WO2023060815A1 - 一种提高配电网可靠性的储能容量优化配置方法 - Google Patents

一种提高配电网可靠性的储能容量优化配置方法 Download PDF

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WO2023060815A1
WO2023060815A1 PCT/CN2022/077253 CN2022077253W WO2023060815A1 WO 2023060815 A1 WO2023060815 A1 WO 2023060815A1 CN 2022077253 W CN2022077253 W CN 2022077253W WO 2023060815 A1 WO2023060815 A1 WO 2023060815A1
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energy storage
storage system
power
capacity
reliability
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French (fr)
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周柯
丘晓茵
秦丽文
莫枝阅
李珊
周杨珺
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广西电网有限责任公司电力科学研究院
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
    • H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/28—Arrangements for balancing of the load in a network by storage of energy
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/10—Flexible AC transmission systems [FACTS]

Definitions

  • the invention relates to the technical field of distribution networks, in particular to an energy storage capacity optimization configuration method for improving the reliability of distribution networks.
  • the traditional power grid "peak shaving and valley filling” technology usually uses the method of inputting and cutting loads to maintain the stability of the voltage and frequency of the large power grid, but this method consumes a lot of energy, and causes poor user experience and low power supply reliability.
  • BESS battery energy storage systems
  • the electricity is fed back to the power grid, which effectively realizes the "peak-shaving and valley-filling" of the power grid.
  • This method effectively stabilizes the fluctuation of grid voltage and frequency, and improves the quality of power supply for users.
  • the manufacturing cost and maintenance cost of energy storage also increase. How to optimize the power and capacity of energy storage in the distribution network and compromise the benefits of energy storage The cost of energy has become a real problem.
  • CN 104851053 A discloses a method for evaluating the reliability of a distribution network connected to a wind-storage complementary micro-grid.
  • CN104851053 A Disclosed is a method for evaluating the reliability of distribution network power supply with wind power storage.
  • CN110188998 A discloses a method for evaluating the reliability of the timing construction of wind turbines and energy storage in the distribution network.
  • the above patent documents have studied the improved access
  • the distribution network reliability evaluation method for wind, wind and storage devices has improved the accuracy of the evaluation of the impact of energy storage on the power supply reliability of the distribution network.
  • CN108551175 B discloses a method for configuring distribution network energy storage capacity
  • CN112260300 A discloses a method and device for determining energy storage configuration and optimal delay period
  • the above-mentioned patent documents have studied the optimization of distribution network energy storage capacity, effectively The fluctuation of the grid voltage is stabilized, but the economic cost is not considered, which has certain limitations.
  • CN108599206 B discloses a distribution network hybrid energy storage configuration method in a high-proportion uncertain power source scenario.
  • CN112232983 A discloses an active distribution network energy storage optimization configuration method, electronic equipment and storage media, and studies the distribution network energy storage configuration method from the perspective of considering voltage fluctuation suppression ability and energy storage economy, but does not consider The reliability benefits of energy performance are mainly considered from the perspective of the electricity market.
  • CN110061492 A discloses a method for optimizing the configuration of energy storage system capacity considering the reliability of distribution network power supply.
  • the method for optimizing the distribution network energy storage capacity can be effectively solved.
  • the actual demand of the distribution network scenario of important loads, but for ordinary power loads, the maximum emergency support capacity is not focused, and more attention is paid to the reliability power supply index, so as to obtain better benefits on the basis of improving power supply reliability.
  • the traditional energy storage capacity optimization configuration method considers power flow constraints, voltage fluctuations, etc., but does not comprehensively consider the reliability and economic benefits of the distribution network connected to energy storage.
  • the power grid it is more concerned about how to The use of energy storage improves the reliability of power supply in a specific area while improving economic efficiency.
  • traditional methods are mostly based on comparison and enumeration calculation methods, and it takes a long time to search for the optimal operating point.
  • the technical problem to be solved by the present invention is to overcome the deficiencies of the above-mentioned background technology, provide a method for optimizing the allocation of energy storage capacity to improve the reliability of the distribution network, and solve the problem that the existing method does not consider the connection of energy storage to the distribution network.
  • the problem of comprehensive optimization of reliability benefits and economic benefits is to achieve the optimal economic allocation of energy storage under the premise of improving the reliability of the distribution network.
  • the technical solution adopted by the present invention to solve the technical problem is a method for optimizing the allocation of energy storage capacity to improve the reliability of the distribution network, including the following steps:
  • C total is the construction cost of the energy storage system
  • f 1 is the economic benefit of configuring the energy storage system
  • f 2 is the reliability benefit of configuring the energy storage system
  • step (1) the economic benefit f 1 of configuring the energy storage system can be expressed as:
  • B 1 represents the economic benefits of energy storage "peak shaving”
  • B 2 represents the reduced income of generator assembly capacity
  • P id represents the discharge power of the energy storage system in the i-th hour
  • P ie represents the charging power of the energy storage system in the i-th hour
  • R i is the real-time electricity price in the i-th hour
  • the reduced generator installed capacity revenue B2 can be expressed as:
  • k s represents the price of unit installed capacity
  • ⁇ is the asset depreciation rate
  • Ph is the power of the energy storage system when the load reaches the maximum value
  • the reliability gain f2 of configuring the energy storage system can be expressed as:
  • M represents the number of load points
  • K j represents the number of power outages of load point j
  • P jk is the load value of load point j at the kth power outage
  • T OFFjk is the power outage time of load point j at the kth power outage
  • C Ljk is The average power failure loss of load point j during the kth power failure
  • the construction cost C total of the energy storage system can be expressed as:
  • C INESS represents the one-time construction cost of the energy storage system
  • C RESS represents the total maintenance cost of the energy storage system
  • the one-time construction cost C INESS of the energy storage system can be expressed as:
  • k e is the expenditure of the energy storage system per unit capacity
  • k p is the expenditure of the converter per unit power
  • k f is the expenditure of accessories per unit power
  • E N represents the capacity of the energy storage system
  • P N represents the energy storage system power
  • the total maintenance cost C RESS of the energy storage system can be expressed as:
  • k r is the maintenance expenditure per unit power.
