Large wind-light-storage energy base installed capacity planning method based on extra-high voltage direct current delivery

Abstract

The invention provides a large wind-light-storage energy base installed capacity planning method based on extra-high voltage direct current delivery, and aims to seek the optimal installed capacity of wind power, photovoltaic power generation and pumped storage power stations so as to improve the utilization rate of clean energy and the stability of a power grid. By analyzing data such as wind speed, sunlight and load demand, operation parameters and constraint conditions are set, a double-layer optimization model is constructed, an outer-layer model screens an optimal capacity scheme through a TOPSIS method, and an inner-layer model determines an optimal operation process under each group of wind and light energy storage capacity scheme based on time sequence production simulation. The invention further provides two different operation modes to adapt to different power grid operation requirements, and the effectiveness and practicability of the method are verified by analyzing a capacity planning case of a certain energy base in Qinghai province. The technical scheme of the invention can improve the energy utilization rate, optimize the resource configuration, enhance the stability of the power grid and improve the economy, and has the characteristics of high flexibility and adaptability.

Description

Technical Field

[0001] The present invention belongs to the field of power system engineering, and relates to optimization technology for power resource allocation and energy management, and specifically to a method for planning installed capacity of a large-scale wind-solar-storage energy base based on ultra-high voltage direct current transmission. Background Art

[0002] With the increasing depletion of fossil energy and the intensification of environmental pollution, the development and utilization of clean, low-carbon and pollution-free renewable energy has become an important direction for global energy transformation. As the most promising renewable energy sources, wind and solar energy have seen rapid growth in installed capacity, but this has also brought challenges to the safe and stable operation of the power grid. Especially when the scale of wind and solar power installed is large, the intermittent, random and volatile nature of their output puts tremendous pressure on the power grid. In "Capacity tariff mechanism of a pumped hydro storage station: Pricing approaches for reducing benefit allocation unfairness of integrated renewable energy systems", Zhang X mentioned that there is a widespread phenomenon of spatial and temporal mismatch between energy supply and load demand in China. Therefore, it is necessary to use ultra-high voltage transmission lines to transmit electricity from wind and solar energy bases to peak electricity consumption areas to ensure the stable consumption of wind and solar energy. Subject to the influence of multiple factors such as the characteristics of wind and solar resources at the sending end, the power grid's consumption capacity at the receiving end, and the cost of development and utilization, there is a certain threshold for the scale of new energy installed in the multi-energy complementary energy base.

[0003] At present, domestic and foreign researchers have conducted a lot of research on the capacity configuration of water (including pumped storage) wind and solar energy bases. Zhang et al. in "Ptimal sizing of the grid-connected hybrid system integrating hydropower, photovoltaic, and wind considering cascade reservoir connection and photovoltaic-wind complementarity" determined the typical wind power and photovoltaic output scenarios according to different factors such as seasons, and based on this, built a capacity planning model for the water-wind-solar hybrid system to determine the optimal installed capacity of wind power and photovoltaic; Cheng et al. in "Optimal Capacity Configuration of Pumped-Storage Power Station in Wind-PV-Fire-Pump Storage System-All Databases" selected the optimal scale of pumped storage power stations in a certain independent wind-solar-fire-pump storage energy base by improving the gray wolf algorithm to maximize the economic benefits of the energy base, and took typical days in summer and winter as examples to perform optimization scheduling analysis, verifying the effectiveness of the model and algorithm; Zhou et al. in "Research on Pumped Storage Capacity Allocation of Cascade Hydro-Wind-Solar-Pumped Storage Hybrid System Considering Economy and Operational In the study “Photovoltaic capacity optimization of small and medium-sized hydro-photovoltaic hybrid energy systems considering multiple uncertainties”, Yuan et al. constructed a scenario combination to deal with the risk of hydro-photovoltaic output uncertainty and proposed a cumulative confidence method.This method allows the complementary system to accommodate as much photovoltaic capacity as possible while maintaining a high confidence level; Cheng et al proposed a novel inner-outer layer framework in "Inner-outer layer co-optimization of sizing and energy management for renewable energy microgrid with storage", including an outer layer for capacity optimization and an inner layer for optimized operation, to plan the capacity of microgrids containing wind, photovoltaic, and battery energy storage; Wen et al stated in "Optimizing the sizes of wind and photovoltaic plants complementarily operating with cascade hydropower stations: Balancing risk and benefit" that the short-term volatility of wind and solar energy is the source of system risk, and thus proposed a multi-time scale nested operation model that considers the relationship between risk and benefit, and determines the optimal wind and photovoltaic capacity based on this. Most of the above studies are based on the simulation scheduling results of multiple typical days as the basis for capacity planning, which is difficult to fully capture the uncertainty and seasonal volatility of renewable energy output and its actual impact on system operation, and have great limitations.

[0004] In the process of capacity planning, simulating the 8760-hour sequential operation of the energy base can fully capture the uncertainty of new energy output and comprehensively cover the details of the multi-energy complementary operation of the energy base; Zhang et al. in "Assessing the integration potential of new energy in river basin clean energy corridors considering energy-power coupled complementary operation modes" constructed a capacity planning model for the hydro-photovoltaic complementary system with the maximum annual power generation as the objective function, and determined the optimal photovoltaic installed capacity by simulating the operation process of 8760 hours in the Beipanjiang River Basin in 2017; Nassar et al. in "Synthetic analysis and sizing optimization of a pumped hydroelectric storage-integrated hybrid PV / Wind system: A case study" constructed a wind-solar storage capacity planning model with the lowest levelized cost of electricity and 100% load demand satisfaction as the objective function; Xu et al. in "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system》, from the perspective of investors, a model for optimizing the configuration of wind, solar and storage capacity was constructed with the maximum power supply reliability and the minimum investment cost as the objective function. The model was solved by a multi-objective particle swarm optimization algorithm to obtain the capacity configuration of an off-grid wind, solar and storage energy base in Sichuan. The above literature effectively captures the uncertainty of new energy output by adopting the 8760 time series simulation method, thus achieving better planning results in theory, but it does not consider the limiting impact of UHV DC transmission.

