrated energy system based on mixed game theory.Energy,2024,295:130953.

[0017]

[12] Li K, Ye N, Li S, et al. Distributed collaborative operation strategies in multi-agent integrated energy system considering integrated demand response based on game theory. Energy, 2023, 273:127137. Summary of the invention

[0018] The present invention provides an energy system optimization method and device based on offline domain and online domain optimization. The present invention improves the economy and safety of the park integrated energy system and the regional integrated energy system, and improves the optimization scheduling efficiency. See the following description for details:

[0019] In a first aspect, an energy system optimization method based on offline domain and online domain optimization comprises:

[0020] Build a state perception module to monitor the operating status and environmental changes of multi-energy systems in real time, and provide instant data support for RIES and CIES system optimization decisions;

[0021] Construct a safety-economy optimization module, combine Stackelberg game and cooperative game strategies, and build an energy scheduling and resource allocation optimization model;

[0022] Construct an offline domain generation module, construct a matrix form of the energy scheduling and resource allocation scheme optimization model based on multi-parameter planning theory, analyze the correlation between scenarios, iterations and model parameters in the CIES system, and generate scenario domains, iteration domains and model domains of the optimization problem based on critical regions;

[0023] An online domain optimization module is constructed to identify the scenario domain, iteration domain, and model domain corresponding to the current operating state according to the real-time data of the state perception module during the operation of the CIES system. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, the linear equation is solved, and the energy scheduling and resource allocation schemes are determined based on the linear equation solution results of the online domain optimization module.

[0024] Wherein, the offline domain generation module is:

[0025] Iteration Dependencies:

[0026] min f=(c+△c) T x

[0027] stAx=bx≥0

[0028] Where Δc is a random variable related to c;

[0029] Time Dependency:

[0030] min f=c T x

[0031] stAx=b+△bx≥0

[0032] Where Δb is a random variable related to b;

[0033] Model Dependencies:

[0034] min f=c T x

[0035] st(A+△A)x=bx≥0

[0036] Where ΔA is a random variable related to A;

[0037] Formulate the iteration domain R that relates CIES to Δc, Δb, and ΔA c , scene domain R b , model domain R A It is expressed as follows:

[0038] R c ={(△c)|c N +△c N =A N T (A B -1 ) T (c B +△c B )}

[0039] R b ={(△b)|A B -1 (b+△b)≥0}

[0040]

[0041] Among them, B is the critical area activation mark, N is the non-critical area mark; c N represents the non-critical region vector corresponding to c; A N represents the non-critical region matrix corresponding to A; c B represents the critical region vector corresponding to c; A B represents the critical region matrix corresponding to A; △c N represents the non-critical region vector corresponding to Δc; ΔA N represents the non-critical area matrix corresponding to ΔA; △cB represents the critical region vector corresponding to Δc; ΔA B Represents the critical region matrix corresponding to ΔA.

[0042] Wherein, the online domain optimization module is:

[0043] Iterative correlation criterion:

[0044]

[0045] Time correlation criterion:

[0046]

[0047] Model similarity criterion:

[0048] c N T -c B (A B +△A B ) -1 (A N +△A N )≤0

[0049] (A B +△A B ) -1 b≥0

[0050] The offline domain space consists of several critical regions, each of which contains several optimization problems with the same parameters B and N. The mapping relationship between the optimization problem and the critical region is constructed in the online domain optimization module to determine the corresponding critical region of the current optimization problem in the offline domain generation module.

[0051] In a second aspect, an energy system optimization device based on offline domain and online domain optimization, the device comprises: a processor and a memory, wherein program instructions are stored in the memory, and the processor calls the program instructions stored in the memory to enable the device to perform the following operations:

[0052] Real-time monitoring of the operating status and environmental changes of multi-energy systems, providing instant data support for RIES and CIES system optimization decisions;

[0053] Combining Stackelberg game and cooperative game models, an energy scheduling and resource allocation optimization model is constructed;

[0054] Based on the multi-parameter planning theory, the matrix form of the energy scheduling and resource allocation scheme optimization model is constructed, the scenarios, iterations and correlations of model parameters in the CIES system are analyzed, and based on the critical regions, the scenario domain, iteration domain and model domain of the optimization problem are generated;

[0055] According to the real-time data of the state perception module, the scenario domain, iteration domain and model domain corresponding to the current operating state are identified. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, a linear equation is used to solve it.

