Multi-energy complementary system site selection-constant volume-operation closed loop collaborative optimization method based on geographic information system

Abstract

The invention discloses a multi-energy complementary system dynamic collaborative optimization method based on a geographic information system, and belongs to the technical field of energy system optimization. In order to solve the bottleneck problems of lack of operation feedback, disjunction of planning and scheduling and the like in the prior art, a real-time optimization architecture of space evaluation, capacity allocation, operation verification and closed-loop adjustment is provided. The method is characterized in that a hour-level operation model is constructed through mixed integer programming, and the actual performance of capacity configuration is accurately quantified; establishing a bidirectional feedback channel, and feeding operation indexes such as a load power shortage rate and a power abandoning rate back to an upper-layer capacity decision in real time; and an improved NSGA-II algorithm is adopted to realize dynamic balance of economy and reliability. According to the method, the adaptive capacity of the system to uncertainty is remarkably enhanced, the planning scheme is ensured to be continuously optimized in the whole life cycle, and a self-adaptive decision tool is provided for the renewable energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy system optimization, and more particularly to a GIS-based multi-energy complementary system site-sizing-operation closed-loop collaborative optimization method. BACKGROUND

[0002] With the continuous growth of global energy demand and the increasing urgency of environmental protection, energy systems are transforming from the traditional fossil fuel-dominated mode to sustainable and low-carbon renewable energy systems. In this context, multi-energy complementary systems play a key role in reducing dependence on fossil fuels and improving renewable energy consumption through spatial complementarity and operational flexibility. Current research has made significant progress: spatial layout optimization methods based on GIS multi-criteria decision-making, and capacity configuration optimization techniques using heuristic optimization algorithms, multi-objective evolution algorithms, and other algorithms. Recent research has attempted to combine long-term investment planning and operation scheduling through comprehensive evaluation models. However, existing methods still have the following key limitations: fragmented integration of spatial planning and operation optimization, lack of site-sizing-capacity feedback mechanisms based on actual operation data, insufficient consideration of complex multi-dimensional variable coupling in multi-energy complementary systems, and lack of closed-loop optimization capabilities to bridge the gap between theoretical planning assumptions and actual performance. These deficiencies result in insufficient resource utilization of geographically advantageous sites, low capacity allocation efficiency, and insufficient feasibility of the proposed scheme, fully demonstrating the urgent need to develop a GIS-based closed-loop collaborative optimization framework. SUMMARY

[0003] Therefore, the embodiments of the present application provide a GIS-based multi-energy complementary system site-sizing-capacity-operation closed-loop collaborative optimization method, which integrates geographic spatial analysis and operational constraints through resource-oriented site scoring, flexibility-optimized energy storage configuration, and reliability-driven capacity adjustment mechanisms, to achieve joint optimization of site-sizing-capacity-operation, while providing a quantitative path for continuous improvement based on operational feedback.

[0004] To achieve the above-mentioned purpose, the technical solutions adopted by the present application are as follows:

[0005] S1, a spatial evaluation system including climate conditions, geographical location, environmental factors, and terrain characteristics is constructed, the analytic hierarchy process is used to determine the weight of each criterion, the hierarchical screening is performed through the hard exclusion standard first, then the comprehensive score is implemented, and finally the grid-based candidate site spatial suitability distribution map is generated;

[0006] S2, decision variables including wind farm / photovoltaic power station capacity and energy storage system are constructed, a multi-objective optimization model with the maximum net present value and the minimum load power shortage rate as the target is established, a site value-oriented capacity allocation strategy based on GIS scoring results is adopted, and an improved NSGA-II algorithm is used to solve and obtain a Pareto optimal solution set;

[0007] S3, build a mixed integer linear programming-based hourly operation optimization model to minimize the weighted total operation cost including power generation cost, maintenance cost and power shortage penalty, verify the feasibility of the capacity configuration scheme through operation simulation, and calculate actual performance indicators such as load power shortage rate, power curtailment rate and energy storage utilization rate;

[0008] S4, feed back the operation performance indicators to the upper capacity optimization link, iteratively adjust the capacity configuration scheme based on reliability and economic indicators, so as to ensure the coordinated optimization of long-term investment decision and short-term operation demand.