[0005] In addition, the way wind, solar and storage energy bases are connected to the power grid will affect the grid's absorption of renewable energy and the transmission stability of ultra-high voltage DC interconnection lines. Zhang et al. in "Capacity configuration of a hydro-wind-solar-storage bundling system with transmission constraints of the receiving-end power grid and its techno-economic evaluation" fully considered the impact of pumped-storage power stations on the complementary system from an economic and technical perspective, and proposed a triple progressive screening principle for capacity planning, thereby clarifying the relationship between wind power abandonment rate, transmission guarantee rate and grid-connected electricity price. Cheng et al. set the maximum output of the cascade hydro-wind-PV-battery complementary system as the total installed capacity of the cascade power stations in "Contribution of complementary operation in adapting to climate change impacts on a large-scale wind-solar-hydro system: A case study in the Yalong River Basin", and analyzed the impact of climate change and complementary operation on the cascade hydro-wind-PV-battery complementary system; Tian et al. considered the characteristics of multi-energy integration into power grid in "The capacity planning method for a hydro-wind-PV-battery complementary system considering the characteristics of multi-energy integration into power grid", proposed a capacity planning model for the hydro-wind-PV-battery complementary system, and proposed centralized and distributed configuration modes to explore the optimal capacity configuration mode. The above literature considers the transmission of energy base electricity through UHV DC transmission lines in capacity planning, but generally only considers the impact of channel capacity on output, and lacks consideration of the operating characteristics of UHV DC interconnection lines. In addition, most studies only analyze a single power transmission mode, lack in-depth discussion of the response of the transmission system under different operating conditions, and lack universality.

[0006] This paper focuses on the installed capacity planning of large-scale wind-solar-storage energy bases under UHV DC transmission, aiming to solve the problem of determining reasonable installed capacity of new energy within various thresholds to match the double-end characteristics of multi-energy complementary energy bases. Therefore, studying the optimal capacity configuration and operation strategy planning of large-scale renewable energy bases under UHV DC transmission is of great significance for solving the problem of reverse distribution of wind and solar resources and load centers and improving the clean energy absorption capacity. Summary of the invention

[0007] The purpose of the present invention is to provide a method for planning the installed capacity of a large-scale wind-solar-storage energy base based on ultra-high voltage direct current transmission, so as to optimize the installed capacity and operation strategy of wind power, photovoltaic power generation and pumped storage power stations, thereby improving the utilization rate of clean energy and the stability of the power grid.

[0008] When planning capacity, existing energy bases often fail to fully consider the complementarity of multiple renewable energy sources and energy storage technologies, as well as the operational constraints of UHV DC transmission lines. This may lead to problems such as unstable power supply, energy waste, and low economic benefits in actual operation of energy bases.

[0009] The technical solution of the present invention:

[0010] a. Based on the output characteristics of wind power and photovoltaic power generation in the target area and local policy planning, propose the potential installed capacity range of wind power and photovoltaic power generation and the potential installed capacity range of pumped storage power stations;

[0011] b. Set constraints and initial data

[0012] The constraints include pumped storage power station operation constraints, UHV DC interconnection line transmission line operation constraints, new energy power station operation constraints, and energy base system constraints;

[0013] Initial data includes grid load curve, load loss penalty, power abandonment penalty, grid-connected electricity price, and power station maintenance cost;

[0014] c. Construct a two-layer optimization model, with the outer layer being the capacity optimization model and the inner layer being the optimization operation model; the outer layer uses the TOPSIS method to evaluate the outer layer capacity optimization model and determine the optimal capacity solution, while the inner layer optimization operation model determines the optimal operation process under each group of wind, solar and energy storage capacity solutions based on time series production simulation;

[0015] d. Linearize the nonlinear factors appearing in the two-layer optimization model while meeting the accuracy requirements;

[0016] e. The effectiveness of the proposed capacity planning method is verified through case analysis, and corresponding operation strategies are proposed based on the local power transmission curve.

[0017] Beneficial effects of the present invention:

[0018] 1. Improve energy utilization: Through multi-energy complementarity, achieve a power supply guarantee rate of more than 90% and a power abandonment rate of less than 15%.

[0019] 2. Optimize resource allocation: Rationally plan the installed capacity of various power generation resources and improve resource utilization efficiency.

[0020] 3. Enhance grid stability: Through the regulatory effect of energy storage technology, the volatility of wind and solar power generation can be smoothed out and the stability of the grid can be enhanced.

[0021] 4. Improve economic efficiency: Reduce the operating costs of the energy base and improve economic benefits by optimizing the operation strategy.

[0022] 5. Strong flexibility and adaptability: The proposed operation mode can adapt to different grid operation requirements and market changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a schematic diagram of a wind, solar and storage energy base. After the hybrid energy base generates electricity, it is uploaded to the power grid through UHV DC lines for transmission;

[0024] Figure 2 It is a schematic diagram of the power transmission of the high-voltage direct current transmission line under the free power transmission mode at the sending end. In this mode, the receiving-end power grid does not make specific requirements for the output curve of the energy base, but only limits the upper and lower limits of the energy base output. The energy base can freely adjust the output according to the wind and solar output characteristics to improve the utilization rate of wind and solar resources and the efficiency of power generation. Taking into account the limitations of factors such as the operating reliability, control feasibility and equipment operating life of the high-voltage direct current interconnection line, the high-voltage direct current converter equipment should not be adjusted frequently. Under normal operating conditions, the direct current transmission curve should filter out factors such as burrs, sawtooth, and frequent reciprocating fluctuations. Therefore, the operation mode of the high-voltage direct current interconnection line should adopt a stepped segmented operation mode, and the transmission power in each segment should be relatively stable;