[0056] Based on the linear equation solution results of the online domain optimization module, the energy scheduling and resource allocation schemes are determined.

[0057] In a third aspect, a computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes the method according to any one of the claims in the first aspect.

[0058] The beneficial effects of the technical solution provided by the present invention are:

[0059] 1) Improve the operational reliability and economy of multi-energy systems: By establishing a reliable and economical multi-energy system optimization model and combining the strategies of offline domain generation and online domain optimization, the system's ability to operate stably in complex environments can be improved;

[0060] 2) Improve the solution efficiency of the optimization model: Through the strategy of offline domain generation and online domain optimization, the amount of online calculations can be reduced, and complex optimization problems can be converted into linear solutions to further improve the solution efficiency; especially in large-scale energy systems, multiple types of problems can be quickly solved to adapt to the dynamic changes of energy systems;

[0061] 3) Realize the coordinated optimization of multiple systems: Combining Stackelberg game and cooperative game, an energy support strategy based on price incentives is developed, which enables CIES to form a micro energy network in fault scenarios and provide load support for RIES, enhancing the system's adaptability in sudden faults or extreme conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of an energy system optimization method based on offline domain and online domain optimization;

[0063] Figure 2 Schematic diagram of energy transaction between CIES under fault conditions;

[0064] Figure 3 This is a comparison chart of calculation time. DETAILED DESCRIPTION

[0065] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention are described in further detail below.

[0066] Example 1

[0067] The embodiment of the present invention provides an energy system optimization method based on offline domain and online domain optimization, the method comprising: a state perception module, an offline domain generation module, an online domain optimization module and a critical area fast solution module.

[0068] Build a state perception module to monitor the operating status and environmental changes of multi-energy systems in real time, and provide instant data support for RIES and CIES system optimization decisions;

[0069] Construct a safety-economy optimization module, combine Stackelberg game and cooperative game strategies, and build an energy scheduling and resource allocation optimization model;

[0070] Construct an offline domain generation module, construct a matrix form of the energy scheduling and resource allocation scheme optimization model based on multi-parameter planning theory, analyze the correlation between scenarios, iterations and model parameters in the CIES system, and generate scenario domains, iteration domains and model domains of the optimization problem based on critical regions;

[0071] An online domain optimization module is constructed to identify the scenario domain, iteration domain, and model domain corresponding to the current operating state according to the real-time data of the state perception module during the operation of the CIES system. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, the linear equation is solved, and the energy scheduling and resource allocation schemes are determined based on the linear equation solution results of the online domain optimization module.

[0072] In summary, the energy system optimization method based on offline domain and online domain optimization provided by the embodiment of the present invention ensures that the RIES and CIES systems can efficiently, economically and reliably cope with complex environments and dynamic changes during operation by generating critical areas offline and combining online optimization strategies.

[0073] Example 2

[0074] The scheme in Example 1 is further introduced below in combination with specific calculation formulas and examples, as described below for details:

[0075] 1. State perception module

[0076] Among them, the integrated energy system state perception module includes two parts: 1) RIES state perception submodule; 2) CIES state perception submodule.

[0077] 1) RIES state perception submodule

[0078] The distribution network uses the Distflow model to achieve state awareness.

[0079]

[0080] Where t is the time index; Ω b is a node set; P t,ij and Q t,ij Represents the active and reactive power of branch ij; r ij and x ij Represents the resistance and reactance of branch ij; V t,i and V t,j They represent the square values ​​of the voltage amplitudes at nodes i and j at time t; l t,ij is the branch current amplitude; P t,i is the active power of the load carried by node i; Q t,i is the reactive power of the load carried by node i; Q t,jk is the reactive power of branch jk; P t,jk is the active power of branch jk; k is all load nodes connected to node j.

[0081] The natural gas network achieves state perception based on node pressure equations and pipeline flow equations.