[0009] Optionally, the specific steps of S1 include:

[0010] S11, build a spatial evaluation system including climate conditions, geographical location, environmental elements and terrain characteristics;

[0011] S12, determine the weight of each evaluation criterion by using the analytic hierarchy process;

[0012] S13, first perform hierarchical screening through hard exclusion criteria, and then perform comprehensive scoring;

[0013] S14, generate a grid-based candidate site spatial suitability distribution map.

[0014] Optionally, the specific steps of S2 include:

[0015] S21, define the decision variables of the wind farm / photovoltaic power station capacity and energy storage system;

[0016] S22, establish a multi-objective optimization model with the maximum net present value and the minimum load power shortage rate as the optimization objectives;

[0017] S23, use site value-oriented capacity allocation strategy based on GIS scoring results;

[0018] S24, use improved NSGA-II algorithm to obtain a Pareto optimal solution set.

[0019] Optionally, the specific steps of S3 include:

[0020] S31, build a mixed integer linear programming-based hourly operation optimization model;

[0021] S32, minimize the weighted total operation cost composed of power generation cost, maintenance cost and power shortage penalty as the optimization objective;

[0022] S33, verify the feasibility of the capacity configuration scheme through operation simulation;

[0023] S34, calculate actual operation indicators including load power shortage rate, power curtailment rate and energy storage utilization rate.

[0024] Optionally, the S4 specific steps include:

[0025] S41, feeding the operation performance index to an upper capacity optimization module;

[0026] S42, iteratively adjusting the capacity allocation scheme based on reliability and economy indexes;

[0027] S43, realizing the collaborative optimization of long-term investment decision and short-term operation demand.

[0028] The beneficial effects of the present application are as follows:

[0029] The present application significantly improves the performance of multi-energy complementary system through the GIS-based closed-loop collaborative optimization method, and realizes significant economic benefits such as net present value improvement, initial investment reduction and operation cost reduction compared with traditional methods. The GIS-based multi-criteria evaluation system breaks through the limitations of the traditional resource-oriented mode, realizes higher power generation efficiency by considering multiple indexes such as grid access conditions, terrain constraints and environmental factors. The closed-loop feedback mechanism systematically adjusts the capacity configuration based on actual operation data, ensures the efficient collaboration of long-term planning and short-term operation, and realizes the full consumption of renewable energy while ensuring system reliability. The present application shows high robustness under uncertainty, and provides a scientific method to overcome the limitations of traditional fragmented optimization, and establishes a universal optimization framework for multi-energy complementary systems in different regions. BRIEF DESCRIPTION OF DRAWINGS

[0030] In order to more clearly illustrate the implementation of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description.

[0031] Figure 1 The GIS-based multi-energy complementary system site selection-capacity determination-operation closed-loop collaborative optimization method flowchart provided for the embodiment of the present application;

[0032] Figure 2 The GIS-based multi-criteria site selection evaluation flowchart provided for the embodiment of the present application;

[0033] Figure 3 The upper capacity allocation optimization flowchart provided for the embodiment of the present application;

[0034] Figure 4 The lower layer operation verification flowchart provided for the embodiment of the present application;

[0035] Figure 5 The closed-loop feedback flowchart provided for the embodiment of the present application. DETAILED DESCRIPTION

[0036] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0037] Embodiment 1

[0038] Referring to Figure 1 , the present application provides a GIS-based multi-energy complementary system site selection-capacity determination-operation closed-loop collaborative optimization method, comprising the following steps:

[0039] S1, a spatial evaluation system containing climate conditions, geographical locations, environmental factors and terrain features is constructed, the analytic hierarchy process is used to determine the weight of each criterion, hierarchical screening is first performed through hard exclusion criteria, then comprehensive scoring is implemented, and finally a grid-based candidate site spatial suitability distribution map is generated;