[0025] Figure 3 is the geographical location of the clean energy base and the province of export selected in the embodiment;

[0026] Figure 4 is the average monthly output of wind power and photovoltaic power in the clean energy base in the embodiment;

[0027] Figure 5 1 is the typical daily output of wind power and photovoltaic power in the clean energy base in the embodiment; (a) is January, (b) is April, (c) is July, and (d) is September;

[0028] Figure 6 is the monthly load demand of the receiving power grid in the transmission province in the embodiment;

[0029] Figure 7 is the typical daily output of the clean energy base under the free power transmission mode at the receiving end; among them, (a) is the typical daily output of the clean energy base on January 1, (b) is the typical daily output of the clean energy base on April 1, (c) is the typical daily output of the clean energy base on July 1, and (d) is the typical daily output of the clean energy base on October 1;

[0030] Figure 8 is the typical daily output of the clean energy base under the agreed transmission curve mode at the receiving end; among them, (a) is the typical daily output of the clean energy base on January 1, (b) is the typical daily output of the clean energy base on April 1, (c) is the typical daily output of the clean energy base on July 1, and (d) is the typical daily output of the clean energy base on October 1;

[0031] Fig. 9 It is a sensitivity analysis of the base wind and solar power generation source under the free transmission mode at the receiving end; among them, (a) is the impact of wind power availability on the optimal capacity, (b) is the impact of photovoltaic availability on the optimal capacity, (c) is the impact of wind power grid-connected electricity price on the optimal capacity, (d) is the impact of photovoltaic grid-connected electricity price on the optimal capacity, (e) is the impact of wind power curtailment penalty coefficient on the optimal capacity, and (f) is the impact of photovoltaic curtailment penalty coefficient on the optimal capacity;

[0032] Fig.10 It is a sensitivity analysis of the base wind and solar power generation source under the agreed transmission curve mode at the receiving end, where (a) is the impact of wind power availability on the optimal capacity, (b) is the impact of photovoltaic availability on the optimal capacity, (c) is the impact of wind power grid-connected electricity price on the optimal capacity, (d) is the impact of photovoltaic grid-connected electricity price on the optimal capacity, (e) is the impact of wind power curtailment penalty coefficient on the optimal installed capacity, and (f) is the impact of photovoltaic curtailment penalty coefficient on the optimal installed capacity. DETAILED DESCRIPTION

[0033] The specific implementation of the present invention is further described below in conjunction with the accompanying drawings and technical solutions.

[0034] This paper proposes a two-layer capacity planning model based on the wind-solar-storage energy base, which takes into account the operation constraints of pumped-storage power stations and ultra-high voltage direct current transmission lines. The model includes an outer capacity optimization model and an inner optimization operation model, in which the outer layer selects the optimal capacity plan through TOPSIS, and the inner layer determines the optimal operation process under each group of wind and solar capacity plans based on time series production simulation, and proposes two different operation modes in the inner optimization operation model to enhance the practicality and versatility of the model:

[0035] The following is a specific embodiment, which describes the detailed steps of the present invention:

[0036] 1. Data preparation: Collect data on wind speed, sunshine, load demand, etc. of an energy base in Qinghai Province, analyze the power transmission curve and time-of-use electricity price information of the power grid, and set the operating parameters and constraints of wind power, photovoltaic power generation, and pumped storage power stations based on the collected data;

[0037] 2. Model construction:

[0038] Construct an outer capacity optimization model and select the optimal capacity solution through the TOPSIS method;

[0039] Construct an inner optimization operation model to determine the optimal operation process for each group of wind, solar and energy storage capacity solutions based on time-series production simulation;

[0040] As the outer layer of the two-layer optimization model, its main task is to input the wind and solar capacity combination into the inner layer and conduct a comprehensive evaluation of each plan based on the feedback results of the inner layer, and quantify the advantages and disadvantages of each plan. In the evaluation process of the outer capacity plan, the TOPSIS method is used, including constructing an ideal positive solution and an ideal negative solution, and calculating the distance between each alternative plan and the two ideal solutions. Finally, by calculating the Euclidean distance with the ideal positive solution and the ideal negative solution, all feasible plans are ranked, effectively quantifying the comprehensive performance of each plan and identifying the optimal capacity plan.

[0041]

[0042] Where: S WT , S PV They represent the set of wind power and photovoltaic installed capacity solutions respectively; C WT , C PV Respectively represent the maximum and minimum installed capacity of wind power and photovoltaic power, in MW; ΔC WT , ΔC PV Respectively represent the iteration step lengths of wind power and photovoltaic power; N represents the number of iterations;

[0043] The specific indicators for evaluating the performance of energy bases under different capacity plans are as follows:

[0044] a. Electricity delivered: the total annual electricity of the energy base, expressed as follows:

[0045]

[0046] Where, T represents the total duration of the scheduling cycle, unit: h; Δt represents the time step in the scheduling model, unit: h; P t Indicates the power transmission value in the power transmission curve mode agreed upon by the transmitter and receiver;

[0047] b. Unit electricity cost: the cost required for the energy base to produce 1kWh of electricity, expressed as follows:

[0048]

[0049] In the formula, C inv represents the investment and construction cost of the wind, solar and storage energy base, unit: yuan; C OM It represents the operation and maintenance cost of the wind, solar and storage energy base, unit: Yuan;

[0050] c. Abandonment rate: This is an important indicator to measure the operating efficiency of energy bases, reflecting the proportion of renewable energy power that cannot be used due to various reasons to the ideal power generation capacity:

[0051]

[0052] Where P t wc , P t pvc Respectively represent the abandoned power of wind farm and photovoltaic power station at time t, unit: MW; C w , C pv Indicates the installed capacity of the wind farm, unit: MW;