  • Monte Carlo simulation is used to obtain the average power supply availability index and power shortage expectation index of the system, and the power range of the energy storage system is selected according to the average power supply availability ratio index and power shortage expectation value index.
  • the power interval of the energy storage system is selected according to the average power supply availability index and the expected power shortage index.
  • the specific method is: draw the curve of the average power supply availability index and the expected power shortage index with the power change; select the minimum value of the expected power shortage index The sum of the corresponding power value and the power value corresponding to the maximum value of the power shortage expectation value index divided by 2 is determined as the reference power P N1 of the energy storage system; select the power value corresponding to the minimum value of the average power supply availability index and the average power supply availability rate The sum of the power values corresponding to the maximum index value divided by 2 is determined as the reference power P N2 of the energy storage system; the power range expression of the energy storage system is (1 ⁇ 10%)(P N1 +P N2 ).
  • step (3) the Monte Carlo method is used to estimate the "revenue-input ratio" of energy storage systems with different capacities configured in the power supply network.
  • step (3) the Monte Carlo method is used to estimate the "benefit-input ratio" of energy storage systems with different capacities configured in the power supply network, including the following steps:
  • step (4) according to the "revenue-input ratio" of the energy storage system under the current capacity, the particle swarm optimization algorithm is used to search for the optimal configuration capacity of the energy storage system.
  • step (4) the optimal configuration capacity of the energy storage system is searched using the particle swarm optimization algorithm, including the following steps:
  • the configuration parameters of initializing the particle swarm optimization algorithm are specifically: setting the maximum number of iterations, the number of independent variables, and the maximum particle velocity; the independent variable of the particle swarm optimization algorithm is the capacity of the energy storage system; setting the particle swarm optimization The initial velocity and position of , set the particle swarm size to M.
  • step (4-4) the formula for updating speed and position is expressed as:
  • ⁇ ( ⁇ 0) is the inertia weight
  • C 1 is the individual learning factor
  • C 2 is the social learning factor
  • random(0,1) represents any value between 0 and 1
  • P id is the i-th particle independent variable
  • P gd is the d-th dimension variable of the global optimal solution
  • X id is the d-th dimension variable of the i-th particle’s last position
  • V id is the d-th-dimensional variable of the i-th particle’s velocity set .
  • the present invention evaluates the comprehensive benefits of energy storage configuration reliability benefits and economic benefits, and proposes an energy storage capacity optimization configuration method based on particle swarm algorithm to improve the reliability of distribution network, which can intelligently and efficiently plan energy storage
  • the configuration of power and capacity realizes the comprehensive optimization of reliability benefits and economic benefits of energy storage configuration, and has the advantage of a high degree of intelligence.
  • Fig. 1 is a flow chart of the method of the embodiment of the present invention.
  • Fig. 2 is a flow chart of estimating the "revenue-input ratio" of energy storage systems with different capacities configured in the power supply network using the Monte Carlo method according to an embodiment of the present invention.
  • FIG. 3 is a schematic structural diagram of an IEEE-34 system according to an embodiment of the present invention.
  • Fig. 4 is a schematic diagram of an average power supply availability index of an IEEE-34 system according to an embodiment of the present invention.
  • Fig. 5 is a schematic diagram of an expected power-shortage index of an IEEE-34 system according to an embodiment of the present invention.
  • Fig. 6 is a schematic diagram of the optimal "revenue-input ratio" of the energy storage system according to the embodiment of the present invention.
  • the present invention evaluates the comprehensive benefits of energy storage allocation reliability benefits and economic benefits, and proposes an energy storage capacity optimization configuration method based on a particle swarm algorithm to improve the reliability of a distribution network.
  • present embodiment method comprises the following steps:
  • C total is the construction cost of the energy storage system
  • f 1 is the economic benefit of configuring the energy storage system
  • f 2 is the reliability benefit of configuring the energy storage system.
  • the economic benefit f 1 of configuring the energy storage system can be expressed as:
  • B 1 represents the economic benefits of energy storage "peak shaving”
  • B 2 represents the income from the reduced installed capacity of generators.
  • P id represents the discharge power of the energy storage system in the i-th hour
  • P ie represents the charging power of the energy storage system in the i-th hour
  • R i is the real-time electricity price in the i-th hour.
  • the reduced generator installed capacity revenue B2 can be expressed as:
  • k s represents the price of unit installed capacity
  • ⁇ is the asset depreciation rate
  • Ph is the power of the energy storage system when the load reaches the maximum value.
  • the reliability gain f2 of configuring the energy storage system can be expressed as:
  • M represents the number of load points
  • K j represents the number of power outages of load point j
  • P jk is the load value of load point j at the kth power outage
  • T OFFjk is the power outage time of load point j at the kth power outage
  • C Ljk is The average outage loss of load point j during the kth outage.
