Simulating a Commercial Power Aggregator at Scale Design and Lessons Learned

被引:0
作者
Howorth, Gary [1 ]
Kockar, Ivana [2 ]
机构
[1] Univ Strathclyde, Inst Energy & Environm, 99 George St, Glasgow, Lanark, Scotland
[2] Univ Strathclyde, Inst Energy & Environm, 204 George St, Glasgow, Lanark, Scotland
来源
SIMULTECH: PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON SIMULATION AND MODELING METHODOLOGIES, TECHNOLOGIES AND APPLICATIONS, 2019 | 2019年
基金
英国工程与自然科学研究理事会;
关键词
Agent based Modelling; Aggregation; Power; Simulation; Smart Grid;
D O I
10.5220/0007854402770284
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
To evaluate aggregation models in the context of a power system, a software tool (the SmartNet simulator) has been developed to look at the impact of managing Distributed Energy Resources (DERs) on networks' technical operation (e.g. power flows and voltage levels) and simulates wholesale and ancillary services market conditions. This paper focusses on the design and implementation of one of the aggregation models that addresses the Curtailable Generator / Curtailable Load (CGCL) aggregator. The paper outlines the design of such a software aggregator agent and discusses the lessons learned in simulating a more realistic large power grid system. The aggregator is represented as an agent based object orientated model using a financed based buckets system to aggregate bids from up to 300,000 devices across 10 -20,000 power nodes. The concept/implementation can be extended to include more sophisticated bidding strategies and to use multiple perspectives on tranches. Simulation and testing of such a large simulation system was challenging, and we have proved that it is possible to simulate the aggregation and clustering of different types of flexibility into a number of manageable bids in a timely manner.
引用
收藏
页码:277 / 284
页数:8
相关论文
共 17 条
  • [1] Anikin D., 2016, WHAT IN MEMORY DATAB
  • [2] Couturier R, 2013, DESIGNING SCI APPL G, V13
  • [3] Django Software Foundation, 2017, DJANG DOC DJANG DOC
  • [4] Dzamarija M., 2018, AGGREGATION MODELS
  • [5] A stochastic optimisation framework for analysing economic returns and risk distribution in the LNG business
    Furlonge, Haydn I.
    [J]. INTERNATIONAL JOURNAL OF ENERGY SECTOR MANAGEMENT, 2011, 5 (04) : 471 - 493
  • [6] Gouy I., 2018, PYTHON 3 VS JAVA WHI
  • [7] Intel, 2018, INT DISTR PYTH ACC P INT DISTR PYTH ACC P
  • [8] Kerstiens C., 2018, DATABASE SHARDING EX
  • [9] A Closer Look at Demand Bids in California ISO Energy Market
    Kohansal, Mahdi
    Mohsenian-Rad, Hamed
    [J]. IEEE TRANSACTIONS ON POWER SYSTEMS, 2016, 31 (04) : 3330 - 3331
  • [10] The optimal structure of PD buckets
    Krink, Thierno
    Paterlini, Sandra
    Resti, Andrea
    [J]. JOURNAL OF BANKING & FINANCE, 2008, 32 (10) : 2275 - 2286