[0040] S2, decision variables containing wind farm / photovoltaic power station capacity and energy storage system are constructed, a multi-objective optimization model with maximum net present value and minimum load power shortage rate as targets is established, a site value-oriented capacity allocation strategy is adopted based on the GIS scoring results, and an improved NSGA-II algorithm is used to solve and obtain a Pareto optimal solution set;

[0041] S3, a time-based operation optimization model based on mixed integer linear programming is constructed, with the minimum weighted total operation cost containing generation cost, maintenance cost and power shortage penalty as the target, the feasibility of the capacity configuration scheme is verified through operation simulation, and actual performance indicators such as load power shortage rate, power curtailment rate and energy storage utilization rate are calculated;

[0042] S4, the operation performance indicators are fed back to the upper capacity optimization link, the capacity configuration scheme is iteratively adjusted based on reliability and economic indicators, so as to ensure the collaborative optimization of long-term investment decisions and short-term operation needs.

[0043] Embodiment 2

[0044] Referring to Figure 2 , specifically, the specific steps of S1 include:

[0045] S11, a spatial evaluation system containing climate conditions, geographical locations, environmental factors and terrain features is constructed:

[0046] Score i =∑(ω j ×C ij )

[0047] In the formula, ωj is the weight of criterion j; C ij is the normalized score of criterion j at site i.

[0048] S12, determine the weight of each evaluation criterion by analytic hierarchy process;

[0049] S13, first filter by hard exclusion criteria, then perform comprehensive scoring;

[0050] S14, generate a grid-based candidate site space suitability distribution map.

[0051] Embodiment 3

[0052] Please refer to Figure 3 , specifically: S2 specific steps include:

[0053] S21, define the decision variables of wind farm / photovoltaic power station capacity and energy storage system;

[0054]

[0055] In the formula, N w and N s are the number of candidate wind farms and photovoltaic power stations respectively; W i and S j are the capacity values of the corresponding sites; E batt and E pump are the energy capacities of electrochemical energy storage and pumped storage respectively.

[0056] S22, establish a multi-objective optimization model with the maximum net present value and the minimum load power shortage rate as the target:

[0057] (1) Economic target

[0058]

[0059] In the formula, NPV is the net present value; C init is the initial investment cost; R y and C y are the annual income and cost of y years, r is the discount rate; Y is the project period.

[0060] Wherein, the initial investment cost can be calculated by the following formula:

[0061]

[0062] In the formula, C w , C s , C batt and C pump are the unit investment costs of wind power, photovoltaic power, electrochemical energy storage and pumped storage respectively.

[0063] Considering the time value impact of phased construction, the present application modifies formula (3) to reflect the economic characteristics of time-series development:

[0064]

[0065] In the formula, and respectively represent the wind farm and photovoltaic power station set planned for construction in the pth phase; ΔT is the time interval of each phase.

[0066] (2) Reliability target

[0067] System reliability is the core performance indicator of multi-energy complementary base, and the present application takes the minimization of load power shortage probability (LPSP) as the second optimization target:

[0068]

[0069] In the formula, P shortage (t) represents the power shortage at time t; P load (t) is the load demand at time t; T is the planning period.

[0070] For large multi-energy complementary systems, direct calculation of accurate LPSP values requires a large number of time series simulations, with high computational complexity. Therefore, the present application draws on the Z-statistic method in capacity adequacy assessment of power systems and proposes a simplified LPSP estimation model based on capacity contribution coefficients. This model considers the differentiated contribution of various types of power sources to system reliability, significantly reducing the computational burden while maintaining reasonable accuracy.

[0071]

[0072] In the formula, P peak is the load peak value; C total is the total effective capacity of the system, which takes into account the capacity contribution coefficients of various types of power sources:

[0073]

[0074] In the formula, γ w , γ s , γ h , γ c , γ batt , and γ pump are the capacity contribution coefficients of wind power, photovoltaic power, hydropower, thermal power, electrochemical energy storage, and pumped storage; η batt and η pump are the efficiency coefficients of electrochemical energy storage and pumped storage.

