配電系統(tǒng)中多目標條件下可控負荷的最優(yōu)控制
發(fā)布時間：2019-04-27 08:35
【摘要】：電力需求側(cè)管理（demand side management, DSM）是指通過采取有效的措施來引導、激勵或輔助電力用戶改變用電習慣，提高供用電效率，以降低負荷費用，平滑負荷曲線，減少網(wǎng)絡(luò)損耗、提高供電可靠性等為目的而采取的一項對環(huán)境、電力公司、電力用戶及社會都有巨大意義的工程。作為需求側(cè)管理最重要的方面，負荷管理從本質(zhì)上改變了以往純粹依靠增加發(fā)電側(cè)發(fā)電機組容量來應對負荷快速增長的局面，充分調(diào)動了電力用戶參與電網(wǎng)安全穩(wěn)定合理運行與發(fā)展的積極性，電力公司與用戶協(xié)作來提高電力系統(tǒng)運行穩(wěn)定性并減少雙方的供電、用電成本，最大化雙方利益。本文研究負荷管理的重要組成部分--可控負荷在配網(wǎng)中的多目標優(yōu)化控制策略，并將改進的多目標粒子群優(yōu)化算法(improved multi-objectiveparticle swarm optimization, IMOPSO)應用到了可控負荷模型的優(yōu)化中，相關(guān)仿真波形驗證了該可控負荷多目標控制策略在降低配網(wǎng)網(wǎng)損與用戶電費方面的有效性和可靠性。 論文針對現(xiàn)階段配網(wǎng)中存在的各種問題，如負載率過低，高峰時段峰值較大但持續(xù)時間一般較短，系統(tǒng)發(fā)電容量不足以滿足快速增長的負荷，可再生能源大規(guī)模接入帶來的波動性功率輸出及負荷隨機性等造成的頻率、電壓波動甚至崩潰等，提出了利用對配網(wǎng)中不同種類可控負荷進行控制的解決方案，并闡述了可控負荷在解決這些問題時的優(yōu)越性。 論文重點對空調(diào)、熱水器、冰箱、電動汽車等典型可控負荷的工作特性進行了研究，根據(jù)“黑盒子”理論只考慮其外部工作特性，建立了簡化的可控負荷數(shù)學模型。過程的簡化與參數(shù)的概率選擇提高了模型在實際求解中的簡易性及準確性，在此基礎(chǔ)上論文提出了基于低壓配網(wǎng)的可控負荷多目標優(yōu)化控制策略，該策略利用啟發(fā)式算法對不同節(jié)點、不同種類、不同時段的可控負荷工作狀態(tài)進行優(yōu)化，以期減小配網(wǎng)網(wǎng)損、降低峰谷差、減少用戶電費等，控制策略靈活且不需對現(xiàn)有配網(wǎng)進行大幅改造，便于實際應用推廣。 針對可控負荷多目標優(yōu)化問題的多維度、多約束性，論文采用多目標粒子群算法作為可控負荷控制策略的底層算法，并針對現(xiàn)有多目標粒子群算法存在的，如優(yōu)秀個體選取方式不明確、對約束處理不夠靈活等問題，提出了基于歸一化函數(shù)值位與約束懲罰位的粒子比較策略，該比較策略綜合考慮了粒子對應各目標函數(shù)的函數(shù)值與對各約束的違反程度，可以更好地反映粒子的適應度并引導粒子向最優(yōu)解前沿加速搜索，基于相關(guān)改進設(shè)計了新的多目標粒子群算法IMOPSO，該算法可以更好地求解可控負荷多目標優(yōu)化問題，給出更合理的可控負荷控制序列。 論文的最后利用Matlab軟件編制了相應的IMOPSO源程序，并分別以網(wǎng)損與電費最小為目標函數(shù)，仿真了論文提出的可控負荷多目標優(yōu)化控制策略在IEEE14節(jié)點中，不同天氣情況下的實際優(yōu)化效果，，仿真結(jié)果證明該控制策略在不同天氣情況下,均能在滿足用戶用電舒適度的前提下，大大降低網(wǎng)損與電費，具有很好的應用前景。根據(jù)蒙特卡洛法，得出了控制策略優(yōu)化結(jié)果對各電動汽車滲透率及可控負荷控制比的靈敏度，為進一步研究可控負荷應用提供了方向。
[Abstract]:The power demand side management (DSM) refers to the use of effective measures to guide, motivate or assist the power user to change the power utilization habit, improve the power supply efficiency, to reduce the load cost, to smooth the load curve, to reduce network loss, A project for the purpose of improving the reliability of power supply, etc., is of great significance to the environment, the power company, the power user and the society. As the most important aspect of the demand side management, the load management changes the situation that the load is rapidly growing by increasing the capacity of the power generation side generating set in essence, and fully realizes the initiative of the power user to participate in the safe and stable operation and development of the power grid, The utility and the user cooperate to improve the operation stability of the power system and reduce the power supply, the power utilization cost and the benefit of the two parties. In this paper, the important component of load management--the multi-objective optimization control strategy in the distribution network is studied, and the improved multi-objective particle swarm optimization (IMPSO) is applied to the optimization of the controllable load model. The related simulation waveforms verify the effectiveness and reliability of the controllable load multi-target control strategy in reducing the network loss and the user's electricity fee. In view of the problems existing in the distribution network at present, such as the low load rate, the high peak period, the duration is generally shorter, and the power generation capacity of the system is not enough to meet the negative of the rapid growth The frequency, voltage fluctuation and even breakdown caused by the large-scale access of the renewable energy source and the large-scale access of the renewable energy source and the like, and the solution of controlling the different kinds of controllable loads in the distribution network are put forward. The case, and expounds the superiority of the controllable load in the solution of these problems The paper focuses on the working characteristics of typical controllable loads such as air-conditioning, water heater, refrigerator and electric vehicle. According to the "black box" theory, only the external working characteristics are considered, and the simplified controllable load number is established. The simplified and parameter selection of the process improves the simplicity and accuracy of the model in the practical solution. On the basis of this, the paper presents a multi-objective optimization control strategy based on the control load of the low-voltage distribution network. The controllable load working state of the same kind and different time period is optimized, with the aim of reducing the network loss, reducing the peak-to-valley difference, reducing the user's electric charge and the like, In this paper, the multi-objective particle swarm optimization algorithm is used as the bottom-layer algorithm of the controllable load control strategy, and the multi-objective particle swarm algorithm is used as the bottom-layer algorithm of the controllable load control strategy, and the multi-objective particle swarm optimization algorithm is applied to the existing multi-objective particle swarm optimization algorithm, such as the excellent individual selection. In this paper, we put forward a particle comparison strategy based on the normalized function value bit and the constraint penalty bit, which takes into account the function value of the particle's corresponding target function and the constraint on each constraint. The invention can better reflect the fitness of the particles and guide the particles to accelerate the search to the optimal solution front, and design a new multi-target particle swarm optimization algorithm (IMEPSO) based on the related improvement, which can better solve the problem of multi-objective optimization of the controllable load and give a more reasonable and controllable negative effect. In the end of this paper, the corresponding IMOPSO source program is developed by using the Matlab software, and the net loss and the electric charge are the least objective function, and the control strategy of the multi-objective optimization of the controlled load is simulated in the IEEE14 node under different weather conditions. The simulation results show that the control strategy can greatly reduce the net loss and the electric charge under different weather conditions, and has the advantages of greatly reducing net loss and electric charge, According to the Monte-Carlo method, the sensitivity of the control strategy optimization result to the control ratio of the permeability and the controllable load of each electric vehicle is obtained, and the controllable load can be further studied.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM921.5
