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Benefits of Chronological Optimization in Long-Term Planning for Electricity Markets using PLEXOS® Simulation Software Charles I. Nweke, Frank Leanez,

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Presentation on theme: "Benefits of Chronological Optimization in Long-Term Planning for Electricity Markets using PLEXOS® Simulation Software Charles I. Nweke, Frank Leanez,"— Presentation transcript:

1 Benefits of Chronological Optimization in Long-Term Planning for Electricity Markets using PLEXOS® Simulation Software Charles I. Nweke, Frank Leanez, Glenn R. Drayton, and Mohan Kolhe 基于 PLEXOS® 仿真软件的电力市场长期规划优化

2 Energy Exemplar Pty Ltd 公司简介 2 About the company PLEXOS® software development is performed by Energy Exemplar in its Adelaide, Australia office. Energy Exemplar 公司位于澳大 利亚南澳首府阿德莱德。 Energy Exemplar provides local distribution, support, data and consulting services globally. PLEXOS® is also available for Research purposes. 同时为全球客户提供技术支持, 数据分析和咨询服务. PLEXOS® 软件也可以被用于科研用途。

3 Motivation: Renewable and non-conventional resources introduce uncertainty- >require back-up from traditional thermal generation. 作为传统能源的替代品,新能源和可再生能源的发展带来了机遇。 Along with high penetration levels, the following security-related questions arise: – What technology can be replaced? 什么样的能源形势可以被替代? – What technology has to remain? 什么样的能源形势可以继续保留? How simulation models can provide answers? 系统仿真模型如何来 解决这些问题? 3 Study Objectives 本文研究对象

4 Challenges for Planning Tools: 对于规划工具的挑战 Traditional approaches: 1.Heuristic methods/solutions 启发式算法 2.Decoupling investment and production models 解耦处理投资分析 和产能分析 3.Mathematical optimization: Use of Load-Duration-Curve (LDC) 数 学优化方法:负载持续曲线 Drawbacks: 1.Sub-optimal solutions 次佳解,非最优解 2.System-specific: Hard to modify and implement 系统参数很难调 整 3.LDC ‘breaks’ the chronology 负载持续曲线有可能打乱年代序列 4 Study Objectives 本文研究对象

5 Introduce Chronology: Mathematical optimization: – Co-optimize investment and production costs 投资和产能优化同时进行 Introduce thermal generation inter-temporal constraints: 引入下列约束条件 – Minimum stable levels 最小稳定值 – Effect of start-up/shutdown costs 开关机成本 – Min Up/Down times 最小开机关机时间 The challenge: Are we there yet? – Use a real-sized South Australian detailed system 本文中南澳电网模 型被用来做验证分析 5 Approach 实现方法

6 6 Slicing the horizon into periods (blocks) 整个仿真周期阶段化 Shape of the annual electricity consumption of SA 南澳洲年用电量图 LDC Approach: Capture peak (capacity adequacy) but everything has been ‘moved’ ( LDC 方法结果) Chronological Approach: Capture peak and keeps most relevant time-dependant variations (年代表法结果) Adjust best block fit possible using least-square 用最小二 乘法来分别确定最优的时 间跨度

7 LT Plan – Optimal investmentPASA – Optimal reserve shareMT – Resource Allocation ST – Chronological Unit Commitment New Builds/Retirements Maintenance Schedule Operating Policies Detailed Chronological Operation 7 Simulation Phases 仿真阶段

8 Chronological vs LDC 8 Results 结果分析

9 Chronological vs LDC 9 Results 结果分析 OCGT investments are promoted in chronological simulations over usage of old technology (Coal) LDC fail to capture Coal retirements-> Wind variability is mitigated by Coal Higher wind share (specially in late years) can lead to substantially higher pollution costs OCGT investments are promoted in chronological simulations over usage of old technology (Coal) LDC fail to capture Coal retirements-> Wind variability is mitigated by Coal Higher wind share (specially in late years) can lead to substantially higher pollution costs

10 Chronological vs LDC 方法比较 Results from chronologically modelling load in LT planning have shown promising signs that should bring about more apprehension towards traditional methods, and decision- support tools while showing less concern for speed of executions given the magnitude of possible future savings that could be gained for a region. In this era when ignoring environmental constraints is hardly an option in energy planning, decision-making can be better guided towards more appropriate choices in investment. Locations where wind and other intermittent sources are expected to make-up significant shares in the foreseeable future such as in China and parts of Europe are likely to benefit the most from LT modelling using chronological optimization. 10 Conclusions 结论

11 About PLEXOS ® PLEXOS® is a proven simulation tool that uses cutting- edge data handling, mathematical programming, and stochastic optimisation techniques to provide a robust analytical framework for power market analysis. PLEXOS® meets the demands of power market participants, regulators, and analysts alike with a comprehensive range of capabilities. Since its release in 2000, PLEXOS® has emerged as the worldwide simulation tool of choice and is now used in every region of the world, by many of the worlds largest utilities and system operators: PLEXOS® has featured in filings to the US FERC. PLEXOS® was selected by the Irish regulator as the endorsed market simulator for the Irish SEM. In 2010, PLEXOS® installations worldwide passed Energy Exemplar Pty Ltd 公司产品介绍

12 Analysis tools in PLEXOS® PLEXOS® 提供的分析工具 What can you do with PLEXOS®? LRMC Recovery Algorithm Nash-Cournot Competition Shadow Pricing (Bertrand game) Shadow Pricing (Bertrand game) Perfect Competition Uplift mechanisms Perfect Competition Uplift mechanisms Combined Heat and Power Fuel Markets External Energy, Ancillary Services and Capacity Portfolio Optimization N-x Contingency Analysis Security Constrained Unit Commitment Locational Marginal Price Decomposition Transmission Analysis Optimal Generation and Transmission Expansion Large Hydro Storage Decomposition Capacity Expansion Planning Resource allocation Price Forecast Load (wind) forecast Market Analysis 12

13 13 Where is it used? 客户分布 3/ Prospect Road Prospect, Adelaide, SA 5082 Australia Tel: Thanks for attending


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