[0053] d. Power supply guarantee rate: the ratio of the number of time periods in which the energy base output meets the requirements to the total number of time periods, expressed as follows:

[0054]

[0055] Where P t s Indicates the load shortage at time t, unit: MW;

[0056] e. Load power shortage rate: the ratio of the total load shortage to the total load demand:

[0057]

[0058] In the formula, C t It represents the load demand of the power grid at the transmitting and receiving ends during the period t;

[0059] As the inner model, the optimization operation model considers the free power transmission mode of the sending and receiving ends and the agreed power transmission curve mode of the sending and receiving ends:

[0060] Objective function: For wind, solar and storage energy bases, capacity planning is to maximize the comprehensive benefits of wind, solar and storage energy bases by comprehensively considering the complementary characteristics of various energy sources, rationally allocating wind and solar capacity, reducing wind and solar abandonment, and maximizing the comprehensive benefits of wind, solar and storage energy bases. The objective function is mainly composed of wind power generation, photovoltaic power generation, power sales revenue of pumped storage power stations, power abandonment penalties, load shortage penalties, and annualized operation and maintenance costs, which are expressed as follows:

[0061]

[0062] Where, f represents the total revenue of the wind, solar and storage energy base, unit: yuan; T represents the total duration of the dispatch cycle, unit: h; P t WT , P t PV Indicates the actual grid-connected power of wind farms and photovoltaic power stations at time t, unit: MW; c WT 、c PV Respectively represent the on-grid electricity price of wind power and photovoltaic power, unit: yuan / MWh; P t g , P t p They represent the power generation output and water output power of the pumped storage power station at time t, in MW; c PHS represents the electricity revenue of the pumped storage power station, which is the difference between the on-grid electricity price of the pumped storage power station and the pumped electricity price, unit: yuan / MWh; Δt represents the time step in the dispatch model, unit: h; P t wc , P t pvc , P t s Respectively represent the amount of power abandoned by wind farms and photovoltaic power stations and the amount of load shortage at time t, unit: MW; ω WT ,ω PV 、.ω s . Respectively represent wind power curtailment, photovoltaic power curtailment and load shortage penalty, unit: yuan / MWh; C inv represents the investment and construction cost of the wind, solar and storage energy base, unit: yuan; C OM represents the operation and maintenance cost of the wind-solar-storage energy base, unit: yuan; the constraints that need to be met to solve the installed capacity planning problem of large-scale wind-solar-storage energy base based on UHV DC transmission are expressed as follows:

[0063] Pumped storage power station operation constraints;

[0064] (1) Hydraulic connection between upstream and downstream reservoirs

[0065]

[0066] Where: Respectively represent the storage capacity of the upstream and downstream reservoirs at time t-1, unit: m 3 ; V t u 、V t d Respectively represent the storage capacity of the upstream and downstream reservoirs at time t, unit: m 3 ; Respectively represent the pumping flow and power generation flow of the pumped storage power station at time t, unit: m 3 / s;

[0067] (2) Constraints on upstream and downstream reservoir capacity

[0068]

[0069] Where: Indicates the minimum storage capacity limit of the upstream and downstream reservoirs, unit: m 3 ; Respectively represent the maximum storage capacity limit of the upstream and downstream reservoirs, unit: m 3 ;

[0070] (3) Constraints on initial and final storage capacity of upstream reservoirs

[0071]

[0072] Where: Indicates the storage capacity of the upstream reservoir at the initial time of the dispatch period, unit: m 3 ; Indicates the initial storage capacity of the upstream reservoir, unit: m 3 ; Indicates the storage capacity of the upstream reservoir at the end of the scheduling period, unit: m 3 ; Indicates the target storage capacity of the upstream reservoir at the end of the period, unit: m 3 ;

[0073] (4) Unit operating status constraints

[0074]

[0075]

[0076] Where: Respectively represent the minimum and maximum power output limits of the pumped storage power station, unit: MW; The state variables representing the power generation state and pumping state of the pumped storage power station at time t respectively; Indicates the maximum pumping power limit of the pumped storage power station, unit: MW; Respectively represent the minimum and maximum power generation flow limits of the pumped storage power station, unit: m 3 / s;

[0077] (5) Constraints between net water head and upper and lower reservoir water levels

[0078]

[0079] Where: H t It represents the average generating head of the pumped storage power station at time t, unit: m; Respectively represent the water level of the upstream reservoir at time t and t-1, unit: m; Respectively represent the water level of the downstream reservoir at time t and t-1, unit: m;

[0080] (6) Water level and storage capacity relationship function

[0081]

[0082] (7) Unit power characteristic curve constraints

[0083]

[0084] Where: k1 represents the calculation coefficient of the power output of the pumped storage power station; k2 represents the calculation coefficient of the pumped power of the pumped storage power station;

[0085] (8) Minimum unit operating time constraints

[0086]

[0087] Where: The conversion variable representing the conversion of the pumped storage unit from the shutdown state to the power generation state at time t; The conversion variable indicating that the pumped storage unit is in the power generation state at time t; The state variables representing the power generation state of the pumped storage power station at time t and t-1 respectively; Indicates the power generation state of the pumped storage unit and the minimum operating duration of the unit in the pumping state;

[0088] b. UHV DC transmission line transmission constraints:

[0089] The power transmission curve of the high-voltage DC interconnection line should be step-shaped and should not be adjusted in the opposite direction within a short period of time to avoid frequent adjustment of DC converter equipment and ensure the reliability of grid operation.