  • the construction cost C total of the energy storage system can be expressed as:
  • C INESS represents the one-time construction cost of the energy storage system
  • C RESS represents the total maintenance cost of the energy storage system
  • the one-time construction cost C INESS of the energy storage system can be expressed as:
  • k e is the expenditure of energy storage system per unit capacity.
  • k p is the expenditure of the converter per unit power,
  • k f is the expenditure of accessories per unit power,
  • E N represents the capacity of the energy storage system, and
  • PN represents the power of the energy storage system.
  • the total maintenance cost C RESS of the energy storage system can be expressed as:
  • k r is the maintenance expenditure per unit power.
  • ASAI average service availability index
  • EENS expected energy not supplied
  • ASAI and EENS select the power range of the energy storage system, specifically: draw the curves of ASAI and EENS with power change respectively; select the sum of the power value corresponding to the minimum value of EENS and the power value corresponding to the maximum value of EENS and divide it by 2 to determine is the reference power P N1 of the energy storage system; select the sum of the power value corresponding to the ASAI minimum value and the power value corresponding to the ASAI maximum value and divide by 2 to determine the reference power P N2 of the energy storage system; the power range of the energy storage system
  • the expression is (1 ⁇ 10%)(P N1 +P N2 ), ensuring that the power of the energy storage system is not too large or too small.
  • Select the capacity interval according to the power interval of the energy storage system, E N P N t, E N represents the capacity of the energy storage system,
  • Step S2 Randomly generate faults for each load point in the system
  • Step S3 Determine whether there is a fault at each load point at the current time t; if yes, go to step S4, if not, go to step S8;
  • Step S4 Determine whether the power supply at the load point can be restored; if yes, proceed to step S5; if not, the entire system is powered off;
  • Step S5 Determine whether the energy storage system is normal; if yes, go to step S6; if not, power off the entire system;
  • Step S6 Determine whether the power P BESS of the energy storage system is greater than the power P load of the current load; if yes, proceed to step S7; if not, maintain the power outage in the power outage area;
  • Step S7 Determine whether the remaining capacity of the energy storage system can maintain the normal operation of the system for one hour, that is, determine whether E BESS -E min > P load , E BESS is the current charging capacity of the energy storage system, and E min is the energy storage system's Minimum charging capacity; if yes, restore power to the power outage area; if not, maintain power outage in the power outage area;
  • Step S9 Determine whether T ⁇ t; if yes, return to step S3; if no, enter step S10;
  • Step S10 Calculate the "income-input ratio" of the energy storage system under the current capacity.
  • ⁇ ( ⁇ 0) is the inertia weight.
  • C 1 is the individual learning factor, and C 2 is the social learning factor.
  • random(0,1) represents any value between 0 and 1.
  • P id is the d-th dimension variable of the i-th particle independent variable.
  • P gd is the d-th dimension variable of the global optimal solution.
  • X id is the d-th dimension variable of the last position of the i-th particle.
  • V id is the d-th dimension variable of the velocity set of the i-th particle.
  • the method proposed in this embodiment is used to test and verify on the nodes of the IEEE-34 standard system.
  • the structure of the IEEE-34 is shown in FIG. 3 .
  • the failure rate of each load node in IEEE-34 is shown in the matrix Lamda, and the mean time to repair (MTTR) of each point is shown in the matrix MTTR34:
  • Lambda [0.3979;0.8209;0.7666;0.1206;0.8577;0.2586;0.7049;0.2429;0.3521;0.8118;0.1850;0.5135;0.3920;0.7194;0.6776;0.2065;0.3197;0.3226;0.8566;0.3348;0.8950;0.8833;0.6400;0.0412 ;0.0518;0.9458;0.2257;0.7303;0.2191;0.0101;0.7205;0.1289;0.1327].
  • MTTR34 [0.3546;0.6970;0.8490;0.8724;0.0411;0.2098;0.7382;0.7379;0.1978;0.4534;0.2299;0.0704;0.3979;0.8555;0.6809;0.2954;0.8536;0.7195;0.3405;0.0495;0.0174;0.7846;0.2554;0.6597 ;0.8496;0.2965;0.2238;0.0066;0.0684;0.4306;0.7953;0.7759;0.9673].
  • the access location of the energy storage system has been determined, that is, it is connected to the load point 890 .
  • the power of the energy storage system needs to be determined.
  • the average service availability index (ASAI) and expected energy not supplied (EENS) of the system are calculated according to Lamda and MTTR34, as shown in Fig. 4 and Figure 5. It can be seen from Figure 4 and Figure 5 that when the output power of the energy storage system is greater than 2MW, the improvement rate of system reliability slows down.
  • the ASAI is increased to 99.958%, and the EENS is reduced to 6000kW ⁇ h.
  • the rated power of the energy storage system is set to 1MW. After determining the rated power of the energy storage system, determine the capacity of the energy storage system, and calculate the optimal "revenue-input ratio" of the energy storage system, as shown in Figure 6. The results show that the process has been iteratively updated five times. Through the calculation of this method, when the energy storage system capacity is set to 2.8 kW h, the best cost-benefit ratio can be obtained as 0.0128. That is, by configuring the power of the energy storage system to be 1MW, the benefits of the energy storage system running for 8 years can recover the cost.
  • the IEEE-34 standard node system is selected for the researched system, and the general load model is selected for calculation regardless of the load type.
