分布式发电优化配置研究综述-solarbe文库

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s TÈªÄ ¥Âùî8 王守相1,王慧1,蔡声霞2 1.天津大学电力系统仿真控制教育部重点实验室,天津市300072; 2.南开大学周恩来政府管理学院,天津市300071 K1 Û“Æ È©î¹ -ùî £Ä,T¹ 1ÿ Fî¥s TÈ|È ï“dÏ¤ R0 12 112 2009, 3318 式中R为电能可靠性指数;R0为电能可靠性最小值。文献[31]提出了采用网络重构实现微网的经济运行的方法,其本质上是通过网络结构的改变,实现 DG相对位置的调整,改变微网内的功率流动,以达到优化的目的。微网优化运行方面的研究文献很多,超出本文讨论的范围,不多赘述。微网技术作为国际电力系统的一个前沿研究领域,以其灵活、环保、高可靠性的特点被欧盟和美国能源部门大力发展,今后必将在中国得到广泛应用。要做好微网优化方面的工作,必须重视利用神经网络、小波分析、灰色理论及专家系统预测技术建立精确的微网内部随机负荷模型,结合主电网调度计划, 继而从随机最优控制理论出发,结合智能控制人工神经网络、模糊控制等及现代控制理论,建立微网内部发电的随机最优控制模型并实现相应的优化控制。 4 ß Z¬ 单从经济性的角度而言, DG与传统能源发电很难直接竞争。例如原本能源利用效率高、经济效益好的微型燃气轮机发电由于受较高的天然气价格的影响致使投资回收周期明显延长;光伏发电和风力发电则存在初期建设投资成本较高的问题。但是 DG也具有环境友好,排放少甚至零排放等优点,如何将DG对环境保护和节能减排的贡献量化为可与经济性相比拟的指标,并引入对DG的优化规划,对 DG的推动和发展必然大有裨益。DG的经济性受国家能源政策的影响较大,因此,开展与DG相关能源政策的软课题研究,制定鼓励DG发展的政策和法规,是DG发展除技术推动力之外的另一不容忽视的重要推动力。 DG技术和微网技术是电力产业可持续发展的有效途径,符合当前集约型社会的能源利用方针。针对DG在电力系统中越来越广泛的应用,本文就其配置优化的研究现状和模型算法进行了概述和探讨,同时对基于DG的微网技术优化方面进行了简单综述。 DG接入电网的有效方式是组成微网接入。未来的配电系统将是多微网接入的集电能收集、电能传输、电能存储和电能分配为一体的新型电力交换系统。因此, DG优化配置问题会进一步发展为考虑微网构建和多微网相互作用的分布式能源综合优化规划问题。总体说来,目前国外的大量文献对DG的优点、经济性、新型DG以及DG对电力系统可靠性、稳定性、经济性、电能质量等方面的影响做了大量研究工作。这些工作所体现的研究方法和取得的成果给 DG在配电网中的布置、容量确定和选型问题提供了很好的借鉴和指导。而国内对于DG特别是微网技术的研究尚处于起步阶段,与国外所开展的全方位研究有着一定的差距。因此,开展相关研究,加快国内DG系统的发展,已经十分紧迫。国家重点基础研究发展计划 973计划/分布式发电供能系统相关基础研究0资助的子课题中就包含了对DG和微网优化配置问题的研究。 IÓD [ 1] J7 OS G, OOI B T, MCGILLIS D, et al. T he potential of distributed generation to provide ancillary services/ / IEEE Power Engineering Society Summer Meeting, July 16220, 2000, Seattle, WA, USA 176221767. [ 2] CHIRADEJA P, RAMAKUMAR R. An approach to quantify the technical benefits of distributed generation. IEE E Trans on Energy Conversion, 2004, 19 4 7642773. [ 3] M*ENDE Z QU EZADA V H , ABBAD J R, SAN ROM N T G. Assessment of energy distribution losses for increasing penetration of distributed generation. IEEE Trans on Power Systems, 2006, 21 2 5332540. [ 4] HEGAZY Y G, SALAMA M M A, CHIKH AN A Y. Adequacy assessment of distributed generation systems using Monte Carlo simulation. IEEE Trans on Power Systems, 2003, 18 1 48252. [ 5] HOFF T , SH UGAR D S. T he value of grid support photovoltaics in reducing distribution system losses. IEEE Trans on Energy Conversion, 1995, 10 3 5692576. [ 6]陈琳,钟金,倪以信,等.联网分布式发电系统规划运行研究.电力系统自动化, 2007, 31 9 26231. CHE N Lin, ZHONG Jin, NI Yixin, et al. A study on grid2 connected distributed generation system planning and its operation performance. Automation of Electric Power Systems, 2007, 31 9 26231. [ 7] RANJBAR N T, SHIRANI A M, OSTADIL A R. A new appr oach based on ant algorithm for Volt/ Var control in distribution network considering distributed generation. Iranian Journal of Science 2. Nankai University, T ianjin 300071, China Abstract With the smart grid becoming the focus of current study, dist ributed generat ions DGs as one of the main funct ions in smart grid are increasingly widely applied in power systems, and the optimal allocation of DGs has become particularly crucial. A review is made of the opt imal allocation of DG, with its current development at home and abroad summed up and analyzed. Some common optimization allocation models are given, especially those of comprehensive mult i2objective optimization based on single2objective opt imization. Opt imization