[0090]

[0091] Where: C max , C min They represent the maximum and minimum transmission power limits of the UHV DC transmission line, in MW; M represents the number of steps in the transmission power curve of the HVDC interconnection line;

[0092] c. New energy operation constraints

[0093] P t WT =P t wi ·C w -P t wc

[0094] P tPV =P t pvi ·C pv -P t pvc

[0095]

[0096] Where: P t WT , P t PV Respectively represent the actual output of wind farm and photovoltaic farm at time t, unit: MW; P t wi , P t pvi Respectively represent the theoretical output per unit value of wind farm and photovoltaic farm at time t, unit: MW; C w , C pv Indicates the installed capacity of the wind farm, unit: MW; P t wc , P t pvc It represents the abandoned power of wind farm and photovoltaic farm at time t, unit: MW;

[0097] d. Energy base system constraints: It stipulates that the pumped storage power station cannot generate electricity when the energy base has power abandonment. When the energy base has power abandonment in period t, otherwise

[0098] P t =P t wa +P t pva +P t g -P t p

[0099]

[0100] e. Power balance constraint: Under the agreed power transmission curve mode, the system output power should be as close to the load curve as possible. When the power supply is insufficient, load shortage will occur:

[0101] P t =C t -P t s

[0102] 3. Nonlinear constraint transformation:

[0103] There are nonlinear constraints in the above model. Therefore, the present invention linearizes the above nonlinear factors so that the original mixed integer nonlinear programming model is converted into a standard mixed integer linear programming model (MILP) that meets the accuracy requirements. Finally, the mature and efficient CPLEX solver is used to solve the mixed integer linear programming single-layer model. The specific linearization steps are as follows:

[0104] (1) Constrained linearization of the dynamic characteristic curve of the pumped storage unit

[0105] The dynamic characteristic curve constraint of the pumped storage unit is a system that contains two unknown parameters ( H t ), the McCormick envelope inequality is used to transform this nonlinear constraint into a linear constraint:

[0106]

[0107] Where: H min H max Respectively represent the minimum and maximum water heads of the pumped storage power station unit, unit: m;

[0108] (2) UHV DC interconnection line transmission constraint linearization

[0109] The UHV DC transmission power constraint is highly discrete and nonlinear. Therefore, the binary variable and Convert the original nonlinear constraints into linearized form:

[0110]

[0111] Where: N T Indicates the maximum number of fluctuations in the amount of electricity online allowed by the energy base within a year; δ min , δ max Respectively represent the minimum and maximum value of system power change, unit is MW / h. are state variables representing the upward and downward fluctuation of the online power of the energy base at time t; N k Indicates the minimum output duration of system power stability, unit: h.

[0112] 4. Operation mode selection:

[0113] (1) In seasons with high wind power and photovoltaic power output, the free power transmission mode at the sending end is adopted.

[0114] (2) During periods of high and stable load demand, the agreed power transmission curve mode between the transmitter and the receiver is adopted.

[0115] 5. Case analysis: Conduct a capacity planning case analysis on an energy base in Qinghai Province, derive the optimal installation plan, and analyze its technical and economic indicators.

[0116] Qinghai Province is located in the northwest inland of China and has abundant wind and solar renewable energy. Due to the low local load demand, it is impossible to fully absorb the electricity generated by local wind power and photovoltaic power, so it is necessary to transmit electricity to other provinces with higher electricity demand through UHV DC transmission to ensure better absorption of wind and solar energy. Figure 3 As shown in the figure, the research object of this paper is a large energy base composed of wind power stations, photovoltaic power stations and pumped storage power stations.

[0117] Figure 4 It is the average monthly output of wind power and photovoltaic power of the clean energy base in the embodiment. The average annual utilization hours of wind power in the area where the base is located is more than 2000 hours, and the average annual utilization hours of photovoltaic power is more than 1650 hours;

[0118] Figure 5 It is a typical daily output of wind power and photovoltaic power in the clean energy base in the embodiment;

[0119] Table 1 shows the basic parameters of a daily regulation pumped storage power station: The pumped storage power station has been planned and has an installed capacity of 9100MW. Due to its large installed capacity, the pumped storage power station can be used as a peak-shaving power source to meet the needs of daily load peak-valley difference; it can also operate some units at night to provide night-time power support for the UHV DC line;

[0120] Table 1 Main parameters of PHS

[0121]

[0122] Figure 6 It is the load demand of the receiving power grid in the province where the power is transmitted to the outside in the embodiment; the UHV transmission line has been planned and completed, the rated transmission power of the line reaches 8000MW, and its channel utilization hours are 5929h.

[0123] In order to find the optimal wind power and photovoltaic installed capacity under different schemes, we set the iteration step size according to the local renewable energy scale, divided wind power and photovoltaic into 5 and 7 different capacity levels respectively, and combined them to form 35 wind and solar capacity planning schemes, as shown in Table 2.

[0124] Table 2 Parameters of wind power and photovoltaic capacity planning scheme

[0125]

[0126] The optimization of different capacity scheme combinations under the two modes is carried out. First, the scheduling results of all capacity combination schemes in Table 2 are preliminarily screened, and the scope of feasible schemes is preliminarily determined according to the following standards: new energy power abandonment rate ≤ 15%, power supply guarantee rate ≥ 90%, LSR ≤ 10%. Then, the TOPSIS method is used to comprehensively evaluate the feasible schemes after screening. In order to reflect the importance of each indicator, the weights of DE, LCOE, CR, PSGR and LSR are set to 1.6, 1.2, 1.2, 1 and 1.4 respectively. Through TOPSIS analysis, the relative proximity of each scheme is determined, and each scheme is scored in turn. The higher the score, the better the scheme. The optimal wind and solar power installed capacity is the same in the two modes, but because the power transmission curve is considered in the ATC mode, its power abandonment rate increases from 0.82% in the FT mode to 14.05%, and the power supply guarantee rate decreases from 99.49% to 90.36%. The technical and economic indicators of each scheme are shown in Table 3.