  • the core indicators of the key parameters of the energy storage capacity configuration calculation using the method of the present invention are shown in Table 1, involving the cost calculation index parameters of the energy storage system, single power outage loss, asset depreciation rate, etc.
  • the invention proposes the definition of "revenue-input ratio", and based on this, proposes a comprehensive allocation method of distribution network energy storage capacity that takes into account both economy and reliability, which can realize the optimal comprehensive income of distribution network energy storage configuration .
  • the proposed energy storage capacity optimization configuration method based on particle swarm optimization algorithm avoids the parameter trial and enumeration calculation of the traditional method, and realizes the intelligent planning of distribution network energy storage configuration. It solves the problem that the existing method does not consider the comprehensive optimization of the reliability and economic benefits of energy storage access to the distribution network, and can intelligently and efficiently plan the configuration of energy storage power and capacity, realizing the reliability of energy storage configuration.
  • the comprehensive optimization of sexual and economic benefits has the advantage of high intelligence.

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Abstract

一种提高配电网可靠性的储能容量优化配置方法，包括以下步骤：(1)定义储能系统的"收益-投入比"：式（I）其中，Ctotal为储能系统的建设成本，f1为配置储能系统的经济性收益，f2为配置储能系统的可靠性收益；(2)选择储能系统的功率区间，根据储能系统的功率区间选择容量区间；(3)估计供电网络中配置不同容量的储能系统的"收益-投入比"；(4)根据当前容量下储能系统的"收益-投入比"，搜索储能系统最佳的配置容量，即储能系统最大的"收益-投入比"下的配置容量为储能系统最佳的配置容量。本发明能够智能高效地规划储能功率与容量的配置，实现储能配置的可靠性收益与经济性收益的综合最优，具有智能化程度高的优点。

Description

一种提高配电网可靠性的储能容量优化配置方法 技术领域
本发明涉及配电网技术领域，具体是涉及一种提高配电网可靠性的储能容量优化配置方法。
背景技术
随着家庭用电负荷规模逐渐增长，电力系统配电网的供电压力逐渐增大。近年来，为缓解电力系统压力，需要执行“削峰填谷”的措施，以平衡电网的发电侧与负荷侧，并提高母线电压频率的稳定性。
传统的电网“削峰填谷”技术通常使用投入与切出负荷的方式维持大电网电压与频率的稳定，但该方式的能量损耗大，且造成了用户使用体验差，供电可靠性低的难题。随着电力电子技术的发展，大量的储能系统(battery energy storage systems，BESS)通过电力电子装置接入电力系统，储能通过在用电低谷期时吸收电网电能，在用电高峰期将多余的电量回馈至电网，有效地实现了电网的“削峰填谷”。该方式有效平抑了电网电压、频率的波动，并提高了用户供电质量。但是，随着储能的功率与容量的增大，储能的制造成本与维护成本也随之提高，如何对配电网中储能的功率与容量进行优化，折中储能的收益与储能的成本，成为现实难题。
现有的研究中考虑了新能源与储能的接入对配电网可靠性的影响，如CN 104851053 A公开了一种接入风储互补微网的配电网可靠 性评估方法，CN104851053 A公开了一种含风光储的配电网供电可靠性评估方法，CN110188998 A公开了一种配电网的风电机组和储能的时序建设可靠性的评估方法，上述专利文献研究了改进的接入风光储等装置的配电网可靠性评估方法，使得储能对配电网的供电可靠性的影响的评估精度得到了一定的提高。随着接入配电网的储能的规模增加，需要针对配电网的储能容量优化，以降低电网电压波动程度。CN108551175 B公开了一种配电网储能容量配置方法，CN112260300 A公开了一种储能配置及最佳延缓年限的确定方法及装置，上述专利文献研究了配电网储能容量的优化，有效平抑了电网电压的波动，但是未考虑经济性成本，具有一定局限性。CN108599206 B公开了一种高比例不确定电源场景下的配电网混合储能配置方法，该方法采用了非线性规划的方式进行了新能源的优化配置，但是该方法使用的约束条件为非线性模型进行线性化的结果，导致容易获取到真实模型的极大值点但是难以获得全局最优。CN112232983 A公开了一种主动配电网储能优化配置方法、电子设备及存储介质，从考虑电压波动抑制能力与储能经济性的角度出发研究了配电网储能配置方法，但是未考虑储能的可靠性收益，主要从电力市场的角度进行了考虑。CN110061492 A公开了一种考虑配电网供电可靠性的储能系统容量优化配置方法，从建设成本最低并提供最大紧急功率支撑的角度研究了配电网储能容量优化的办法，可以有效解决含有重要负荷的配电网场景的实际需求，但是对于普通用电负荷并不重点关注最大紧急支撑 能力，更关注可靠性供电指标，以在提高供电可靠性的基础上获得较好的收益。
