Multi-agent water-wind-light short-term power generation optimization scheduling method considering space-time coupling constraint

Abstract

The invention relates to the technical field of hydropower dispatching, and discloses a multi-agent water-wind-light short-term power generation optimization dispatching method considering space-time coupling constraints. Firstly, on the basis of drainage basin characteristics, a hierarchical decomposition strategy is adopted, and overall centralized optimization solution of the water-wind-light complementary power supply system is converted into partial multi-agent collaborative optimization scheduling, so that the solution efficiency is improved; secondly, in combination with a multi-agent and unified optimization operation theory, constructing a double-layer nested short-term water consumption minimum model considering wind power and photovoltaic power prediction output, so as to realize intelligent modeling; and finally, an improved alternating direction multiplier method is adopted, and interaction among multiple agents is coordinated based on a modification strategy of a flow balance and electric quantity matching principle, so that the solving quality is improved. According to the method, the solving efficiency and precision can be effectively improved, and a solid theoretical basis and a feasible thought are provided for short-term power generation dispatching of a large-scale water-wind-light complementary power supply system.

Description

Technical Field

[0001] The present invention relates to the technical field of hydropower dispatching, and in particular to a multi-agent hydropower, wind-solar short-term power generation optimization dispatching method considering time-space coupling constraints. Background Art

[0002] In order to effectively utilize wind and solar power generation and reduce the risks and impacts of its randomness, uncertainty and volatility on the power system, a large-scale hydro-wind-solar complementary power supply system with high temporal and spatial coupling has been formed. Its temporal coupling is mainly manifested in water balance, load constraints, and flow constraints between time periods, while spatial coupling involves other related constraints such as water balance, load constraints, and basin topology within a time period. The temporal coupling constraints and spatial coupling constraints of the hydro-wind-solar complementary power supply system are interdependent and closely related. However, the refined scheduling of the power grid, the huge set of constraints and the temporal and spatial coupling constraints have brought many challenges to the short-term optimal scheduling of the hydro-wind-solar complementary power supply system, such as long solution time, complex model construction and low solution accuracy. Therefore, it is crucial to effectively handle complex constraint relationships and obtain practical and feasible scheduling results within a reasonable time range. Traditional linear programming, intelligent algorithms, dynamic programming and their improved algorithms have different degrees of limitations in solving the short-term optimal scheduling of large-scale hydro-wind-solar complementary power supply systems. Driven by actual physical needs, it is effective and necessary to adopt the "whole-part" mode to solve. "Overall" means the overall solution of the hydro-wind-solar hybrid power supply system, while "partial" decomposes the hydro-wind-solar hybrid power supply system into multiple intelligent agents for integrated solution. The alternating direction multiplier method combines the decomposability of the dual ascent algorithm and the convergence of the multiplier method, and is widely used to solve constrained separable structural optimization problems.

[0003] In view of the above problems, this paper takes a large-scale water-wind-solar complementary power supply system in a river basin in southwest China as the research object, and proposes a multi-agent water-wind-solar short-term power generation optimization scheduling method considering time-space coupling constraints. The method of the invention can effectively improve the solution efficiency and accuracy, and provide an important technical reference for the short-term power generation scheduling of other large-scale water-wind-solar complementary power supply systems in southwest my country. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-agent hydro-wind-solar short-term power generation optimization scheduling method considering time-space coupling constraints, thereby improving the timeliness of large-scale hydro-wind-solar complementary power supply systems, realizing intelligent modeling, and improving solution accuracy.

[0005] The technical solution of the present invention is:

[0006] A multi-agent short-term water-wind-solar power generation optimization scheduling method considering spatiotemporal coupling constraints is proposed. Firstly, based on the characteristics of the watershed, a hierarchical decomposition strategy is adopted to transform the "overall" centralized optimization solution of the water-wind-solar complementary power supply system into a "partial" multi-agent collaborative optimization scheduling to improve the solution efficiency; secondly, a double-layer nested short-term water consumption minimum model considering the predicted output of wind power and photovoltaic power is constructed by combining multi-agent and unified optimization operation theory to realize intelligent modeling; finally, an improved alternating direction multiplier method is adopted, and the modification strategy based on the flow balance and power matching principles is used to coordinate the interaction between multi-agents to improve the solution quality.

[0007] The specific steps include:

[0008] Step 1: Transform the hydro-wind-solar complementary power supply system into a multi-agent system based on the hierarchical decomposition strategy of watershed characteristics

[0009] (1.1) According to the output type, the hydropower-wind-solar hybrid power supply system is decomposed into hydropower intelligent body, wind power intelligent body and photovoltaic power intelligent body;

[0010] (1.2) According to the dispatching center, the hydropower stations belonging to different dispatching centers are decomposed into different intelligent agents.

[0011] (1.3) Based on the basin topology, it is assumed that the basin intersection is a new dispatching center that is independent of the existing dispatching center, and the decomposed hydropower intelligent entity is further decomposed;

[0012] (1.4) In order to further reduce the solution scale of the intelligent agent, based on the regulation performance of the hydropower station, the decomposed hydropower intelligent agent is further subdivided into small-scale intelligent agents containing a smaller number of hydropower stations.

[0013] Through the above decomposition steps, the water-wind-solar complementary power supply system is decomposed into multiple independent and smaller intelligent entities in turn.

[0014] Step 2: Wind power and photovoltaic power output forecast

[0015]

[0016] Where: is the capacity factor of wind farm g in period t; is the wind speed of wind farm g in time period t, m·s -1 ; is the coefficient of wind farm g; is the output of wind farm g in time period t, MW; is the installed capacity of wind farm g; is the capacity factor of the photovoltaic field h in time period t; is the coefficient of the photovoltaic electric field h; is the radiation of the photovoltaic electric field h in time period t, ℃; is the temperature of the photovoltaic field h in time period t, °C; is the temperature of the photovoltaic electric field h under standard test conditions, °C; is the output of the photovoltaic field h in time period t, MW. is the installed capacity of the photovoltaic field h.

[0017] Step 3: Using the hydropower, wind power and photovoltaic power intelligent entities decomposed in step 1 as dispatching units, a short-term water consumption minimization model among intelligent entities is constructed;

[0018] According to the different coupling relationships between water and electricity, the basin system can be divided into series, parallel and hybrid structures, among which the coupling relationship between hydraulic power and electricity in the hybrid structure is the most complex. The present invention models and describes four typical agents in the hybrid structure: agent A is located upstream of the tributary, agent B is located upstream of the main river, agent C is located downstream of the main river, the upstream agent of agent C is agent B, agent D is located at the lowest downstream of the main river, and the upstream agents of agent D are agents A and C. In addition, agent A, agent D, wind power agent and photovoltaic power agent are controlled by the dispatch center I, and agent B and agent C are controlled by the dispatch center II. The model is described as follows:

[0019] A. Objective Function

[0020]

[0021] Where: F is water consumption, 1×10 4 m 3 ; t is the time period number; T is the total number of time periods; Q i,t is the outbound flow of agent i in period t, m 3 / s, i = A, B, C, D; Δt is the step size of the scheduling calculation period.