本文編號：2466833
[Abstract]:The power demand side management (DSM) refers to the use of effective measures to guide, motivate or assist the power user to change the power utilization habit, improve the power supply efficiency, to reduce the load cost, to smooth the load curve, to reduce network loss, A project for the purpose of improving the reliability of power supply, etc., is of great significance to the environment, the power company, the power user and the society. As the most important aspect of the demand side management, the load management changes the situation that the load is rapidly growing by increasing the capacity of the power generation side generating set in essence, and fully realizes the initiative of the power user to participate in the safe and stable operation and development of the power grid, The utility and the user cooperate to improve the operation stability of the power system and reduce the power supply, the power utilization cost and the benefit of the two parties. In this paper, the important component of load management--the multi-objective optimization control strategy in the distribution network is studied, and the improved multi-objective particle swarm optimization (IMPSO) is applied to the optimization of the controllable load model. The related simulation waveforms verify the effectiveness and reliability of the controllable load multi-target control strategy in reducing the network loss and the user's electricity fee. In view of the problems existing in the distribution network at present, such as the low load rate, the high peak period, the duration is generally shorter, and the power generation capacity of the system is not enough to meet the negative of the rapid growth The frequency, voltage fluctuation and even breakdown caused by the large-scale access of the renewable energy source and the large-scale access of the renewable energy source and the like, and the solution of controlling the different kinds of controllable loads in the distribution network are put forward. The case, and expounds the superiority of the controllable load in the solution of these problems The paper focuses on the working characteristics of typical controllable loads such as air-conditioning, water heater, refrigerator and electric vehicle. According to the "black box" theory, only the external working characteristics are considered, and the simplified controllable load number is established. The simplified and parameter selection of the process improves the simplicity and accuracy of the model in the practical solution. On the basis of this, the paper presents a multi-objective optimization control strategy based on the control load of the low-voltage distribution network. The controllable load working state of the same kind and different time period is optimized, with the aim of reducing the network loss, reducing the peak-to-valley difference, reducing the user's electric charge and the like, In this paper, the multi-objective particle swarm optimization algorithm is used as the bottom-layer algorithm of the controllable load control strategy, and the multi-objective particle swarm algorithm is used as the bottom-layer algorithm of the controllable load control strategy, and the multi-objective particle swarm optimization algorithm is applied to the existing multi-objective particle swarm optimization algorithm, such as the excellent individual selection. In this paper, we put forward a particle comparison strategy based on the normalized function value bit and the constraint penalty bit, which takes into account the function value of the particle's corresponding target function and the constraint on each constraint. The invention can better reflect the fitness of the particles and guide the particles to accelerate the search to the optimal solution front, and design a new multi-target particle swarm optimization algorithm (IMEPSO) based on the related improvement, which can better solve the problem of multi-objective optimization of the controllable load and give a more reasonable and controllable negative effect. In the end of this paper, the corresponding IMOPSO source program is developed by using the Matlab software, and the net loss and the electric charge are the least objective function, and the control strategy of the multi-objective optimization of the controlled load is simulated in the IEEE14 node under different weather conditions. The simulation results show that the control strategy can greatly reduce the net loss and the electric charge under different weather conditions, and has the advantages of greatly reducing net loss and electric charge, According to the Monte-Carlo method, the sensitivity of the control strategy optimization result to the control ratio of the permeability and the controllable load of each electric vehicle is obtained, and the controllable load can be further studied.
【學位授予單位】：湖南大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM921.5
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