[0127] Table 3. Operating parameters of optimal capacity solutions under different operating modes

[0128]

[0129] In the free transmission mode at the sending end, there is no specific requirement for the shape of the transmission curve, and the output of the energy base only needs to meet the channel capacity requirements. The optimal installed capacity plan is determined as: 8000MW of wind power and 20500MW of photovoltaic power. In order to analyze the optimal operation process of the energy base under different plans, 4 days each in January, April, July and October are selected to analyze its short-term operation process, such as Figure 8 The green shaded area is the pumped storage output, the area above the x-axis represents the power generation of the pumped storage power station in the generating state, and the area below the x-axis represents the pumped hydropower in the pumping state. Figure 8 It can be seen that under the free power transmission mode at the sending end, the output of the energy base is smooth and stable as a whole, which meets the operation requirements of the UHV DC transmission line. From the perspective of the whole year, power abandonment is mainly concentrated in April and October, while power abandonment is less in January and July. This is mainly due to the large number of wind and solar resources in April, and the energy base cannot absorb electricity during some periods. In October, the photovoltaic day output is higher, which leads to a large change between day and night, so there is more power abandonment.

[0130] Under the agreed transmission curve mode, the energy base follows Figure 6 The optimal installed capacity is determined as follows: 8000MW of wind power and 20500MW of photovoltaic power. The typical daily power generation process is as follows: Fig.10 As shown. Fig. 9It can be seen that the load demand in January was low, and the pumped storage power station only had a daily regulation function, resulting in a large amount of power abandonment. In April, due to the increase in load demand, the power abandonment situation improved. However, there was a serious power shortage in July, which was mainly due to the mismatch between load demand and renewable energy supply. Specifically, the load demand was the highest in July, but the average monthly output of photovoltaic power was significantly lower than that in April. Entering October, with the decrease in load demand, the energy base can basically meet the load demand. In view of this situation, it is recommended to introduce gas units in this mode to provide additional power support in months with higher load demand, while appropriately reducing the installed capacity of renewable energy to reduce power abandonment, thereby optimizing the overall operating efficiency and economy of the energy base.

[0131] Fig.10 This is a sensitivity analysis under the free power transmission mode at the sending end. It can be seen from the figure that availability has a significant impact on the selection of the optimal capacity. With the change of availability, the optimal wind power installed capacity gradually increases from 6000MW to 8000MW, and remains stable after reaching the maximum available installed capacity. At the same time, the optimal photovoltaic installed capacity decreases from 22000MW to 19000MW. From a macro perspective, the optimal installed capacity should generally decrease with increasing availability. This anomaly reveals the complex relationship between availability and the optimal installed capacity, and reminds us that we cannot simply select the optimal installed capacity based on the changing trend. The correlation between electricity price and optimal capacity is not obvious, but it is generally positively correlated, such as Fig.10 (c) and 10(d). When the electricity price of the renewable energy source rises, the optimal installed capacity of the source with high electricity price tends to increase, while the installed capacity of the other source decreases. Fig.10 (e) and 10(f) show the impact of the power abandonment penalty coefficient on the optimal installed capacity. The optimal installed capacity of wind power has almost no change, while the optimal installed capacity of photovoltaic power has dropped from 22,000MW to 19,000MW. This is because there is no obvious difference in the distribution of wind power during the day, so there is less wind power abandonment. Photovoltaic power generation is mainly concentrated in the daytime, which is easy to cause power abandonment, and is therefore more sensitive to power abandonment penalties.

[0132] Fig.10 Sensitivity analysis under the agreed transmission curve model shows that the availability of wind power and photovoltaic power is negatively correlated with the optimal installed capacity. It should be noted that the optimal wind power installed capacity does not show a downward trend when the wind power availability ranges from -30% to 10%. This is because there is no obvious difference in the distribution of wind power during the day and at night, so a higher wind power installed capacity can provide a higher power supply guarantee rate. The electricity prices of wind power and photovoltaic power are positively correlated with the optimal installed capacity, but the impact is limited. Fig.10As shown in (c) and 10(d), when the wind power price increases from -30% to 30%, the optimal wind power installed capacity increases to the optimal installed capacity of 8000MW and then stops changing. The proportion of the optimal photovoltaic installed capacity gradually decreases with the increase of wind power price, and the photovoltaic power price also shows a similar trend. This is because increasing the proportion of the party with higher electricity prices can increase the income of the energy base. The power abandonment penalty coefficient shows a strong negative correlation with the optimal installed capacity. The optimal wind power installed capacity decreases from 8000MW to 6000MW, and the optimal photovoltaic installed capacity decreases from 23500MW to 17500MW. This is because the power abandonment penalty coefficient is set relatively high, so its change has a greater impact on the optimal result.

[0133] Through the above embodiments, the present invention can provide a systematic and practical solution for the capacity planning of large-scale wind, solar and energy storage bases, effectively improving the clean energy absorption capacity and the stability of the power grid.

Claims

1. A method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission, characterized in that: Here are the steps: a. Based on the output characteristics of wind power and photovoltaic power generation in the target area and local policy planning, propose the potential installed capacity range of wind power and photovoltaic power generation and the potential installed capacity range of pumped storage power stations; b. Set constraints and initial data The constraints include pumped storage power station operation constraints, UHV DC interconnection line transmission line operation constraints, new energy power station operation constraints, and energy base system constraints; Initial data includes grid load curve, load loss penalty, power abandonment penalty, grid-connected electricity price, and power station maintenance cost; c. Construct a two-layer optimization model, with the outer layer being the capacity optimization model and the inner layer being the optimization operation model; use the TOPSIS method to evaluate the capacity optimization model and determine the optimal capacity solution; the optimization operation model determines the optimal operation process under each group of wind, solar and energy storage capacity solutions based on time series production simulation; d. Linearize the nonlinear factors appearing in the two-layer optimization model while meeting the accuracy requirements; e. The effectiveness of the proposed capacity planning method is verified through case analysis, and corresponding operation strategies are proposed based on the local power transmission curve.