[0022] B. Water Balance Constraints

[0023] a) Constraints on upstream agents (Agent A and Agent B)

[0024] V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600 (6)

[0025]

[0026] Where: V i,t is the reservoir capacity of agent i in time period t, 1×10 4 m 3 ;Ii,t is the inflow flow of agent i in period t, (m 3 / s); is the power generation flow of agent i in period t, m 3 / s;S i,t is the amount of abandoned water flow of agent i in period t, (m 3 / s); i=A,B.

[0027] b) Downstream Agent (Agent C) Constraints

[0028] V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600 (8)

[0029]

[0030] I i,t =R i,t +Q B,t (10)

[0031] Where: R i,t is the interval flow of agent i in time period t, m 3 / s, i=C.

[0032] c) Downstream Agent (Agent D) Constraints

[0033] V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600 (11)

[0034]

[0035] I i,t =R i,t +Q A,t +Q C,t (13)

[0036] Where: i=D.

[0037] C. Power Constraint: Wind power agents and photovoltaic power agents are only controlled by the dispatch center I

[0038] Load I,t =P A,t +P D,t +P t w +P t s (14)

[0039] LoadII,t =P B,t +P C,t (15)

[0040]

[0041] Where: Load I,t is the load demand of dispatch center I, MW; Load II,t is the load demand of dispatch center II, MW; P i,t is the output of agent i in period t, MW, i = A, B, C, D; P t w is the output of the wind power intelligent agent in time period t, MW; P t s is the output of the PV power intelligent entity in time period t, MW.

[0042] Step 4: Based on the wind power and photovoltaic power output predicted in step 2, and taking the hydropower station as the basic dispatching unit, a short-term water consumption minimum model within the intelligent agent is constructed;

[0043] A. Objective function:

[0044]

[0045] Where: F i is the water consumption of agent i, 1×10 4 m 3 ;Q i,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; i = A, B, C, D; j is the hydropower station number; J i is the number of hydropower stations contained in agent i.

[0046] B. Constraints:

[0047] a) Water balance equation

[0048] V i,j,t+1 =V i,j,t +(I i,j,t -Q i,j,t )×Δt×3600 (19)

[0049]

[0050] Where: V i,j,t is the reservoir capacity of hydropower station j in agent i at time period t, 1×10 4 m 3 ;I i,j,t is the inflow of hydropower station j in agent i in period t, m 3 / s;Qi,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; is the power generation flow of hydropower station j in intelligent agent i in time period t, m 3 / s;S i,j,t is the abandoned water flow of hydropower station j in intelligent agent i in time period t, m 3 / s; R i,j,t is the interval flow of hydropower station j in agent i in time period t, m 3 / s; m is the number of the power station directly upstream of hydropower station j; Ω j is the set of power stations directly upstream of hydropower station j; τ m,j is the lag time between hydropower stations m and j, h; is the intelligent agent i hydropower station m in the period t-τ m,j Outbound flow, m 3 / s.

[0051] b) Outbound flow constraints

[0052]

[0053] Where: is the upper and lower limits of the outflow flow of hydropower station j in agent i, m 3 / s.

[0054] c) Power generation flow constraints

[0055]

[0056] Where: is the upper and lower limits of the power generation flow of hydropower station j in intelligent agent i, m 3 / s.

[0057] d) Discarded water flow constraints

[0058]

[0059] Where: is the upper and lower limits of the abandoned water flow of hydropower station j in intelligent agent i, m 3 / s.

[0060] e) Water level constraints on dam

[0061]

[0062] Where: Z i,j,t is the water level above the dam of hydropower station j in agent i at time period t, m; are the upper and lower limits of the water level of hydropower station j in agent i, m.

[0063] f) Water level and storage capacity relationship curve

[0064]

[0065] Where: is the water level storage capacity curve function of hydropower station j in intelligent agent i.

[0066] g) Tailwater level discharge relationship curve

[0067]

[0068] Where: is the tailwater level of hydropower station j in agent i in time period t, m; is the tailwater level-flow relationship curve function of hydropower station j in intelligent agent i.

[0069] h) Head constraint

[0070]

[0071] Where: H i,j,t is the water head of hydropower station j in agent i at time period t, m.

[0072] i) Output constraints

[0073]

[0074] Where: P i,j,t is the output of hydropower station j in agent i in time period t, MW; are the upper and lower limits of the output of hydropower station j in agent i, in MW.

[0075] j) Power characteristic curve

[0076]

[0077] Where: It is the curve function between the output of hydropower station j in intelligent agent i and the power generation flow and water head.

[0078] k) Load constraints

[0079]

[0080] l) Initial water level constraint

[0081] Z i,j,init =Z i,j,1 (32)

[0082] Where: Z i,j,init is the initial water level of hydropower station j in agent i, m.

[0083] Step 5: Redescribe the agent-based mathematical model based on the alternating direction multiplier method;

[0084] A. Objective function:

[0085] minF=F A +F B +F C +F D (33)

[0086]

[0087]

[0088] Where: AD,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent A to agent D; is the consistent outbound flow from agent A to agent D, m 3 / s; is the consistent hydropower output from agent A to agent D, MW; ρ and ρ P is the penalty parameter for the hydraulic and electrical coupling variables; BC,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent B to agent C; is the consistent outbound flow from agent B to agent C, m 3 / s; is the consistent hydropower output from agent B to agent C, MW; λ CB,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent C to agent B; Q' B,t is the outbound flow rate that agent C expects agent B to have, m 3 / s;P' B,t is the output that agent C expects agent B to produce, MW; CD,t is the Lagrange multiplier of the outbound flow from agent C to agent D; is the consistent outbound flow from agent C to agent D, m 3 / s;λ DA,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent D to agent A; Q' A,t is the outbound flow rate that agent D expects agent A to have, m 3 / s;P' A,t is the output that agent D expects agent A to produce, MW; DC,t is the Lagrange multiplier of the outbound flow from agent D to agent C; Q' C,t is the outbound flow rate that agent D expects agent C to have, m3 / s.

[0089] B. Coupling constraints:

[0090] a) Hydraulic coupling constraints

[0091] Q A,t =Q' A,t (38)

[0092] Q B,t =Q' B,t (39)

[0093] Q C,t =Q' C,t (40)

[0094] b) Power coupling constraints

[0095] P A,t =P' A,t (41)

[0096] P B,t =P' B,t (42)

[0097] Step 6, iteratively update the hydraulic and electric coupling constraint variables, and set the tolerance values ​​of the original residual and the dual residual to stop the iteration;

[0098] A. Update of hydraulic coupling constraint variables (agents A and D):

[0099]

[0100] Where: is the outbound flow of agent A in the k+1th iteration of period t, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; is the Lagrange multiplier of the outbound flow from agent A to agent D at the kth iteration; is the outbound flow rate that agent D expects agent A to have in the k+1th iteration, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; Lagrange multiplier of the outbound flow from agent D to agent A at the kth iteration; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s.