因此，传统的储能容量优化配置方法多从潮流约束、电压波动等方面进行考虑，但未综合考虑接入储能的配电网可靠性收益与经济性收益，对于电网来说，更加关心如何使用储能提升特定区域的供电可靠性的同时提高经济性。同时，传统的方法多基于比较与枚举计算方法，搜索最优运行点耗时长。
发明内容
本发明所要解决的技术问题是，克服上述背景技术的不足，提供一种提高配电网可靠性的储能容量优化配置方法，解决了现有方法中未考虑储能接入配电网中的可靠性收益与经济性收益综合最优的问题，在提高配电网可靠性的前提下实现储能的最优经济性配置。
本发明解决其技术问题采用的技术方案是，一种提高配电网可靠性的储能容量优化配置方法，包括以下步骤：
(1)定义储能系统的“收益-投入比”：
Figure PCTCN2022077253-appb-000001
其中，C total为储能系统的建设成本，f 1为配置储能系统的经济性收益，f 2为配置储能系统的可靠性收益；
(2)选择储能系统的功率区间，根据储能系统的功率区间选择容量区间；
(3)估计供电网络中配置不同容量的储能系统的“收益-投入 比”；
(4)根据当前容量下储能系统的“收益-投入比”，搜索储能系统最佳的配置容量，即储能系统最大的“收益-投入比”下的配置容量为储能系统最佳的配置容量。
进一步，步骤(1)中，所述配置储能系统的经济性收益f 1可表示为：
f 1＝B 1+B 2    (2)
其中，B 1表示储能“削峰”的经济效益，B 2表示减少的发电机组装机容量收益；
其中，储能“削峰”的经济效益B 1可表示为：
Figure PCTCN2022077253-appb-000002
其中，P id表示储能系统在第i个小时的放电功率，P ie表示储能系统在第i个小时的充电功率，R i为第i个小时的实时电价；
其中，减少的发电机组装机容量收益B 2可表示为：
B 2＝λk sP h   (4)
其中，k s表示单位装机容量的价格，λ为资产折旧率，P h为负载达到最大值时储能系统的功率；
其中，配置储能系统的可靠性收益f 2可表示为：
Figure PCTCN2022077253-appb-000003
其中，M表示负载点数，K j表示负载点j的停电次数，P jk为第k次停电时负载点j的负荷值，T OFFjk为第k次停电时负载点j的停电时间，C Ljk为第k次停电时负载点j的平均停电损失；
其中，储能系统的的建设成本C total可表示为：
C total＝C INESS+C RESS     (6)
其中，C INESS表示储能系统的一次性建设成本，C RESS表示储能系统的总维护费用；
其中，储能系统的一次性建设成本C INESS可表示为：
C INESS＝k eE N+(k p+k f)P N     (7)
其中，k e为单位容量的储能系统的支出；k p为单位功率的变换器的支出，k f为单位功率的配件支出，E N表示储能系统的容量，P N表示储能系统的功率；
其中，储能系统的总维护费用C RESS可表示为：
C RESS＝k rP N    (8)
其中，k r为单位功率的维修支出。
进一步，采用蒙特卡洛法模拟得到系统的平均供电可用率指标与缺电期望值指标，并根据平均供电可用率指标与缺电期望值指标选择储能系统的功率区间。
进一步，根据平均供电可用率指标与缺电期望值指标选择储能系统的功率区间，具体方法为：绘制平均供电可用率指标与缺电期望值 指标随着功率变化的曲线；选择缺电期望值指标最小值对应的功率值与缺电期望值指标最大值对应的功率值相加的和除以2定为储能系统的基准功率P N1；选择平均供电可用率指标最小值对应的功率值与平均供电可用率指标最大值对应的功率值相加的和除以2定为储能系统的基准功率P N2；储能系统的功率区间表达式为(1±10％)(P N1+P N2)。
进一步，步骤(3)中，使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”。
进一步，步骤(3)中，使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”，包括以下步骤：
(3-1)设定蒙特卡洛最大模拟时长T，初始化模拟时间t＝0；
(3-2)在系统中针对各个负荷点随机产生故障；
(3-3)判断各个负荷点当前时间t下是否有故障；
(3-4)若有故障，判断负荷点的供电是否可以恢复；
(3-5)若可以恢复，判断储能系统是否正常；
(3-6)若正常，判断储能系统的功率P BESS是否大于当前负荷的功率P load；
(3-7)若大于，判断储能系统的剩余容量是否可以维持系统正常运转一小时；
(3-8)若可以，停电区域恢复供电；
(3-9)重复步骤(3-2)～(3-8)，直至模拟时间t达到最大模拟时长T；
(3-10)计算当前容量下储能系统的“收益-投入比”。
进一步，步骤(4)中，根据当前容量下储能系统的“收益-投入比”，利用粒子群算法搜索储能系统最佳的配置容量。
进一步，步骤(4)中，利用粒子群算法搜索储能系统最佳的配置容量，包括以下步骤：
(4-1)初始化粒子群算法的配置参数；
(4-2)定义适应度函数为步骤(1)中定义的储能系统的“收益-投入比”，粒子的个体适应度函数的最大值为每个粒子的“个体最优解”；
(4-3)比较所有粒子的个体适应度函数的最大值，选出其中的最大值，定义为“全局最优解”；