2. The method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission according to claim 1, characterized in that: In step a, the potential installed capacity range of wind power, photovoltaic power generation and pumped storage power generation is determined by analyzing the wind speed, sunshine data and energy future planning policies in the target area.

3. The method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission according to claim 1, characterized in that: In step b, considering the regulation capacity of the pumped storage power station and the need to smooth the fluctuation of wind and solar power generation, various initial conditions and initial data are set, including: (1) Operation constraints of pumped storage power stations: including hydraulic connections between upstream and downstream reservoirs of pumped storage power stations, storage capacity constraints of upstream and downstream reservoirs, initial and final storage capacity constraints of upstream reservoirs, unit operation status constraints, constraints between net water head and water levels of upstream and downstream reservoirs, water level and storage capacity relationship function, unit dynamic characteristic curve constraints, and unit minimum operation time constraints; (2) Transmission constraints of UHV DC transmission lines: Based on the capacity limitation and operating characteristics of UHV DC interconnection lines, the overall output of the energy base is constrained so that the power transmission curve outside the base runs in a step-like manner within the channel capacity range; (3) Constraints on the operation of new energy power stations: namely, constraints on the actual grid-connected power of wind power and photovoltaic power, which decompose the original output of wind and photovoltaic power into grid-connected power and abandoned power, and at the same time constrain the total abandoned wind power and total abandoned photovoltaic power to be proportional to their installed capacity; (4) Energy base system constraints: Pumped storage power stations cannot generate electricity during the period when the energy base generates power abandonment; (5) Transmission curve: Two different transmission plans are selected according to the needs of the power grid and the agreed energy base, namely, the agreed transmission curve mode at the receiving end and the variable transmission curve mode; (6) Setting initial data: grid load curve process, setting base load loss penalty price, power abandonment penalty price, grid-connected electricity price of each energy source, and maintenance cost of each power station.