[0101] The original residual and dual residual of hydraulic variables are expressed as:

[0102]

[0103] Where: are the original residual and dual residual of the outbound flow from agent A to agent D.

[0104] Similarly, formulas (43)-(49) can be used to update the outbound flow, expected outbound flow, consistency flow, Lagrange multipliers of the outbound flow, primal residuals and dual residuals between agent B and agent C, and to update similar variables between agent C and agent D.

[0105] B. Update of power coupling constraint variables:

[0106]

[0107] Where: is the output of agent A in the k+1th iteration of period t, WM; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent A to agent D in the kth iteration; is the output that agent D expects agent A to produce in the k+1th iteration, MW; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent D to agent A at the kth iteration; is the consistent hydropower output from agent A to agent D at the kth iteration, MW.

[0108] The original residual and dual residual of power variables are expressed as:

[0109]

[0110] Where: It is the original residual and dual residual of hydropower output from agent A to agent D.

[0111] Similarly, formulas (50)-(56) can be used to update the hydropower output, expected hydropower output, consistent hydropower output, Lagrange multiplier of hydropower output, primal residual and dual residual between agent B and agent C.

[0112] Step 7: Generate the initial consistent outbound flow and consistent output of each agent based on the heuristic algorithm;

[0113] (7.1) Formulas (57) and (58) are used to explain in detail the process of obtaining the initial load requirements of agents B and C, and formulas (18)-(32) are further used to obtain the initial consistent outbound flow.

[0114]

[0115] Where: D B,t , D C,t are the initial load requirements of agents B and C, MW; Ins B 、Ins C are the installed capacities of agents B and C, MW respectively.

[0116] (7.2) Formulas (59) and (60) are used to explain in detail the process of obtaining the initial load requirements of agents A and D, and formulas (18)-(32) are further used to obtain the initial consistent outbound flow.

[0117]

[0118] Where: D A,t , D D,t are the initial load requirements of agents A and D, MW; Ins A 、Ins D are the installed capacity of agents A and D, MW respectively.

[0119] Step 8: Based on steps 5 to 7, the optimal outbound flow and power generation process of each intelligent agent is obtained by using the alternating direction multiplier method;

[0120] Step 9: Based on the principle of water balance and power matching, on the basis of step 8, the inflow and dispatching demand of the downstream intelligent body are corrected, and finally the optimal water consumption of the water-wind-solar complementary power supply system is obtained; the specific correction steps are as follows:

[0121] (9.1) Since the upstream agent is not affected by the outflow of other agents, the output process of the upstream agent is the final optimal scheduling result, and its final optimal water consumption is and

[0122] (9.2) Modify the inflow and dispatching requirements of the downstream agent. For agent C, according to formulas (10) and (39), its water balance equation can be expressed as The scheduling demand can be expressed as follows according to formulas (15) and (42): Since the primal and dual residuals, Q' B,t ≠Q B,t and P' B,t ≠P B,tTherefore, the actual total inflow flow and actual dispatch demand can be calculated according to formulas (61) and (62).

[0123] I C,t =R C,t +Q B,t (61)

[0124] P C,t =Load II,t -P B,t (62)

[0125] (9.3) Using formulas (18)-(32), determine the final optimal outflow flow of agent C (see formula (63)) and the final optimal water consumption F C * .

[0126] Q C,t =Q * C,t (63)

[0127] (9.4) For agent D, according to formulas (13), (38) and (40), its water balance equation can be expressed as D,t =R D,t +Q' A,t +Q' C,t , the scheduling demand can be expressed as P according to formulas (14) and (41): D,t =Load I,t -P' A,t -P t w -P t s , due to the primal and dual residuals, Q' A,t ≠Q A,t , Q' C,t ≠Q C,t and P' A,t ≠P A,t Therefore, the final optimal outbound flow Q of agent C obtained based on (9.3) is * C,t , the actual total inbound flow and actual scheduling demand of agent D can be calculated according to formulas (64) and (65).

[0128] I D,t =R D,t +Q A,t +Q * C,t (64)

[0129] P D,t =Load I,t -P A,t -Pt w -P t s (65)

[0130] Step 9.5: Use formulas (18)-(32) to determine the optimal outflow flow of agent D (see formula (66)) and the final optimal water consumption

[0131] Q D,t =Q * D,t (66)

[0132] Beneficial effects of the present invention: In view of the problems of long solution time, complex model construction and low solution accuracy of large-scale water-wind-solar complementary power supply systems, the present invention proposes a multi-agent water-wind-solar short-term power generation optimization scheduling method considering time-space coupling constraints. Firstly, based on the characteristics of the watershed, a hierarchical decomposition strategy is adopted to transform the "overall" centralized optimization solution of the water-wind-solar complementary power supply system into a "partial" multi-agent collaborative optimization scheduling to improve the solution efficiency; secondly, a double-layer nested short-term water consumption minimum model considering the predicted output of wind power and photovoltaic power is constructed by combining multi-agents and unified optimization operation theory to realize intelligent modeling; finally, an improved alternating direction multiplier method is adopted, and a modification strategy based on the flow balance and power matching principles is used to coordinate the interaction between multi-agents to improve the solution quality. Compared with the prior art, the present invention can quickly and effectively solve the short-term power generation scheduling problem of large-scale water-wind-solar complementary power supply systems, and has strong practicality. BRIEF DESCRIPTION OF THE DRAWINGS

[0133] Figure 1 It is a schematic diagram of the overall solution framework of the present invention;

[0134] Figure 2 This is a schematic diagram of the topological structure of the water-wind-solar complementary power supply system;

[0135] Figure 3 It is a geographical location map of water, wind and solar power stations in a certain river basin;

[0136] Figure 4 is the convergence process of the original residual and the dual residual: (a1) is the hydraulic original residual; (a2) is the hydraulic dual residual; (b1) is the electric power original residual; (b2) is the electric power dual residual;

[0137] Figure 5 It is the detailed output process of the downstream hydropower intelligent body;

[0138] Figure 6 It is the water level change process and difference of the hydropower station before and after correction;

[0139] Figure 7It is the output process of different intelligent agents after correction: (a) is the output process of the intelligent agent in dispatching center 1; (b) is the output process of the intelligent agent in dispatching center 2. DETAILED DESCRIPTION

[0140] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0141] This embodiment mainly includes three parts: transforming the water-wind-solar complementary power supply system into a multi-agent optimization system based on a hierarchical decomposition strategy of watershed characteristics, constructing a double-layer nested short-term water consumption minimum model based on multi-agent optimization, and solving the short-term power generation optimization scheduling using an improved alternating direction multiplier method. Figure 1 shown.