allocation methods are boiled down to analytic methods, heurist ic methods, and probability optimization methods plus those for multi2object ive optimization. Finally, optimization allocation of DGs in the micro2grid is discussed, with the fut ure development of DG and micro2grid optimization projected. This work is supported by National Natural Science Foundation of China No. 50777047, 50837001 and Program for New Century Excellent Talents in University No. NCET20720602. Key words distribut ed generat ion; opt imal allocat ion; multi2objective optimization; micro2grid optimizat ion; smart grid ¤»46 continued from page 46 [ 20] CH EN H, WONG K P, NGU YEN D H M, et al. Analyzing oligopolistic electricity market using coevolutionary computation. IE EE T rans on Power Systems, 2006, 21 1 1432152. [ 21] CH EN H , WONG K P, CH UNG C Y, et al. A coevolutionary approach to analyzing supply function equilibrium model. IEEE T rans on Power Systems, 2006, 21 3 101921028. [ 22] AXELROD R, H AMIL TON W D. T he evolution of cooperation. Science, 1981, 211 139021396. [ 23] CH ONG S Y, YAO X. Multiple choices and r eputation in multiagent interactions. IEEE T rans on Evolutionary Computation, 2007, 11 6 6892711. [ 24] H ARRALD P G, FOGEL D B. E volving continuous behaviors in the iterated prisoner. s dilemma. Biosystems, 1996, 37 1 1352145. [ 25]陈皓勇,杨彦,张尧.电力市场智能模拟中代理决策模块的实现.电力系统自动化, 2008, 32 20 22226. CH EN H aoyong, YANG Yan, ZH ANG Yao. Realization of decision2making module in agent2based simulation of power mar kets. Automation of Electric Power Systems, 2008, 3220 22226. [ 26]程莹,刘明波.求解离散无功优化的非线性原对偶内点算法.电力系统自动化, 2001, 25 9 23227. CHE NG Ying, LIU Mingbo. Reactive2power optimization of large2scale power system with discrete control variables. Automation of Electric Power Systems, 2001, 25 9 23227. 1983 , 3,p Vùî 3,ö1ùîZ_È ï gÆ、È ï“d½ Ðç。E2mail yang. yanmail. scut. edu. cn ç/§ 1975 , 3,p V, q,ö1ùîZ_È ï g Æ、È ï“dªÄÃÐ、 ¦ýÆ È ï“d¥¨。 f 1948 , 3,YT,p V, q,p V 3 ,ö1ùîZ_È ï“d½ Ðç、È ï gÆ。 E2mail epyzhangscut. edu. cn A Coevolutionary Approach to Calculate Equilibrium for Oligopolistic Electricity Market YANGYan, CHEN Haoyong, ZHANGYao, WANG Yeping, JINGZhaoxia, TAN Ke South China University of Technology, Guangzhou 510640, China Abstract A linear supply function equilibrium LSFE model considering network constraint s and demand side bidding for oligopolist ic electricity market is presented. In this market model, the ISO solves an optimal power flow for dispat ching generation and determining nodal prices, and participant s will choose their bids to seek the maximum profits. Developed from agent2based simulation methods, the coevolutionary approach simulates the coevolutionary mechanism in nat ure and adopts the notions of ecosystem. It is employed to calculat e the Nash equilibrium point in this work. Numerical examples are used to validate the effectiveness of t he proposed method. Simulation results show that the robust and flexible of the coevolutionary approach. It can converge to pure st rategy equilibrium rapidly if it exists. Key words electricity market; Nash equilibrium; network constraints; linear supply function equilibrium; coevolutionary approach 115 8 王守相,等分布式发电优化配置研究综述

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