[0075] S23, adopt site value oriented capacity allocation strategy based on GIS score results;

[0076] S24, adopt improved NSGA-II algorithm to obtain Pareto optimal solution set.

[0077] Embodiment 4

[0078] Please refer to Figure 4 , specifically: S3 specific steps include:

[0079] S31, construct a time-by-time operation optimization model based on mixed integer linear programming;

[0080] S32, minimize the weighted total operation cost composed of power generation cost, maintenance cost and power shortage penalty as the optimization objective:

[0081]

[0082] In the formula, and are the operation costs of thermal power generation, wind power operation and maintenance, photovoltaic operation and maintenance, hydropower operation and maintenance, energy storage loss and power shortage penalty respectively; ω s is used to represent the weight distribution of typical days in the whole year (the number of days in each season corresponds to the weight).

[0083] To ensure the technical feasibility of system operation, the application establishes a constraint system covering power balance, equipment characteristics and system safety:

[0084] (1) power balance constraint

[0085] System power balance is the basic requirement to ensure stable operation, which is expressed as:

[0086]

[0087] (2) upper limit constraint of power generation

[0088] The output of various power sources is limited by factors such as resource conditions and equipment capacity, and its constraint is as follows:

[0089]

[0090] 0≤P t hy ≤P t hy,max

[0091] 0≤P t c ≤P c,max

[0092] In the formula, CF t w,i and CFt s,j are the hourly capacity factors of wind farms and photovoltaic power plants, respectively; P t hy,max is the available output of hydropower station t hours later; P c,max is the maximum output of thermal power.

[0093] (3) Dynamic constraints of energy storage

[0094] The operating state of the energy storage system satisfies the principle of energy conservation, and its state of charge (SOC) is expressed as:

[0095]

[0096] where, η batt and η pump are the efficiency coefficients of electrochemical energy storage and pumped storage, respectively.

[0097] (4) Capacity constraints of energy storage

[0098] The charging and discharging power and energy capacity of the energy storage system are physically limited and need to satisfy the following constraints:

[0099]

[0100] 0≤P t b,ch ≤P batt

[0101] 0≤P t b,dis ≤P batt

[0102] 0≤P t p,ch ≤P pump

[0103] 0≤P t p,dis ≤P pump

[0104] (5) Continuity constraints of energy storage

[0105] To ensure the sustainability of the energy storage system in periodic operation, the first and last state continuity constraints are introduced:

[0106]

[0107]

[0108] (6) Ramp constraints of thermal power

[0109] The output regulation rate of thermal power units is limited by technology and needs to satisfy the ramp constraints:

[0110] where R c is the maximum ramp rate of thermal power.

[0111] (7) Water power ramping constraint

[0112] The water power unit is also limited by the output adjustment rate, and the constraint is:

[0113]

[0114] where R hy is the maximum ramp rate of water power.

[0115] (8) Daily water power generation constraint

[0116] The daily water power generation of the water power station is limited by the available amount of water resources:

[0117]

[0118] where P is the daily maximum water power generation.

[0119] (9) Minimum thermal power output constraint

[0120] The thermal power unit needs to maintain a certain minimum technical output:

[0121] P t c ≥ P c,min

[0122] where P c,min is the minimum technical output of thermal power.

[0123] (10) Minimum energy storage cycle constraint

[0124] In order to guarantee the use efficiency of the energy storage system, the minimum cycle depth constraint is set:

[0125]

[0126] where β batt and β pump are the minimum cycle coefficients of the electrochemical energy storage and pumped storage respectively.

[0127] S33, verifying the feasibility of the capacity configuration scheme through operation simulation;

[0128] S34, calculating actual operation indexes including load power shortage rate, power curtailment rate and energy storage utilization rate.