[0142] (1) The hierarchical decomposition strategy based on watershed characteristics transforms the water-wind-solar hybrid power supply system into a multi-agent system:

[0143] Step 1: According to the output type, the hydro-wind-solar complementary power supply system is decomposed into hydropower intelligent body, wind power intelligent body and photovoltaic power intelligent body;

[0144] Step 2: According to the dispatching center, the hydropower stations belonging to different dispatching centers are decomposed into different intelligent agents.

[0145] Step 3: Based on the basin topology, assume that the basin intersection is a new dispatching center that is independent of the existing dispatching center, and further decompose the decomposed hydropower intelligent entity;

[0146] Step 4: In order to further reduce the solution scale of the intelligent agent, based on the regulation performance of the hydropower station, the decomposed hydropower intelligent agent is further subdivided into intelligent agents containing a smaller number of hydropower stations.

[0147] Through the above decomposition steps, the water-wind-solar complementary power supply system is decomposed into multiple independent and smaller intelligent entities in turn.

[0148] (2) Wind power and photovoltaic power output forecast

[0149]

[0150] Where: is the capacity factor of wind farm g in period t; is the wind speed of wind farm g in time period t, m·s -1 ; is the coefficient of wind farm g; is the output of wind farm g in time period t, MW; is the installed capacity of wind farm g; is the capacity factor of the photovoltaic field h in time period t; is the coefficient of the photovoltaic electric field h; is the radiation of the photovoltaic electric field h in time period t, ℃; is the temperature of the photovoltaic field h in time period t, °C; is the temperature of the photovoltaic electric field h under standard test conditions, °C; is the output of the photovoltaic field h in time period t, MW. is the installed capacity of the photovoltaic field h.

[0151] (3) Construction of a short-term water consumption minimization model based on multi-agent double-layer nesting:

[0152] According to the different coupling relationships between water and electricity, the watershed system can be divided into series, parallel and hybrid structures, among which the coupling relationship between hydraulic power and electricity in the hybrid structure is the most complex. The present invention takes four typical intelligent agents in the hybrid structure as an example for modeling and description: intelligent agent A is located upstream of the tributary, intelligent agent B is located upstream of the main river, intelligent agent C is located downstream of the main river, the upstream intelligent agent of intelligent agent C is intelligent agent B, and intelligent agent D is located at the downstream of the main river, and its upstream intelligent agents are intelligent agents A and C. In addition, intelligent agent A, intelligent agent D, wind power intelligent agent and photovoltaic power intelligent agent are controlled by the dispatch center I, and intelligent agent B and intelligent agent C are controlled by the dispatch center II. (See details. Figure 2 ).

[0153] 1. Construction of mathematical model between intelligent agents:

[0154] A. Objective Function

[0155]

[0156] Where: F is water consumption, 1×10 4 m 3 ; t is the time period number; T is the total number of time periods; Q i,t is the outbound flow of agent i in period t, m 3 / s, i = A, B, C, D; Δt is the step size of the scheduling calculation period.

[0157] B. Water Balance Constraints

[0158] a) Constraints on upstream agents (Agent A and Agent B)

[0159] V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600 (71)

[0160]

[0161] Where: V i,t is the reservoir capacity of agent i in time period t, 1×104 m 3 ;I i,t is the inbound flow of agent i in period t, m 3 / s; is the power generation flow of agent i in period t, m 3 / s;S i,t is the amount of abandoned water flow of agent i in period t, m 3 / s; i=A,B.

[0162] b) Downstream Agent (Agent C) Constraints

[0163] V C,t+1 =V C,t +(I C,t -Q C,t )×Δt×3600 (73)

[0164]

[0165] I C,t =R C,t +Q B,t (75)

[0166] Where: R i,t is the interval flow of agent i in time period t, m 3 / s, i=C.

[0167] c) Downstream Agent (Agent D) Constraints

[0168] V D,t+1 =V D,t +(I D,t -Q D,t )×Δt×3600 (76)

[0169]

[0170] I D,t =R D,t +Q A,t +Q C,t (78)

[0171] Where: i=D.

[0172] C. Power Constraint: Wind power agents and photovoltaic power agents are only controlled by the dispatch center I

[0173] Load I,t =P A,t +P D,t +P t w +P t s (79)

[0174] Load II,t =P B,t +P C,t (80)

[0175]

[0176] Where: Load I,t is the load demand of dispatch center I, MW; Load II,t is the load demand of dispatch center II, MW; P t w is the output of the wind power intelligent agent in time period t, MW; P t s is the output of the PV power intelligent entity in time period t, MW.

[0177] 2. Construction of the internal mathematical model of the intelligent agent:

[0178] A. Objective function:

[0179]

[0180] Where: F i is the water consumption of agent i, 1×10 4 m 3 ;Q i,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; i = A, B, C, D; j is the hydropower station number; J i is the number of hydropower stations contained in agent i.

[0181] B. Constraints:

[0182] a) Water balance equation

[0183] V i,j,t+1 =V i,j,t +(I i,j,t -Q i,j,t )×Δt×3600 (84)

[0184]

[0185] Where: V i,j,t is the reservoir capacity of hydropower station j in agent i at time period t, 1×10 4 m 3 ;I i,j,t is the inflow of hydropower station j in agent i in period t, m 3 / s;Q i,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; is the power generation flow of hydropower station j in intelligent agent i in time period t, m 3 / s;S i,j,t is the abandoned water flow of hydropower station j in intelligent agent i in time period t, m 3 / s; R i,j,t is the interval flow of hydropower station j in agent i in time period t, m 3 / s; m is the number of the power station directly upstream of hydropower station j; Ω j is the set of power stations directly upstream of hydropower station j; τ m,j is the lag time h between hydropower stations m and j; is the intelligent agent i hydropower station m in the period t-τ m,j Outbound flow, m 3 / s.

[0186] b) Outbound flow constraints

[0187]

[0188] Where: is the upper and lower limits of the outflow flow of hydropower station j in agent i, m 3 / s.

[0189] c) Power generation flow constraints

[0190]

[0191] Where: is the upper and lower limits of the power generation flow of hydropower station j in intelligent agent i, m 3 / s.

[0192] d) Discarded water flow constraints

[0193]

[0194] Where: is the upper and lower limits of the abandoned water flow of hydropower station j in intelligent agent i, m 3 / s

[0195] e) Water level constraints on dam

[0196]

[0197] Where: Z i,j,t is the water level above the dam of hydropower station j in agent i at time period t, m; are the upper and lower limits of the water level of hydropower station j in agent i, m.