4. The method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission according to claim 1, characterized in that: In step c, an internal and external double-layer optimization model is constructed, and the following constraints are included: (1) Capacity optimization model: As the outer layer of the two-layer optimization model, its main task is to input the wind and solar capacity combination into the inner layer and conduct a comprehensive evaluation of each plan based on the feedback results of the inner layer to quantify the advantages and disadvantages of each plan. In the evaluation process of the outer layer capacity plan, the TOPSIS method is used, including constructing an ideal positive solution and an ideal negative solution, and calculating the distance between each alternative plan and the two ideal solutions. Finally, all feasible plans are ranked by calculating the Euclidean distance with the ideal positive solution and the ideal negative solution, effectively quantifying the comprehensive performance of each plan and identifying the optimal capacity plan. Where: S WT , S PV They represent the set of wind power and photovoltaic installed capacity solutions respectively; C WT , C PV Respectively represent the maximum and minimum installed capacity of wind power and photovoltaic power, in MW; ΔC WT , ΔC PV Respectively represent the iteration step lengths of wind power and photovoltaic power; N represents the number of iterations; The specific indicators for evaluating the performance of energy bases under different capacity plans are as follows: a. Electricity delivered: the total annual electricity of the energy base, expressed as follows: Where, T represents the total duration of the scheduling cycle, unit: h; Δt represents the time step in the scheduling model, unit: h; P t Indicates the power transmission value in the power transmission curve mode agreed upon by the transmitter and receiver; b. Unit electricity cost: the cost required for the energy base to produce 1kWh of electricity, expressed as follows: In the formula, C inv It represents the investment and construction cost of the wind, solar and storage energy base, unit: Yuan; C OM It represents the operation and maintenance cost of the wind, solar and storage energy base, unit: Yuan; c. Abandonment rate: This is an important indicator to measure the operating efficiency of energy bases, reflecting the proportion of renewable energy power that cannot be used due to various reasons to the ideal power generation capacity: Where P t wc , P t pvc They represent the abandoned power of wind farm and photovoltaic power station at time t, in MW; C w , C pv Indicates the installed capacity of the wind farm, unit: MW; d. Power supply guarantee rate: the ratio of the number of time periods in which the energy base output meets the requirements to the total number of time periods, expressed as follows: Where P t s Indicates the load shortage at time t, unit: MW; e. Load power shortage rate: the ratio of the total load shortage to the total load demand: In the formula, C t It represents the load demand of the receiving power grid during period t; (2) Optimized operation model: As an inner model, the optimized operation model considers the following two operation modes: a. Free power transmission mode between transmitter and receiver Objective function: For wind, solar and storage energy bases, capacity planning is to maximize the comprehensive benefits of wind, solar and storage energy bases by comprehensively considering the complementary characteristics of various energy sources, rationally allocating wind and solar capacity, reducing wind and solar abandonment, and maximizing the comprehensive benefits of wind, solar and storage energy bases. The objective function is mainly composed of wind power generation, photovoltaic power generation, power sales revenue of pumped storage power stations, power abandonment penalties, load shortage penalties, and annualized operation and maintenance costs, which are expressed as follows: In the formula, f represents the total income of the wind, solar and storage energy base, unit: yuan; T represents the total duration of the scheduling cycle, unit: h; P t WT , P t PV It indicates the actual grid-connected power of wind farms and photovoltaic power stations at time t, in MW; c WT 、c PV Respectively represent the on-grid electricity price of wind power and photovoltaic power, unit: yuan / MWh; P t g , P t p They represent the power generation output and water output power of the pumped storage power station at time t, in MW; c PHS represents the electricity revenue of the pumped storage power station, which is the difference between the on-grid electricity price of the pumped storage power station and the pumped electricity price, unit: yuan / MWh; Δt represents the time step in the dispatch model, unit: h; P t wc , P t pvc , P t s Respectively represent the amount of power abandoned by wind farms and photovoltaic power stations and the amount of load shortage at time t, unit: MW; ω WT ,ω PV 、.ω s . Respectively represent wind power curtailment, photovoltaic power curtailment and load shortage penalty, unit: yuan / MWh; C inv It represents the investment and construction cost of the wind, solar and storage energy base, unit: Yuan; C OM It represents the operation and maintenance cost of the wind, solar and storage energy base, unit: Yuan; Pumped storage power station operation constraints; (1) Hydraulic connection between upstream and downstream reservoirs Where: Respectively represent the storage capacity of the upstream and downstream reservoirs at time t-1, unit: m 3 ; Respectively represent the storage capacity of the upstream and downstream reservoirs at time t, unit: m 3 ; Respectively represent the pumping flow and power generation flow of the pumped storage power station at time t, unit: m 3 / s; (2) Constraints on upstream and downstream reservoir capacity Where: Indicates the minimum storage capacity limit of the upstream and downstream reservoirs, unit: m 3 ; Respectively represent the maximum storage capacity limit of the upstream and downstream reservoirs, unit: m 3 ; (3) Constraints on initial and final storage capacity of upstream reservoirs Where: Indicates the storage capacity of the upstream reservoir at the initial time of the dispatch period, unit: m 3 ; Indicates the initial storage capacity of the upstream reservoir, unit: m 3 ; Indicates the storage capacity of the upstream reservoir at the end of the scheduling period, unit: m 3 ; Indicates the target storage capacity of the upstream reservoir at the end of the period, unit: m 3 ; (4) Unit operating status constraints Where: Respectively represent the minimum and maximum power output limits of the pumped storage power station, unit: MW; The state variables representing the power generation state and pumping state of the pumped storage power station at time t respectively; Indicates the maximum pumping power limit of the pumped storage power station, unit: MW; Respectively represent the minimum and maximum power generation flow limits of the pumped storage power station, unit: m 3 / s; (5) Constraints between net water head and upper and lower reservoir water levels Where: H t It represents the average generating head of the pumped storage power station at time t, unit: m; Respectively represent the water level of the upstream reservoir at time t and t-1, unit: m; Respectively represent the water level of the downstream reservoir at time t and t-1, unit: m; (6) Water level and storage capacity relationship function (7) Unit power characteristic curve constraints Where: k1 represents the calculation coefficient of the power output of the pumped storage power station; k2 represents the calculation coefficient of the pumped power of the pumped storage power station; (8) Minimum unit operating time constraints Where: The conversion variable representing the conversion of the pumped storage unit from the shutdown state to the power generation state at time t; The conversion variable indicating that the pumped storage unit is in the power generation state at time t; The state variables representing the power generation state of the pumped storage power station at time t and t-1 respectively; Indicates the power generation state of the pumped storage unit and the minimum operating duration of the unit under the pumping state; (9) UHV DC interconnection line transmission line operation constraints: The power transmission curve of the high-voltage DC interconnection line should be in a step-like shape and should not be adjusted in the opposite direction within a short period of time to avoid frequent adjustment of DC converter equipment and ensure the reliability of grid operation: Where: C max , C min Respectively represent the maximum and minimum transmission power limits of UHV DC transmission lines, in MW; M represents the number of steps of the power curve of the high-voltage DC interconnection line; (10) Constraints on the operation of new energy power plants P t WT =P t wi ·C w -P t wc P t PV =P t pvi ·C pv -P t pvc Where: P t WT , P t PV Respectively represent the actual output of the wind farm and photovoltaic farm at time t, unit: MW; P t wi , P t pvi They represent the theoretical output per unit value of the wind farm and photovoltaic farm at time t, in MW; C w , C pv Indicates the installed capacity of the wind farm, unit: MW; P t wc , P t pvc It represents the abandoned power of wind farm and photovoltaic farm at time t, unit: MW; (11) Energy base system constraints It is stipulated that the pumped storage power station cannot generate electricity when the energy base has power abandonment. When the energy base has power abandonment in period t, otherwise P t =P t wa +P t pva +P t g -P t p b. The transmission curve mode agreed upon by the transmitter and receiver Normally, the receiving power grid will formulate a power transmission plan in advance according to system requirements and agreements, that is, the energy base needs to transmit power according to the agreed power transmission curve; in this mode, the power balance constraint and transmission power constraint are adjusted accordingly, and the objective function and other constraints remain unchanged; Power balance constraint: In the agreed power transmission curve mode, the system output power is as close to the load curve as possible. When the power supply is insufficient, load shortage occurs: P t =C t -P t s 。 5. The method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission according to claim 1, characterized in that: In step d, the nonlinear factors appearing in the two-layer optimization model are linearized under the premise of meeting the accuracy requirements: (1) Constrained linearization of the dynamic characteristic curve of the pumped storage unit The dynamic characteristic curve constraint of the pumped storage unit is a system that contains two unknown parameters: The McCormick envelope inequality is used to transform this nonlinear constraint into a linear constraint: Where: H min H max Respectively represent the minimum and maximum water heads of the pumped storage power station unit, unit: m; (2) UHV DC interconnection line transmission constraint linearization The UHV DC transmission power constraint is highly discrete and nonlinear. Therefore, the binary variable and Convert the original nonlinear constraints into linearized form: Where: N T Indicates the maximum number of fluctuations in the amount of electricity online allowed by the energy base within a year; δ min , δ max Respectively represent the minimum and maximum value of system power change, unit: MW / h; are state variables representing the upward and downward fluctuation of the online power of the energy base at time t; N k Indicates the minimum output duration of system power stability, unit: h.

6. The method for planning installed capacity of a large-scale wind-solar-storage energy base based on UHV DC transmission according to claim 1, characterized in that: In step e, case analysis verifies the effectiveness of the proposed capacity planning method, and proposes corresponding operation strategies for the calculation of the power supply guarantee rate and power abandonment rate of the wind-solar-energy-storage complementary system in different seasons and under different load demands based on the local power transmission curve.

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