[0082]

[0083] pf i =ρ·pf j (6)

[0084] A G ·F G -G l ·D l -S=F L (7)

[0085] Among them, F ij Indicates the natural gas flow rate; c ij is the pipeline parameter; pf i and pf i Represents the gas pressure at nodes i and j respectively. ρ represents the compression ratio; A G is the node-branch correlation matrix of the natural gas system; F G represents the branch flow matrix; G l is the compressor node association matrix; D l Represents the compressor consumption flow; S and F L Represent the gas source output and node load respectively.

[0086] The thermal system achieves state perception based on hydraulic model and thermal model.

[0087] A H m=m q (8)

[0088] Bh f=BKm|m|=0 (9)

[0089] Η L =C p m q (T s -T o ) (10)

[0090]

[0091] (∑m out )T end =∑(m in T start ) (12)

[0092] Among them, A H is the node-branch association matrix; m represents the mass flow rate vector; m q is the injection flow vector at the system node. f is the vector of thermal pressure loss; B is the branch correlation matrix; K represents the resistance coefficient of the thermal pipeline. L Indicates thermal power, C p represents the specific heat capacity of water; T s and T o Corresponding to the injection and outflow temperatures at the node, respectively. T end and T start are the inlet and outlet temperatures of the pipe respectively. The ambient temperature is T a represents, λ represents the heat transfer coefficient per unit length of the pipeline; m out and m in They represent the mass flow rate of the pipe when leaving or entering the mixing node, L represents the pipe length, and m represents the mass flow scalar parameter.

[0093] 2) CIES state perception submodule

[0094] The state perception model of ground source heat pump, air source heat pump, gas boiler, gas turbine, compressed air chiller, and electric chiller can be expressed as:

[0095]

[0096]

[0097] Where P, H and C represent the power of electricity, heat and cold respectively; F represents the flow rate of natural gas; η ASHP , η GHP , η GB , η GT , η AC and η EC Both represent energy conversion coefficient; q gasis the calorific value of natural gas; t is the time index; GHP is ground source heat pump; ASHP is air source heat pump; GB is gas boiler; GT is gas turbine; AC is compressed air chiller; EC is electric chiller.

[0098] The state perception model of extraction condensing combined heat and power (CHP) unit can be expressed as:

[0099]

[0100] Among them, Z CHP It is the ratio of the change in electrical output power to the thermal output power. represents the power of the extraction condensing cogeneration unit in full condensing mode; η g2e is the efficiency of CHP in converting natural gas into electricity; η CHP Represents the overall efficiency of CHP; is the fuel consumption flow of the CHP at time t; F CHP is the rated fuel consumption flow of the cogeneration unit; is the output power of the cogeneration unit at time t; is the output thermal power of the cogeneration unit at time t.

[0101] 2. Safety-Economic Optimization Module

[0102] 1) RIES optimization submodule

[0103] As the leader of the Stackelberg game, RIES aims to maximize its benefits by trading energy with each lower-level CIES and ensure reliable and safe operation. The objective function of RIES is expressed as follows:

[0104]

[0105] in, and Representing economic benefits and reliability costs respectively, they can be calculated using the following equations:

[0106]

[0107] Where n is the index of CIES; t is the time step; k represents the type of energy; and e represents the type of load demand. b and s represent the identifiers of CIES buying and selling energy to RIES, respectively, while B and S represent whether RIES buys or sells energy to the superior energy network. and represents the energy price, and represents the energy transaction volume, represents the reliability cost of load type e, Indicates the amount of load reduction.

[0108] 2) CIES optimization submodule

[0109] The optimization of CIES aims to minimize the total operating cost. The objective function is expressed as follows:

[0110]

[0111] in, represents the transaction costs associated with RIES; represents the energy transaction cost between multiple CIES; It refers to the operating cost of the equipment within CIES n; represents the costs associated with IDR; Corresponding to the energy transmission cost between multiple CIES. Represents the carbon emission cost of CIES n.

[0112] The specific expressions for each cost are provided in the equations below:

[0113]

[0114] Here, m is the index of CIES except CIES n. They are the energy purchase and sale prices between CIES; are the energy purchase and sale between CIES respectively; is the transmission cost coefficient of the energy interconnection line; v dp and v dg Represent the operating cost coefficients of electrical equipment and gas equipment respectively. dp,n,t and G dg,n,t are the output power of electric equipment and gas equipment respectively; is the transferable load cost of load e; and are the increase and decrease of the transferred load at time t respectively; u Ca Represents the carbon price. and They represent the actual carbon emissions and carbon emission quota of CIES n respectively.