[0198] f) Water level and storage capacity relationship curve

[0199]

[0200] Where: is the water level storage capacity curve function of hydropower station j in intelligent agent i.

[0201] g) Tailwater level discharge relationship curve

[0202]

[0203] Where: is the tailwater level of hydropower station j in agent i in time period t, m; is the tailwater level-flow relationship curve function of hydropower station j in intelligent agent i.

[0204] h) Head constraint

[0205]

[0206] Where: H i,j,t is the water head of hydropower station j in agent i at time period t, m.

[0207] i) Output constraints

[0208]

[0209] Where: P i,j,t is the output of hydropower station j in agent i in time period t, MW; are the upper and lower limits of the output of hydropower station j in agent i, in MW.

[0210] j) Power characteristic curve

[0211]

[0212] Where: It is the curve function between the output of hydropower station j in intelligent agent i and the power generation flow and water head.

[0213] k) Load constraints

[0214]

[0215] l) Initial water level constraint

[0216] Z i,j,init =Z i,j,1 (97)

[0217] Where: Z i,j,init is the initial water level of hydropower station j in agent i, m.

[0218] To further improve the solution efficiency, the nonlinear hydropower constraints (91), (92) and (95) are further converted into linear constraints using constraint aggregation technology.

[0219] (4) Use the improved alternating direction multiplier method to solve:

[0220] 1. Construction of mathematical optimization model based on alternating direction multiplier method:

[0221] A. Objective function:

[0222] minF=F A +F B +F C +F D (98)

[0223]

[0224]

[0225] Where: AD,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent A to agent D; is the consistent outbound flow from agent A to agent D, m 3 / s; is the consistent hydropower output from agent A to agent D, MW; ρ and ρ P is the penalty parameter of the hydraulic / electrical coupling variable; BC,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent B to agent C; is the consistent outbound flow from agent B to agent C, m 3 / s; is the consistent hydropower output from agent B to agent C, MW; λ CB,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent C to agent B; Q' B,t is the outbound flow rate that agent C expects agent B to have, m 3 / s;P' B,t is the output that agent C expects agent B to produce, MW; CD,t is the Lagrange multiplier of the outbound flow from agent C to agent D; is the consistent outbound flow from agent C to agent D, m 3 / s;λ DA,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent D to agent A; Q' A,t is the outbound flow rate that agent D expects agent A to have, m 3 / s;P' A,t is the output that agent D expects agent A to produce, MW; DC,tis the Lagrange multiplier of the outbound flow from agent D to agent C; Q' C,t is the outbound flow rate that agent D expects agent C to have, m 3 / s.

[0226] B. Coupling constraints:

[0227] a) Hydraulic coupling constraints

[0228] Q A,t =Q' A,t (103)

[0229] Q B,t =Q' B,t (104)

[0230] Q C,t =Q' C,t (105)

[0231] b) Power coupling constraints

[0232] P A,t =P' A,t (106)

[0233] P B,t =P' B,t (107)

[0234] 2. Update variables in coupling constraints:

[0235] A. Update of hydraulic coupling constraint variables (agents A and D):

[0236]

[0237] Where: is the outbound flow of agent A in the k+1th iteration of period t, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; is the Lagrange multiplier of the outbound flow from agent A to agent D at the kth iteration; is the outbound flow rate that agent D expects agent A to have in the k+1th iteration, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; Lagrange multiplier of the outbound flow from agent D to agent A at the kth iteration; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s.

[0238] The original residual and dual residual of hydraulic variables are expressed as:

[0239]

[0240] Where: are the original residual and dual residual of the outbound flow from agent A to agent D.

[0241] B. Update of hydraulic coupling constraint variables (agent B and agent C):

[0242]

[0243] Where: is the outbound flow of agent B in the k+1th iteration of period t, m 3 / s; is the consistent outbound flow from agent B to agent C in the kth iteration, m 3 / s; is the Lagrange multiplier of the outbound flow from agent B to agent C at the kth iteration; is the outbound flow rate that agent C expects agent B to have in the k+1th iteration, m 3 / s; is the consistent outbound flow from agent B to agent C in the kth iteration, m 3 / s; Lagrange multiplier of the outbound flow from agent C to agent B at the kth iteration; is the consistent outbound flow from agent B to agent C in the kth iteration, m 3 / s.

[0244] The original residual and dual residual of hydraulic variables are expressed as:

[0245]

[0246] Where: are the original residual and dual residual of the outbound flow from agent B to agent C.

[0247] C. Update of hydraulic coupling constraint variables (agents C and D):

[0248]

[0249] Where: is the outbound flow of agent C in the k+1th iteration of period t, m 3 / s; is the consistent outbound flow from agent C to agent D in the kth iteration, m 3 / s; is the Lagrange multiplier of the outbound flow from agent C to agent D at the kth iteration; is the outbound flow rate that agent D expects agent C to have in the k+1th iteration, m 3 / s; is the consistent outbound flow from agent C to agent D in the kth iteration, m 3 / s; Lagrange multiplier of the outbound flow from agent D to agent C at the kth iteration; is the consistent outbound flow from agent C to agent D in the kth iteration, m 3 / s.

[0250] The original residual and dual residual of hydraulic variables are expressed as:

[0251]

[0252] Where: are the original residual and dual residual of the outbound flow from agent C to agent D.

[0253] D. Power coupling constraint variable update (agent A and agent D):

[0254]

[0255] Where: is the output of agent A in the k+1th iteration of period t, WM; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent A to agent D in the kth iteration; is the output that agent D expects agent A to produce in the k+1th iteration, MW; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent D to agent A at the kth iteration; is the consistent hydropower output from agent A to agent D at the kth iteration, MW.

[0256] The original residual and dual residual of power variables are expressed as:

[0257]

[0258] Where: It is the original residual and dual residual of hydropower output from agent A to agent D.

[0259] E. Power coupling constraint variable update (agent B and agent C):

[0260]

[0261]

[0262] Where: is the output of agent B in the k+1th iteration of period t, WM; is the consistent hydropower output from agent B to agent C at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent B to agent C in the kth iteration; is the output that agent C expects agent B to produce in the k+1th iteration, MW; is the consistent hydropower output from agent B to agent C at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent C to agent B at the kth iteration; is the consistent hydropower output from agent B to agent C at the kth iteration, MW.

[0263] The original residual and dual residual of power variables are expressed as:

[0264]

[0265] Where: It is the original residual and dual residual of the hydropower output from agent B to agent C.

[0266] 3. Parameter settings:

[0267] The heuristic algorithm is used to generate the initial consistent outbound flow and consistent output of each agent. The initial load demand of each agent depends on the proportion of its installed capacity in the same dispatching center, thereby obtaining the initial consistent output. The present invention uses formulas (143) and (144) to explain in detail the process of obtaining the initial load demand of agents B and C. Further, formulas (83)-(97) are used to obtain the initial consistent outbound flow.