(4-4)将当前“全局最优解”与历史“全局最优解”进行比较，对自变量的粒子速度、位置进行更新；
(4-5)判断是否满足终止迭代操作的条件，如果当前迭代次数达到设定的大迭代次数，终止迭代，选取所有“全局最优解”中最大值对应的配置容量为储能系统最佳的配置容量；否则，返回步骤(3)继续执行。
进一步，步骤(4-1)中，初始化粒子群算法的配置参数具体为：设置最大迭代次数，自变量个数，最大粒子速度；粒子群算法的自变量为储能系统的容量；设置粒子群的初始速度和位置，设置粒子群大 小为M。
进一步，步骤(4-4)中，更新速度和位置的公式表示为：
Figure PCTCN2022077253-appb-000004
其中ω(ω≥0)为惯性权重；C 1为个体学习因子，C 2为社会学习因子；random(0,1)代表0到1之间的任意值；P id是第i个粒子自变量的第d维变量；P gd是全局最优解的第d维变量；X id是第i个粒子上一次位置的第d维变量；V id是第i个粒子的速度集的第d维变量。与现有技术相比，本发明的优点如下：
本发明针对储能配置可靠性收益与经济性收益的综合性收益进行评估，提出了一种基于粒子群算法的提高配电网可靠性的储能容量优化配置方法，能够智能高效地规划储能功率与容量的配置，实现储能配置的可靠性收益与经济性收益的综合最优，具有智能化程度高的优点。
附图说明
图1是本发明实施例方法流程图。
图2是本发明实施例使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”的流程图。
图3是本发明实施例IEEE-34系统的结构示意图。
图4是本发明实施例IEEE-34系统的平均供电可用率指标的示意图。
图5是本发明实施例IEEE-34系统的缺电期望值指标的示意图。
图6是本发明实施例储能系统的最优“收益-投入比”示意图。
具体实施方式
下面结合附图及具体实施例对本发明作进一步详细描述。
本发明针对储能配置可靠性收益与经济性收益的综合性收益进行评估，提出了一种基于粒子群算法的提高配电网可靠性的储能容量优化配置方法。
参照图1，本实施例方法，包括以下步骤：
(1)定义储能系统的“收益-投入比”：
Figure PCTCN2022077253-appb-000005
其中，C total为储能系统的建设成本，f 1为配置储能系统的经济性收益，f 2为配置储能系统的可靠性收益。
其中，配置储能系统的经济性收益f 1可表示为：
f 1＝B 1+B 2   (2)
其中，B 1表示储能“削峰”的经济效益，B 2表示减少的发电机组装机容量收益。
其中，储能“削峰”的经济效益B 1可表示为：
Figure PCTCN2022077253-appb-000006
其中，P id表示储能系统在第i个小时的放电功率，P ie表示储能系统在第i个小时的充电功率，R i为第i个小时的实时电价。
其中，减少的发电机组装机容量收益B 2可表示为：
B 2＝λk sP h      (4)
其中，k s表示单位装机容量的价格，λ为资产折旧率，P h为负载达到最大值时储能系统的功率。
其中，配置储能系统的可靠性收益f 2可表示为：
Figure PCTCN2022077253-appb-000007
其中，M表示负载点数，K j表示负载点j的停电次数，P jk为第k次停电时负载点j的负荷值，T OFFjk为第k次停电时负载点j的停电时间，C Ljk为第k次停电时负载点j的平均停电损失。
其中，储能系统的的建设成本C total可表示为：
C total＝C INESS+C RESS      (6)
其中，C INESS表示储能系统的一次性建设成本，C RESS表示储能系统的总维护费用。
其中，储能系统的一次性建设成本C INESS可表示为：
C INESS＝k eE N+(k p+k f)P N     (7)
其中，k e为单位容量的储能系统的支出。k p为单位功率的变换器的支出，k f为单位功率的配件支出，E N表示储能系统的容量，P N表示储能系统的功率。
其中，储能系统的总维护费用C RESS可表示为：
C RESS＝k rP N    (8)
其中，k r为单位功率的维修支出。
(2)假设储能系统的容量为无穷大，使用蒙特卡洛法模拟得到系统的平均供电可用率指标(average service availability index，ASAI)与缺电期望值指标(expected energy not supplied，EENS)，并根据ASAI与EENS选择储能系统的功率区间，具体为：分别绘制ASAI与EENS随着功率变化的曲线；选择EENS最小值对应的功率值与EENS最大值对应的功率值相加的和除以2定为储能系统的基准功率P N1；选择ASAI最小值对应的功率值与ASAI最大值对应的功率值相加的和除以2定为储能系统的基准功率P N2；储能系统的功率区间表达式为(1±10％)(P N1+P N2)，确保储能系统的功率不过大或者过小。根据储能系统的功率区间选择容量区间，E N＝P Nt，E N表示储能系统的容量，P N表示储能系统的功率，t表示时间。
(3)使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”。执行过程如图2所示。
步骤S1：设定蒙特卡洛最大模拟时长T，初始化模拟时间t＝0(小时)；
步骤S2：在系统中针对各个负荷点随机产生故障；
步骤S3：判断各个负荷点当前时间t下是否有故障；如果是，进入步骤S4，如果否，进入步骤S8；
步骤S4：判断负荷点的供电是否可以恢复；如果是，进入步骤S5；如果否，整个系统停电；
步骤S5：判断储能系统是否正常；如果是，进入步骤S6；如果否，整个系统停电；
步骤S6：判断储能系统的功率P BESS是否大于当前负荷的功率P load；如果是，进入步骤S7；如果否，停电区域维持停电；
步骤S7：判断储能系统的剩余容量是否可以维持系统正常运转一小时，即判断是否E BESS-E min＞P load，E BESS为储能系统当前的荷电容量，E min为储能系统的最低荷电容量；如果是，停电区域恢复供电；如果否，停电区域维持停电；
步骤S8：t＝t+1；
步骤S9：判断是否T≥t；如果是，返回步骤S3；如果否，进入步骤S10；