[0129] Embodiment 5

[0130] Please refer to Figure 5 , specifically: the specific steps of S4 include:

[0131] S41, feeding the operation performance index to the upper capacity optimization module;

[0132] S42, iteratively adjusting the capacity allocation scheme based on the reliability and economy indexes;

[0133] W i (k+1) = W i (k) × (1 + α × Performance i )

[0134] In the formula, Performance i includes actual load power shortage rate, power abandonment rate and economic index.

[0135] S43, realizing the collaborative optimization of long-term investment decision and short-term operation demand.

Claims

1. A closed-loop collaborative optimization method for site selection, capacity determination, and operation of a multi-energy complementary system based on Geographic Information System (GIS), characterized in that: Includes the following steps: S1. Construct a spatial evaluation system that includes climate conditions, geographical location, environmental factors and topographic features. Use the analytic hierarchy process (AHP) to determine the weight of each criterion. First, use hard exclusion criteria to screen the sites at different levels, then implement comprehensive scoring, and finally generate a grid-based spatial suitability distribution map of candidate sites. S2. Construct decision variables including wind farm / photovoltaic power plant capacity and energy storage system, establish a multi-objective optimization model with the goal of maximizing net present value and minimizing load deficit rate, adopt a site value-oriented capacity allocation strategy based on GIS scoring results, and use the improved NSGA-II algorithm to solve for Pareto optimal solution set. S3. Construct an hourly operation optimization model based on mixed integer linear programming, with the goal of minimizing the weighted total operating cost including generation cost, maintenance cost and power shortage penalty. Verify the feasibility of the capacity configuration scheme through operation simulation, and calculate actual performance indicators such as load power shortage rate, power curtailment rate and energy storage utilization rate. S4. Feed back the operational performance indicators to the upper-level capacity optimization stage, and iteratively adjust the capacity configuration scheme based on reliability and economic indicators, thereby ensuring the coordinated optimization of long-term investment decisions and short-term operational needs.

2. The GIS-based multi-energy complementary system site selection-capacity determination-operation closed-loop collaborative optimization method according to claim 1, characterized in that: The specific steps in S1 include: S11. Construct a spatial evaluation system that includes climate conditions, geographical location, environmental elements and topographic features; S12. Use the analytic hierarchy process (AHP) to determine the weights of each evaluation criterion. S13. First, use strict exclusion criteria to conduct hierarchical screening, and then conduct comprehensive scoring. S14. Generate a grid-based spatial suitability distribution map of candidate sites.

3. The GIS-based multi-energy complementary system location-capacity determination-operation closed-loop collaborative optimization method according to claim 1, characterized in that: The specific steps in S2 include: S21. Define the decision variables for wind farm / photovoltaic power plant capacity and energy storage system; S22. Establish a multi-objective optimization model with the objectives of maximizing net present value and minimizing load shortage rate; S23. Adopt a site value-oriented capacity allocation strategy based on GIS scoring results; S24. The Pareto optimal solution set is obtained by using the improved NSGA-II algorithm.

4. The GIS-based multi-energy complementary system location-capacity determination-operation closed-loop collaborative optimization method according to claim 1, characterized in that: The specific steps in S3 include: S31. Construct a time-by-time optimization model based on mixed-integer linear programming; S32. The optimization objective is to minimize the weighted total operating cost consisting of power generation cost, maintenance cost, and power shortage penalty. S33. Verify the feasibility of the capacity configuration scheme by running simulations; S34. Calculate the actual operating indicators, including load shortage rate, power curtailment rate and energy storage utilization rate.

5. The GIS-based multi-energy complementary system site selection-capacity determination-operation closed-loop collaborative optimization method according to claim 1, characterized in that: The specific steps in S4 include: S41. Feed back the operational performance indicators to the upper-level capacity optimization module; S42. Iteratively adjust the capacity allocation scheme based on reliability and economic indicators; S43. Achieve synergistic optimization between long-term investment decisions and short-term operational needs.

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