[0268]

[0269] Where: D B,t , D C,t are the initial load requirements of agents B and C, MW; Ins B 、Ins C are the installed capacities of agents B and C, MW respectively.

[0270] Formulas (145) and (146) are used to explain in detail the process of obtaining the initial load requirements of agents A and D, and formulas (83)-(97) are further used to obtain the initial consistent outbound flow.

[0271]

[0272] Where: D A,t , D D,t are the initial load requirements of agents A and D, MW; Ins A 、Ins D are the installed capacity of agents A and D, MW respectively.

[0273] 4. Correction process:

[0274] Due to the original residual and the dual residual, there is an inherent deviation between the actual outflow and output of the upstream agent and the expected outflow and output of the downstream agent. The present invention corrects the inflow and load demand of the downstream agent based on the principles of water balance and power matching. The specific correction steps are as follows:

[0275] Step 1: Since the upstream agent is not affected by the outflow of other agents, the output process of the upstream agent is the final optimal scheduling result, and its final optimal water consumption is and

[0276] Step 2: Modify the inflow and dispatching requirements of the downstream agent. For agent C, according to formulas (75) and (104), its water balance equation can be expressed as The scheduling demand can be expressed as P according to formulas (80) and (107): C,t =Load II,t -P' B,t . Since the primal and dual residuals, Q' B,t ≠Q B,t and P' B,t ≠P B,t Therefore, the actual total inflow flow and actual dispatch demand can be calculated according to formulas (147) and (148).

[0277] I C,t =R C,t +Q B,t (147)

[0278] P C,t =Load II,t -P B,t (148)

[0279] Step 3: Use formulas (83)-(97) to determine the final optimal outflow flow of agent C (see formula (149)) and the final optimal water consumption F C *.

[0280] Q C,t =Q * C,t(149)

[0281] Step 4: For agent D, according to equations (78), (103) and (105), its water balance equation can be expressed as D,t =R D,t +Q' A,t +Q' C,t , the scheduling demand can be expressed as follows according to formulas (79) and (106): Since the primal and dual residuals, Q' A,t ≠Q A,t , Q' C,t ≠Q C,t and P' A,t ≠P A,t Therefore, based on the final optimal outbound flow Q of agent C obtained in step 3, * C,t , the actual total inflow flow and actual scheduling demand of agent D can be calculated according to formulas (150) and (151).

[0282] I D,t =R D,t +Q A,t +Q * C,t (150)

[0283] P D,t =Load I,t -P A,t -P t w -P t s (151)

[0284] Step 5: Use formulas (83)-(97) to determine the optimal outflow flow of agent D (see formula (152)) and the final optimal water consumption

[0285] Q D,t =Q * D,t (152)

[0286] 5. Example Analysis

[0287] The actual data of a typical dry season day of a large-scale hydro-wind-solar complementary power supply system in a certain river basin in the southwest is used to perform a 96-point simulation calculation as a verification example of the method of the present invention. The power supply system includes 9 hydropower stations and a series of wind and photovoltaic power plants, among which the hydropower station S A , S D , S E , S F , S G , S H and SI Located on the main river, S B , S C The power station is located in a tributary and is located at the hydropower station S A and S D In addition, the hydropower station S A , S B , S C , S D , S E , S F The wind and photovoltaic power plants are controlled by the dispatch center 1, while the hydropower station S G , S H and S I It is controlled by the dispatch center 2, and the topology structure is shown in Figure 3 . According to the hierarchical decomposition strategy, the water-wind-solar complementary power supply system is decomposed into five hydropower intelligent entities, one wind power intelligent entity, and one photovoltaic power intelligent entity. The basic parameters are shown in Table 1. The model of the present invention is named Model 1, and the conventional comparison model is set as the control group, named Model 2, where Model 2 is a centralized solution of the water-wind-solar complementary power supply system. Since the short-term optimization scheduling requirements change frequently and require rapid response, the maximum solution time of Model 1 and Model 2 is set to 3600s. In addition, in order to avoid water abandonment in actual scheduling, the maximum amount of water abandoned in each period in the model is set to zero.

[0288] Table 1

[0289]

[0290] Table 2 shows the comparison of constraints and variables of different models. As shown in Table 2, since nonlinear constraints are linearized, a large number of integer variables are introduced in all models. It can be seen that the total number of integer variables in Model 1 and Model 2 remains unchanged, totaling 1728. However, as the hydro-wind-solar hybrid power supply system is decomposed into multiple agents, the total number of its continuous variables and constraints also increases. The main reason for this increase is the coupled hydraulic and electrical connections between the agents.

[0291] Table 2

[0292]

[0293] When the original residuals and dual residuals of hydraulic power and electric power of two consecutive iterations between agents are less than the preset tolerance value, Model 1 stops iterating. Based on domain knowledge, the original residual of hydraulic power of the agent of the present invention is set to 0.5% of the power generation flow, and the original residual of electric power is set to 0.5% of the installed capacity. In addition, the dual residuals of hydraulic power and electric power of each agent are set to 0.5%. Figure 4As shown in the figure, at the end of the 7th iteration, the primal residuals and dual residuals of hydraulic power and electric power are smaller than the preset tolerance values. Due to the influence of outflow flow, output penalty parameters and Gap value, the convergence process of the primal residuals and dual residuals oscillates.

[0294] from Figure 5 It can be seen that there is a certain deviation between the actual output of the agent and the expected output of other agents. The parts marked with black boxes represent the maximum and minimum deviations, where a1, a2, and a3 represent the minimum deviations, and b1, b2, and b3 represent the maximum deviations. Figure 5 Taking (a) as an example for analysis, in the 18th period, the actual output of agent I and the output of agent I expected by agent III have the largest deviation, reaching 4.9MW (see black box a1); the minimum deviation value appears in the 8th period, which is 3.41MW (see black box b1). The output deviation will cause the total output of the agent to not match the load demand, further affecting the frequency regulation of the power grid and threatening the safety and stability of the power grid. Therefore, in order to improve the quality of the solution, the present invention adjusts the flow and output of the downstream agent based on the principles of water balance and power matching.

[0295] Table 3

[0296]

[0297] Table 3 summarizes the comparison results of different models after correction. As shown in Table 3, the total water consumption of Model 1 is 39181.67×10 4 m 3 , the total calculation time is 1383s, of which the solution time is 1193s and the correction time is 190s. In comparison, Model 2 fails to obtain a feasible solution within the maximum effective time range.