步骤S10：计算当前容量下储能系统的“收益-投入比”。
(4)根据当前容量下储能系统的“收益-投入比”，利用粒子群算法搜索储能系统最佳的配置容量，即储能系统最大的“收益-投入比”下的配置容量为储能系统最佳的配置容量。具体过程包括以下步骤：
(4-1)初始化粒子群算法的配置参数：设置最大迭代次数，自变量个数，最大粒子速度；粒子群算法的自变量为储能系统的容量；设置粒子群的初始速度和位置，设置粒子群大小为M；
(4-2)定义适应度函数为步骤(1)中定义的储能系统的“收益-投入比”，粒子的个体适应度函数的最大值为每个粒子的“个体最优解”；
(4-3)比较所有粒子的个体适应度函数的最大值，选出其中的最大值，定义为“全局最优解”。粒子群寻优算法(particle swarm optimization，PSO)的全局搜索目标即寻找储能系统容量变化的条件下的最大的“收益-投入比”。
(4-4)将当前“全局最优解”与历史“全局最优解”进行比较，对自变量的粒子速度、位置进行更新。更新速度和位置的公式表示为：
Figure PCTCN2022077253-appb-000008
其中ω(ω≥0)为惯性权重。C 1为个体学习因子，C 2为社会学习因子。random(0,1)代表0到1之间的任意值。P id是第i个粒子自变量的第d维变量。P gd是全局最优解的第d维变量。X id是第i个粒子上一次位置的第d维变量。V id是第i个粒子的速度集的第d维变量。
(4-5)判断是否满足终止迭代操作的条件，如果当前迭代次数达到设定的大迭代次数，终止迭代，选取所有“全局最优解”中最大值对应的配置容量为储能系统最佳的配置容量。否则，返回步骤(3)继续执行。
利用本实施例提出的方法在IEEE-34标准系统的节点上进行测试验证，IEEE-34的结构如图3所示。IEEE-34的各负荷节点的失效率如矩阵Lamda，各点的平均修复时间(mean time to repair，MTTR) 如矩阵MTTR34：
Lambda＝[0.3979；0.8209；0.7666；0.1206；0.8577；0.2586；0.7049；0.2429；0.3521；0.8118；0.1850；0.5135；0.3920；0.7194；0.6776；0.2065；0.3197；0.3226；0.8566；0.3348；0.8950；0.8833；0.6400；0.0412；0.0518；0.9458；0.2257；0.7303；0.2191；0.0101；0.7205；0.1289；0.1327]。
MTTR34＝[0.3546；0.6970；0.8490；0.8724；0.0411；0.2098；0.7382；0.7379；0.1978；0.4534；0.2299；0.0704；0.3979；0.8555；0.6809；0.2954；0.8536；0.7195；0.3405；0.0495；0.0174；0.7846；0.2554；0.6597；0.8496；0.2965；0.2238；0.0066；0.0684；0.4306；0.7953；0.7759；0.9673]。
假设储能系统的接入位置已经确定，即与负载点890相连。首先需要确定储能系统的功率。在不考虑储能系统容量约束的情况下，根据Lamda与MTTR34计算得到系统的平均供电可用率指标(average service availability index，ASAI)与缺电期望值指标(expected energy not supplied，EENS)，分别如图4与图5所示。由图4与图5可知，当储能系统输出功率大于2MW时，系统可靠性的提高率减缓。同时，2MW的储能系统接入系统后，ASAI提高到99.958％，EENS降低到6000kW·h。但是，储能系统的功率过大可能会导致资源的浪费。因此，为了协调可靠性效益和储能系统成本，将储能系统的额定功率设置为1MW。在确定储能系统的额定功率后，确定储能系统的容量， 计算得到储能系统的最优“收益-投入比”如图6所示，结果显示该过程迭代更新了五次。通过对该方法的计算，当储能系统容量设置为2.8kW·h时，可得到最佳的成本效益比为0.0128。即通过配置储能系统的功率为1MW，储能系统运行8年的效益可收回成本。
被研究系统选用了IEEE-34标准节点系统，不考虑负荷类型，选择通用的负荷模型进行计算。使用本发明方法进行储能容量配置计算的关键参数的核心指标如表1所示，涉及了储能系统的成本计算指标参数，单次停电损失，资产折旧率等。
表1储能容量配置计算的主要参数
Figure PCTCN2022077253-appb-000009
本发明提出了“收益-投入比”的定义，并基于此提出了一种兼顾经济性与可靠性的配电网储能容量综合配置方法，能够实现配电网储能配置的综合收益最优。提出的基于粒子群算法的储能容量优化配置方法避免了传统方法的参数试凑与枚举计算，实现了配电网储能配置智能规划。解决了现有方法中未考虑储能接入配电网中的可靠性收益与经济性收益综合最优的问题，能够智能高效地规划储能功率与容量的配置，实现了储能配置的可靠性收益与经济性收益的综合最 优，具有智能化程度高的优点。
本领域的技术人员可以对本发明进行各种修改和变型，倘若这些修改和变型在本发明权利要求及其等同技术的范围之内，则这些修改和变型也在本发明的保护范围之内。
说明书中未详细描述的内容为本领域技术人员公知的现有技术。

Claims (10)

  1. 一种提高配电网可靠性的储能容量优化配置方法，其特征在于，包括以下步骤：
    (1)定义储能系统的“收益-投入比”：
    Figure PCTCN2022077253-appb-100001
    其中，C total为储能系统的建设成本，f 1为配置储能系统的经济性收益，f 2为配置储能系统的可靠性收益；
    (2)选择储能系统的功率区间，根据储能系统的功率区间选择容量区间；
    (3)估计供电网络中配置不同容量的储能系统的“收益-投入比”；
    (4)根据当前容量下储能系统的“收益-投入比”，搜索储能系统最佳的配置容量，即储能系统最大的“收益-投入比”下的配置容量为储能系统最佳的配置容量。