[0115] 3. Offline Domain Generation Module

[0116] The proposed safety-economic optimization module is divided into two stages: RIES and CIES safety operation optimization based on Stackelberg game, and CIES cooperative game economic optimization. For the sake of clarity, the underlying optimization problem of the Stackelberg game model is first expressed in matrix form.

[0117]

[0118] Where T is the transposed symbol; A and b are the technical coefficient matrix and right-hand side vector of model (32), respectively, determined by (13)-(21); c is the cost vector, determined by equations (25), (26)-(31); x is the optimization variable, and f is the objective function.

[0119] Three parameter dependencies are defined:

[0120] 1. Iteration correlation: In each iteration, the upper model converts different energy price vectors and is transmitted to the lower model, resulting in a change in c. Therefore, the iterative similarity model can be expressed as:

[0121]

[0122] where Δc is a random variable associated with c.

[0123] 2. Temporal correlation: In different time frames, changes in load levels and renewable energy output will cause changes in parameter b. Therefore, the temporal similarity model can be further refined.

[0124]

[0125] where Δb is a random variable associated with b.

[0126] 3. Model relevance: In different CIES, the differences in equipment capacity and equipment technical parameters will cause the parameter A to change. Therefore, the model similarity can be expressed as:

[0127]

[0128] where ΔA is a random variable associated with A.

[0129] The critical region (CR) is defined as a set of random variables that remain constant with respect to the optimal basis (i.e., the current set of active constraints). In CIES, the A matrix is ​​kept fixed over time, ensuring that the dimensionality of the optimization model remains consistent. Therefore, in the offline phase, the critical region R of CIES with respect to Δc, Δb, and ΔA can be formulated. c , R b , R A It is expressed as follows:

[0130] R c ={(△c)|c N +△c N =A N T (AB -1 ) T (c B +△c B )} (36)

[0131] R b ={(△b)|A B -1 (b+△b)≥0} (37)

[0132]

[0133] Among them, B is the critical area activation mark, N is the non-critical area mark; c N represents the non-critical region vector corresponding to c; A N represents the non-critical region matrix corresponding to A; c B represents the critical region vector corresponding to c; A B represents the critical region matrix corresponding to A; △c N represents the non-critical region vector corresponding to Δc; ΔA N represents the non-critical area matrix corresponding to ΔA; △c B represents the critical region vector corresponding to Δc; ΔA B Represents the critical region matrix corresponding to ΔA.

[0134] 4. Online Domain Optimization Module

[0135] Once the CR is generated, the optimal strategy can be determined by matching different optimization problems with the corresponding critical regions and converting the optimization problems into equations for solution. The following are the matching criteria for the optimization problems in three critical regions:

[0136] 1. Iterative correlation criterion:

[0137] (c N +△c N ) T -(c B +△c B )A B -1 A N ≤0 (39)

[0138] 2. Time correlation criteria:

[0139] A B -1 (b+△b)≥0 (40)

[0140] 3. Model similarity criteria:

[0141] c N T -cB (A B +△A B ) -1 (A N +△A N )≤0 (41)

[0142] (A B +△A B ) -1 b≥0 (42)

[0143] The parameter space consists of several critical regions, each of which contains several optimization problems with the same parameters B and N. Therefore, in the online operation phase, based on the online domain optimization module, a mapping relationship between the optimization problem and the critical region is constructed, that is, the critical region where the current optimization problem is located in the offline domain generation module is determined, and then in the online phase, the efficiency of solving the optimization problem is significantly accelerated.

[0144] Example 3

[0145] The best implementation of the embodiment of the present invention relates to an energy system optimization device based on offline domain and online domain optimization, which aims to achieve efficient, intelligent and safe operation of the energy system through a highly integrated modular design. The implementation includes the following key components:

[0146] State perception module: This module provides real-time data support by monitoring the operating status and environmental changes of the RIES and CIES systems. It includes two sub-modules: RIES and CIES. The RIES sub-module senses the status of each energy subsystem based on the Distflow model of the distribution network, the pressure and flow equations of the natural gas network, and the hydraulic and thermal models of the thermal system. The CIES sub-module monitors the energy conversion status of various equipment (for example, heat pumps, gas boilers, gas turbines, etc.). Data is transmitted to the data storage module in real time through sensors for subsequent optimization decisions.