[0298] The change of flow and output is ultimately manifested as a change in water level. Therefore, subsequent analysis will focus on the water level change process and the difference in water level before and after modification. It is worth noting that the present invention only carries out a detailed analysis of the intelligent agents that need to adjust the flow. In the actual case of the present invention, intelligent agents I and II are located upstream and are not affected by the upstream intelligent agents. In addition, the wind power intelligent agent and the photovoltaic power intelligent agent are non-dispatched intelligent agents, and their output is a predicted output. Therefore, only intelligent agents III to V need to adjust the flow and output (i.e., the hydropower station S D To S J ).like Figure 6 As shown in Figure 2, the water levels before and after the correction are both within the water level range. In addition, the water level difference of the hydropower station with strong regulation performance is significantly smaller than that of the hydropower station with poor regulation performance. For example, the hydropower station S in Agent IV G The water level difference is controlled within 0.01m.

[0299] Figure 7 The load response capabilities of intelligent agents in different dispatch centers are demonstrated. Figure 7 It can be seen that the output of the hydropower intelligent entity in the dispatching center 1 is lower than its installed capacity, which indicates that there is a certain amount of reserve capacity. When the actual output of wind and solar power is lower than the predicted value, the reserve capacity of hydropower can be used to alleviate the power shortage.

Claims

1. A multi-agent short-term hydropower generation optimization scheduling method considering spatiotemporal coupling constraints, characterized in that: The steps include: Step 1: Transform the hydro-wind-solar complementary power supply system into a multi-agent system based on the hierarchical decomposition strategy of watershed characteristics (1.1) According to the output type, the hydropower-wind-solar hybrid power supply system is decomposed into hydropower intelligent body, wind power intelligent body and photovoltaic power intelligent body; (1.2) According to the dispatching center, the hydropower stations belonging to different dispatching centers are decomposed into different intelligent agents; (1.3) Based on the basin topology, it is assumed that the basin intersection is a new dispatching center that is independent of the existing dispatching center, and the decomposed hydropower intelligent entity is further decomposed; (1.4) In order to further reduce the solution scale of the intelligent agent, based on the regulation performance of the hydropower station, the decomposed hydropower intelligent agent is further subdivided into small-scale intelligent agents containing fewer hydropower stations; Step 2: Wind power and photovoltaic power output forecast Where: is the capacity factor of wind farm g in period t; is the wind speed of wind farm g in time period t, m·s -1 ; is the coefficient of wind farm g; is the output of wind farm g in time period t, MW; is the installed capacity of wind farm g; is the capacity factor of the photovoltaic field h in time period t; is the coefficient of the photovoltaic electric field h; is the radiation of the photovoltaic electric field h in time period t, ℃; is the temperature of the photovoltaic field h in time period t, °C; is the temperature of the photovoltaic electric field h under standard test conditions, °C; is the output of the photovoltaic field h in time period t, MW; is the installed capacity of the photovoltaic field h; Step 3: Using the hydropower, wind power and photovoltaic power intelligent entities decomposed in step 1 as dispatching units, a short-term water consumption minimization model among intelligent entities is constructed; Four typical agents in the hybrid structure are modeled and described: agent A is located upstream of the tributary, agent B is located upstream of the main river, agent C is located downstream of the main river, the upstream agent of agent C is agent B, agent D is located at the lowest downstream of the main river, and the upstream agents of agent D are agents A and C; in addition, agent A, agent D, wind power agent and photovoltaic power agent are controlled by the dispatch center I, and agent B and agent C are controlled by the dispatch center II; the model description is as follows: A. Objective Function Where: F is water consumption, 1×10 4 m 3 ; t is the time period number; T is the total number of time periods; Q i,t is the outbound flow of agent i in period t, m 3 / s, i = A, B, C, D; Δt is the step length of the scheduling calculation period; B. Water Balance Constraints a) Constraints on upstream agents A and B V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600(6) Where: V i,t is the reservoir capacity of agent i in time period t, 1×10 4 m 3 ; I i,t is the inflow flow of agent i in period t, (m 3 / s); is the power generation flow of agent i in period t, m 3 / s;S i,t is the amount of abandoned water flow of agent i in period t, (m 3 / s); i = A, B; b) Downstream Agent C Constraints V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600(8) I i,t =R i,t +Q B,t (10) Where: R i,t is the interval flow of agent i in time period t, m 3 / s, i=C; c) Downstream Agent D Constraints V i,t+1 =V i,t +(I i,t -Q i,t )×Δt×3600(11) I i,t =R i,t +Q A,t +Q C,t (13) Where: i = D; C. Power Constraint: Wind power agents and photovoltaic power agents are only controlled by the dispatch center I Load I,t =P A,t +P D,t +P t w +P t s (14) Load II,t =P B,t +P C,t (15) Where: Load I,t is the load demand of dispatch center I, MW; Load II,t is the load demand of dispatch center II, MW; P i,t is the output of agent i in time period t, MW, i = A, B, C, D; P t w is the output of the wind power intelligent agent in time period t, MW; P t s is the output of the PV power intelligent entity in time period t, MW; Step 4: Based on the wind power and photovoltaic power output predicted in step 2, and taking the hydropower station as the basic dispatching unit, a short-term water consumption minimum model within the intelligent agent is constructed; A. Objective function: Where: F i is the water consumption of agent i, 1×10 4 m 3 ; Q i,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; i = A, B, C, D; j is the hydropower station number; J i is the number of hydropower stations contained in agent i; B. Constraints: a) Water balance equation V i,j,t+1 =V i,j,t +(I i,j,t -Q i,j,t )×Δt×3600(19) Where: V i,j,t is the reservoir capacity of hydropower station j in agent i at time period t, 1×10 4 m 3 ; I i,j,t is the inflow of hydropower station j in agent i in period t, m 3 / s;Q i,j,t is the outflow of hydropower station j in agent i in time period t, m 3 / s; is the power generation flow of hydropower station j in intelligent agent i in time period t, m 3 / s;S i,j,t is the water discharge of hydropower station j in agent i in time period t, m 3 / s; R i,j,t is the interval flow of hydropower station j in agent i in time period t, m 3 / s; m is the number of the power station directly upstream of hydropower station j; Ω j is the set of power stations directly upstream of hydropower station j; τ m,j is the lag time between hydropower stations m and j, h; is the intelligent agent i hydropower station m in the period t-τ m,j Outbound flow, m 3 / s; b) Outbound flow constraints Where: is the upper and lower limits of the outflow flow of hydropower station j in agent i, m 3 / s; c) Power generation flow constraints Where: is the upper and lower limits of the power generation flow of hydropower station j in intelligent agent i, m 3 / s; d) Discarded water flow constraints Where: is the upper and lower limits of the abandoned water flow of hydropower station j in intelligent agent i, m 3 / s; e) Water level constraints on dam Where: Z i,j,t is the water level above