  2. 如权利要求1所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(1)中，所述配置储能系统的经济性收益f 1表示为：
    f 1＝B 1+B 2       (2)
    其中，B 1表示储能“削峰”的经济效益，B 2表示减少的发电机组装机容量收益；
    其中，储能“削峰”的经济效益B 1表示为：
    Figure PCTCN2022077253-appb-100002
    其中，P id表示储能系统在第i个小时的放电功率，P ie表示储能系统在第i个小时的充电功率，R i为第i个小时的实时电价；
    其中，减少的发电机组装机容量收益B 2表示为：
    B 2＝λk sP h      (4)
    其中，k s表示单位装机容量的价格，λ为资产折旧率，P h为负载达到最大值时储能系统的功率；
    其中，配置储能系统的可靠性收益f 2表示为：
    Figure PCTCN2022077253-appb-100003
    其中，M表示负载点数，K j表示负载点j的停电次数，P jk为第k次停电时负载点j的负荷值，T OFFjk为第k次停电时负载点j的停电时间，C Ljk为第k次停电时负载点j的平均停电损失；
    其中，储能系统的的建设成本C total表示为：
    C total＝C INESS+C RESS      (6)
    其中，C INESS表示储能系统的一次性建设成本，C RESS表示储能系统的总维护费用；
    其中，储能系统的一次性建设成本C INESS表示为：
    C INESS＝k eE N+(k p+k f)P N       (7)
    其中，k e为单位容量的储能系统的支出；k p为单位功率的变换器 的支出，k f为单位功率的配件支出，E N表示储能系统的容量，P N表示储能系统的功率；
    其中，储能系统的总维护费用C RESS表示为：
    C RESS＝k rP N       (8)
    其中，k r为单位功率的维修支出。
  3. 如权利要求1所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：采用蒙特卡洛法模拟得到系统的平均供电可用率指标与缺电期望值指标，并根据平均供电可用率指标与缺电期望值指标选择储能系统的功率区间。
  4. 如权利要求3所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：根据平均供电可用率指标与缺电期望值指标选择储能系统的功率区间，具体方法为：绘制平均供电可用率指标与缺电期望值指标随着功率变化的曲线；选择缺电期望值指标最小值对应的功率值与缺电期望值指标最大值对应的功率值相加的和除以2定为储能系统的基准功率P N1；选择平均供电可用率指标最小值对应的功率值与平均供电可用率指标最大值对应的功率值相加的和除以2定为储能系统的基准功率P N2；储能系统的功率区间表达式为(1±10％)(P N1+P N2)。
  5. 如权利要求1所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(3)中，使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”。
  6. 如权利要求5所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(3)中，使用蒙特卡洛法估计供电网络中配置不同容量的储能系统的“收益-投入比”，包括以下步骤：
    (3-1)设定蒙特卡洛最大模拟时长T，初始化模拟时间t＝0；
    (3-2)在系统中针对各个负荷点随机产生故障；
    (3-3)判断各个负荷点当前时间t下是否有故障；
    (3-4)若有故障，判断负荷点的供电是否可以恢复；
    (3-5)若可以恢复，判断储能系统是否正常；
    (3-6)若正常，判断储能系统的功率P BESS是否大于当前负荷的功率P load；
    (3-7)若大于，判断储能系统的剩余容量是否可以维持系统正常运转一小时；
    (3-8)若可以，停电区域恢复供电；
    (3-9)重复步骤(3-2)～(3-8)，直至模拟时间t达到最大模拟时长T；
    (3-10)计算当前容量下储能系统的“收益-投入比”。
  7. 如权利要求1所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(4)中，根据当前容量下储能系统的“收益-投入比”，利用粒子群算法搜索储能系统最佳的配置容量。
  8. 如权利要求7所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(4)中，利用粒子群算法搜索储能系统最 佳的配置容量，包括以下步骤：
    (4-1)初始化粒子群算法的配置参数；
    (4-2)定义适应度函数为步骤(1)中定义的储能系统的“收益-投入比”，粒子的个体适应度函数的最大值为每个粒子的“个体最优解”；
    (4-3)比较所有粒子的个体适应度函数的最大值，选出其中的最大值，定义为“全局最优解”；
    (4-4)将当前“全局最优解”与历史“全局最优解”进行比较，对自变量的粒子速度、位置进行更新；
    (4-5)判断是否满足终止迭代操作的条件，如果当前迭代次数达到设定的大迭代次数，终止迭代，选取所有“全局最优解”中最大值对应的配置容量为储能系统最佳的配置容量；否则，返回步骤(3)继续执行。
  9. 如权利要求8所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(4-1)中，初始化粒子群算法的配置参数具体为：设置最大迭代次数，自变量个数，最大粒子速度；粒子群算法的自变量为储能系统的容量；设置粒子群的初始速度和位置，设置粒子群大小为M。
  10. 如权利要求8所述的提高配电网可靠性的储能容量优化配置方法，其特征在于：步骤(4-4)中，更新速度和位置的公式表示
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