[0147] Safety-Economy Optimization Module: This module combines Stackelberg game and cooperative game models to optimize energy transactions between RIES and CIES. In the RIES optimization submodule, RIES, as the leader, selects the best energy scheduling solution through game with CIES to ensure the safe operation of the RIES system and improve economic benefits. The CIES optimization submodule further optimizes energy use by reducing operating costs and carbon emissions. This module ensures that the RIES and CIES systems respond to faults or extreme events in real time during dynamic operation.

[0148] Offline domain generation module: This module generates the critical region of the CIES system operation based on the state similarity method by analyzing the changes in iteration and timing parameters in the offline stage. By dividing the scene, iteration, and model parameters into different domains, the scene domain, iteration domain, and model domain are generated, covering multiple optimization problems in the CIES system. Once the critical region is generated, the optimization problem can be quickly solved by linear equations, reducing the complexity of online optimization.

[0149] Online domain optimization module: During the operation of the CIES system, this module simplifies complex optimization problems into linear solutions by identifying the critical region to which the current state belongs. When the system is identified as being in a critical region, it directly matches the corresponding critical region and quickly obtains the optimal solution through a linear equation group without the need for optimization operations. This optimization method significantly improves the operating efficiency of the system and ensures that the energy system remains efficient and stable in real-time operation.

[0150] In this study, the RIES test system was built using a modified IEEE 33-bus power distribution system, a Belgian 20-node gas system, and a 6-node thermal system. The test system includes three CIES. Each CIES includes a ground source heat pump, an air source heat pump, a gas boiler, a gas turbine, a compressed air chiller, and an electric chiller to meet the regional demand for heat, electricity, and hydrogen. The IDR is set to 20% of the load demand.

[0151] In order to verify the effectiveness of the proposed REO-RMC framework, three scenarios are set for comparative analysis:

[0152] P1: The Stackelberg game between RIES and multiple CIES includes internal energy sharing but does not exploit cooperative games or consider demand response.

[0153] P2: The Stackelberg game between the RIES and CIES alliances includes internal energy sharing and profit allocation using cooperative game theory, but does not consider demand response.

[0154] P3: Based on P3, IDR strategy is further incorporated.

[0155] Through cooperative game among multiple RIES, the energy operation cost can be significantly reduced. Specifically, compared with P2, the operation cost of P1 is reduced by 12003.7 yuan. More precisely, the costs of CIES1, CIES2 and CIES 3 are reduced by 7702.82 yuan, 438.97 yuan and 3861.91 yuan, respectively. In P3, the operation cost is further reduced through the IDR strategy of multiple CIES. Compared with P2, the operation costs of CIES1, CIES2 and CIES 3 are reduced by 1708.15 yuan, 1074.48 yuan and 1523.47 yuan, respectively. In addition, both the cooperative game strategy and the IDR strategy improve the economic benefits of RIES and the operation cost of CIES. Compared with P1, the RIES income of P3 and P3 increased by 5.58% and 7.73%, respectively.

[0156] Table 1 Comparison of economic and technical indicators

[0157]

[0158]

[0159] (1) The energy support strategy based on price incentives developed by the embodiment of the present invention combines Stackelberg game and cooperative game, so that CIES can provide more than 50% energy support for RIES in the fault scenario, significantly improving the operational reliability of RIES. At the same time, the strategy effectively balances the interests of energy suppliers, increasing the economic benefits of RIES by 7.73% and CIES by 16.4%, and overall improving the economic benefits of RIES and CIES systems.

[0160] (2) The embodiment of the present invention combined with Stackelberg game can effectively reduce the reliability and safety risk cost of RIES by 50.23%; after introducing the price incentive strategy, the reliability and safety risk can be further reduced by about 20%; the energy sharing strategy between CIES systems can reduce the reliability risk between CIES by about 8%.