the dam of hydropower station j in agent i at time period t, m; is the upper and lower limits of the water level of hydropower station j in agent i, m; f) Water level and storage capacity relationship curve Where: is the water level storage capacity curve function of hydropower station j in agent i; g) Tailwater level discharge relationship curve Where: is the tailwater level of hydropower station j in agent i at time period t, m; is the tailwater level-discharge relationship curve function of hydropower station j in intelligent agent i; h) Head constraint Where: H i,j,t is the water head of hydropower station j in agent i at time period t, m; i) Output constraints Where: is the upper and lower limits of the output of hydropower station j in agent i, MW; j) Power characteristic curve Where: P i,j,t is the output of hydropower station j in agent i in time period t, MW; is the curve function between the output of hydropower station j in intelligent agent i and the power generation flow and water head; k) Load constraints l) Initial water level constraint WITH i,j,init =Z i,j,1 (32) Where: Z i,j,init is the initial water level of hydropower station j in agent i, m; Step 5: Redescribe the agent-based mathematical model based on the alternating direction multiplier method; A. Objective function: minF=F A +F B +F C +F D (33) Where: AD,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent A to agent D; is the consistent outbound flow from agent A to agent D, m 3 / s; is the consistent hydropower output from agent A to agent D, MW; ρ and ρ P is the penalty parameter for the hydraulic and electrical coupling variables; BC,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent B to agent C; is the consistent outbound flow from agent B to agent C, m 3 / s; is the consistent hydropower output from agent B to agent C, MW; λ CB,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent C to agent B; Q' B,t is the outbound flow rate that agent C expects agent B to have, m 3 / s;P' B,t is the output that agent C expects agent B to produce, MW; CD,t is the Lagrange multiplier of the outbound flow from agent C to agent D; is the consistent outbound flow from agent C to agent D, m 3 / s;λ DA,t and is the Lagrange multiplier of the outflow flow and hydropower output from agent D to agent A; Q' A,t is the outbound flow rate that agent D expects agent A to have, m 3 / s;P' A,t is the output that agent D expects agent A to produce, MW; DC,t is the Lagrange multiplier of the outbound flow from agent D to agent C; Q' C,t is the outbound flow rate that agent D expects agent C to have, m 3 / s; B. Coupling constraints: a) Hydraulic coupling constraints Q A,t =Q' A,t (38) Q B,t =Q' B,t (39) Q C,t =Q' C,t (40) b) Power coupling constraints P A,t =P' A,t (41) P B,t =P' B,t (42) Step 6, iteratively update the hydraulic and electric coupling constraint variables, and set the tolerance values ​​of the original residual and the dual residual to stop the iteration; A. Update of hydraulic coupling constraint variables of Agent A and Agent D: Where: is the outbound flow of agent A in the k+1th iteration of period t, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; is the Lagrange multiplier of the outbound flow from agent A to agent D at the kth iteration; is the outbound flow rate that agent D expects agent A to have in the k+1th iteration, m 3 / s; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; Lagrange multiplier of the outbound flow from agent D to agent A at the kth iteration; is the consistent outbound flow from agent A to agent D in the kth iteration, m 3 / s; The original residual and dual residual of hydraulic variables are expressed as: Where: are the original residual and dual residual of the outbound flow from agent A to agent D; Similarly, formulas (43)-(49) can be used to update the outbound flow, expected outbound flow, consistent flow, Lagrange multipliers of outbound flow, primal residuals and dual residuals between agent B and agent C, and to update similar variables between agent C and agent D; B. Update of power coupling constraint variables of Agent A and Agent D: Where: is the output of agent A in the k+1th iteration of period t, WM; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent A to agent D in the kth iteration; is the output that agent D expects agent A to produce in the k+1th iteration, MW; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; is the Lagrange multiplier of the hydropower output from agent D to agent A at the kth iteration; is the consistent hydropower output from agent A to agent D at the kth iteration, MW; The original residual and dual residual of power variables are expressed as: Where: The original residual and dual residual of hydropower output from agent A to agent D; Similarly, formulas (50)-(56) can be used to update the hydropower output, expected hydropower output, consistent hydropower output, Lagrange multiplier of hydropower output, primal residual and dual residual between agent B and agent C; Step 7: Generate the initial consistent outbound flow and consistent output of each agent based on the heuristic algorithm; (7.1) Using formulas (57) and (58), the process of obtaining the initial load requirements of agents B and C is described in detail, and the initial consistent outbound flow is further obtained using formulas (18)-(32); Where: D B,t , D C,t are the initial load requirements of agents B and C, MW; Ins B 、Ins C are the installed capacity of agents B and C, MW; (7.2) Formulas (59) and (60) are used to explain in detail the process of obtaining the initial load requirements of agents A and D, and formulas (18)-(32) are further used to obtain the initial consistent outbound flow; Where: D A,t , D D,t are the initial load requirements of agents A and D, MW; Ins A 、Ins D are the installed capacity of agents A and D, MW; Step 8: Based on steps 5 to 7, the optimal outbound flow and power generation process of each intelligent agent is obtained by using the alternating direction multiplier method; Step 9: Based on the principle of water balance and power matching, on the basis of step 8, the inflow and dispatching demand of the downstream intelligent body are corrected, and finally the optimal water consumption of the water-wind-solar complementary power supply system is obtained; the specific correction steps are as follows: (9.1) Since the upstream agent is not affected by the outflow of other agents, the output process of the upstream agent is the final optimal scheduling result, and its final optimal water consumption is and (9.2) Modify the inflow and dispatching requirements of the downstream agent. For agent C, according to formulas (10) and (39), its water balance equation is expressed as I C,t =R C,t +Q' B,t , the scheduling demand is expressed as follows according to formulas (15) and (42): Since the primal and dual residuals, Q' B,t ≠Q B,t and P' B,t ≠P B,t ; Therefore, the actual total inflow flow and actual dispatch demand can be calculated according to formulas (61) and (62); I C,t =R C,t +Q B,t (61) P C,t =Load II,t -P B,t (62) (9.3) Using formulas (18)-(32), the final optimal outflow flow of agent C is determined as shown in formula (63) and the final optimal water consumption Q C,t =Q * C,t (63) (9.4) For agent D, according to formulas (13), (38) and (40), its water balance equation is expressed as D,t =R D,t +Q' A,t +Q' C,t The scheduling demand is expressed as P according to formulas (14) and (41). D,t =Load I,t -P' A,t -P t w -P t s , due to the primal and dual residuals, Q' A,t ≠Q A,t , Q' C,t ≠Q C,t and P' A,t ≠P A,t ; Therefore, the final optimal outbound flow Q of agent C obtained based on (9.3) is * C,t , the actual total inbound flow and actual dispatch demand of agent D are calculated according to formulas (64) and (65); I D,t =R D,t +Q A,t +Q * C,t (64) P D,t =Load I,t -P A,t -P t w -P t s (65) Step 9.5: Use formulas (18)-(32) to determine the optimal outflow flow of agent D, see formula (66) and the final optimal water consumption Q D,t =Q * D,t (66)。

Images