[0161] (3) The method proposed in the embodiment of the present invention reduces the computation time of the two-layer optimization problem to less than 60% of the original method. By combining the offline critical region generation with the online critical region identification framework, the solution efficiency of the proposed method is further improved by about 50%. With the increase of iterative scenarios and time series scenarios, this efficiency advantage is more obvious.

[0162] Example 4

[0163] An energy system optimization device based on offline domain and online domain optimization, the device comprises: a processor and a memory, the memory stores program instructions, the processor calls the program instructions stored in the memory to enable the device to perform the following operations:

[0164] Real-time monitoring of the operating status and environmental changes of multi-energy systems, providing instant data support for RIES and CIES system optimization decisions;

[0165] Combining Stackelberg game and cooperative game models, an energy scheduling and resource allocation optimization model is constructed;

[0166] Based on the multi-parameter planning theory, the matrix form of the energy scheduling and resource allocation scheme optimization model is constructed, the scenarios, iterations and correlations of model parameters in the CIES system are analyzed, and based on the critical regions, the scenario domain, iteration domain and model domain of the optimization problem are generated;

[0167] According to the real-time data of the state perception module, the scenario domain, iteration domain and model domain corresponding to the current operating state are identified. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, a linear equation is used to solve it.

[0168] Based on the linear equation solution results of the online domain optimization module, the energy scheduling and resource allocation schemes are determined.

[0169] Among them, the offline domain generation module is:

[0170] Iteration Dependencies:

[0171] min f=(c+△c) T x

[0172] stAx=bx≥0

[0173] Where Δc is a random variable related to c;

[0174] Time Dependency:

[0175] min f=c T x

[0176] stAx=b+△bx≥0

[0177] Where Δb is a random variable related to b;

[0178] Model Dependencies:

[0179] min f=c T x

[0180] st(A+△A)x=bx≥0

[0181] Where ΔA is a random variable related to A;

[0182] Formulate the iteration domain R that relates CIES to Δc, Δb, and ΔA c , scene domain R b , model domain R A It is expressed as follows:

[0183] R c ={(△c)|c N +△c N =A N T (A B -1 ) T (c B +△c B )}

[0184] R b ={(△b)|A B -1 (b+△b)≥0}

[0185]

[0186] Among them, B is the critical area activation mark, N is the non-critical area mark; c N represents the non-critical region vector corresponding to c; A N represents the non-critical region matrix corresponding to A; c B represents the critical region vector corresponding to c; A B represents the critical region matrix corresponding to A; △c N represents the non-critical region vector corresponding to Δc; ΔA N represents the non-critical area matrix corresponding to ΔA; △c B represents the critical region vector corresponding to Δc; ΔA B Represents the critical region matrix corresponding to ΔA.

[0187] Among them, the online domain optimization module is:

[0188] Iterative correlation criterion:

[0189]

[0190] Time correlation criterion:

[0191]

[0192] Model similarity criteria:

[0193] c N T -c B (AB +△A B ) -1 (A N +△A N )≤0

[0194] (A B +△A B ) -1 b≥0

[0195] The offline domain space consists of several critical regions, each of which contains several optimization problems with the same parameters B and N. The mapping relationship between the optimization problem and the critical region is constructed in the online domain optimization module to determine the corresponding critical region of the current optimization problem in the offline domain generation module.

[0196] It should be pointed out here that the device description in the above embodiment corresponds to the method description in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0197] The execution subjects of the above-mentioned processor and memory can be devices with computing functions such as computers, single-chip microcomputers, and microcontrollers. In specific implementation, the embodiments of the present invention do not limit the execution subjects and are selected according to the needs of actual applications.

[0198] The data signal is transmitted between the memory and the processor via a bus, which is not described in detail in the embodiment of the present invention.

[0199] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium, the storage medium includes a stored program, and when the program is running, the device where the storage medium is located is controlled to execute the method steps in the above embodiment.

[0200] The computer-readable storage medium includes but is not limited to a flash memory, a hard disk, a solid-state drive, and the like.

[0201] It should be pointed out here that the description of the readable storage medium in the above embodiment corresponds to the description of the method in the embodiment, and the embodiment of the present invention will not be described in detail here.

[0202] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the embodiments of the present invention are generated.

[0203] The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions may be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. A computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more available media integrated therein. Available media may be magnetic media or semiconductor media, etc. Except for special instructions for the models of each device in the embodiments of the present invention, the models of other devices are not limited, and any device that can perform the above functions may be used.

[0204] Those skilled in the art will appreciate that the accompanying drawing is only a schematic diagram of a preferred embodiment, and the serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0205] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An energy system optimization method based on offline domain and online domain optimization, characterized in that: The method comprises: Build a state perception module to monitor the operating status and environmental changes of multi-energy systems in real time, and provide instant data support for RIES and CIES system optimization decisions; Construct a safety-economy optimization module, combine Stackelberg game and cooperative game strategies, and build an energy scheduling and resource allocation optimization model; Construct an offline domain generation module, construct a matrix form of the energy scheduling and resource allocation scheme optimization model based on multi-parameter planning theory, analyze the correlation between scenarios, iterations and model parameters in the CIES system, and generate scenario domains, iteration domains and model domains of the optimization problem based on critical regions; An online domain optimization module is constructed to identify the scenario domain, iteration domain, and model domain corresponding to the current operating state according to the real-time data of the state perception module during the operation of the CIES system. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, the linear equation is solved, and the energy scheduling and resource allocation schemes are determined based on the linear equation solution results of the online domain optimization module.

2. The energy system optimization method based on offline domain and online domain optimization according to claim 1 is characterized in that: The offline domain generation module is: Iteration Dependencies: min f=(c+△c) T x stAx=bx≥0 Where Δc is a random variable related to c; Time Dependency: min f=c T x stAx=b+△bx≥0 Where Δb is a random variable related to b; Model Dependencies: min f=c T x st(A+△A)x=bx≥0 Where ΔA is a random variable related to A; Formulate the iteration domain R that relates CIES to Δc, Δb, and ΔA c , scene domain R b , model domain R A It is expressed as follows: R c ={(△c)|c N +△c N =A N T (A B -1 ) T (c B +△c B )} R b ={(△b)|A B -1 (b+△b)≥0} Among them, B is the critical area activation mark, N is the non-critical area mark; c N represents the non-critical region vector corresponding to c; A N represents the non-critical region matrix corresponding to A; c B represents the critical region vector corresponding to c; A B represents the critical region matrix corresponding to A; △c N represents the non-critical region vector corresponding to Δc; ΔA N represents the non-critical area matrix corresponding to ΔA; △c B represents the critical region vector corresponding to Δc; ΔA B Represents the critical region matrix corresponding to ΔA.

3. The energy system optimization device based on offline domain and online domain optimization according to claim 2 is characterized in that: The online domain optimization module is: Iterative correlation criterion: Time correlation criterion: Model similarity criteria: c N T -c B (A B +△A B ) -1 (A N +△A N )≤0 (A B +△A B ) -1 b≥0 The offline domain space consists of several critical regions, each of which contains several optimization problems with the same parameters B and N. The mapping relationship between the optimization problem and the critical region is constructed in the online domain optimization module to determine the corresponding critical region of the current optimization problem in the offline domain generation module.

4. An energy system optimization device based on offline domain and online domain optimization, characterized in that: The device comprises: a processor and a memory, wherein the memory stores program instructions, and the processor calls the program instructions stored in the memory to enable the device to perform the following operations: Real-time monitoring of the operating status and environmental changes of multi-energy systems, providing instant data support for RIES and CIES system optimization decisions; Combining Stackelberg game and cooperative game models, an energy scheduling and resource allocation optimization model is constructed; Based on the multi-parameter planning theory, the matrix form of the energy scheduling and resource allocation scheme optimization model is constructed, the scenarios, iterations and correlations of model parameters in the CIES system are analyzed, and based on the critical regions, the scenario domain, iteration domain and model domain of the optimization problem are generated; According to the real-time data of the state perception module, the scenario domain, iteration domain and model domain corresponding to the current operating state are identified. By matching the corresponding areas, when it is identified that the operating state of the current CIES system is in a critical area, a linear equation is used to solve it. Based on the linear equation solution results of the online domain optimization module, the energy scheduling and resource allocation schemes are determined.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is enabled to perform the method according to any one of claims 1 to 3.

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