Demand response modeling: A comparison between tools
Demand response modeling: A comparison between tools
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DOI：
10.1016/j.apenergy.2015.02.057
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发表时间：
2015-05
期刊：
影响因子：
11.2
通讯作者：
Diana Neves;A. Pina;C. Silva
中科院分区：
文献类型：
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作者：
Diana Neves;A. Pina;C. Silva
重新安排能源系统中部分电力需求的潜力被视为提高系统效率的重要机会，特别是在远程和孤立的系统中。从供应的角度来看，这种灵活性可能会大大改善发电调度，特别是在存在可再生资源的情况下;从需求的角度来看，这种灵活性可以使客户从减少能源费用中受益。为了研究这些类型的影响，建模工具已经引入了可能性，包括灵活的负荷优化过程中，虽然有些使用非常简化的方法来做it. This研究比较不同的建模工具如何考虑固定和灵活的负荷在调度优化，分析他们不同的策略。以葡萄牙科尔沃岛为例，分别在HOMER、Energymonth和Matlab自建的经济调度模型中对3种不同的情景进行了仿真。比较结果表明，HOMER和EnergyEnergy仍然假设灵活负荷是第二优先负荷，在非高峰时段或可再生能源电力过剩的情况下满足，而没有直接考虑这种决定的经济影响。另一方面，在优化方法上更灵活的自建模型更接近实际操作，并且在使用需求响应策略时呈现最佳节省，尽管仅表示操作成本降低0.3%。我们的结论是，建模工具应发展和完善其优化策略，以捕捉使用需求响应，以提高能源系统的性能的总效益。
The potential to reschedule part of the electricity demand in energy systems is seen as a significant opportunity to improve the efficiency of the systems, especially on remote and isolated systems. From the supply point of view, that flexibility might bring significant improvements to the generation dispatch, especially when in the presence of renewable resources; from the demand point of view, that flexibility could allow customers to benefit from reducing their energy bills. To study these types of implications, modeling tools have been introducing the possibility to include flexible loads on the optimization process, although some use very simplified methodologies to do it. This study compares how different modeling tools consider fixed and flexible loads in the dispatch optimization, analyzing their different strategies. Three different scenarios were simulated in HOMER, EnergyPLAN and an economic dispatch self-built model in Matlab, using as case study the Corvo Island, Portugal. The comparison results indicate that HOMER and EnergyPLAN still assume that flexible loads are a second priority load that are met in off-peak hours or in the presence of excess electricity from renewable sources, not taking directly into account the economic impact of such decision. On the other hand, the self-built model that is more flexible on the optimization approach is the more close to the actual operation and presents the best savings when using demand response strategies, albeit representing only a 0.3% decrease in the operation costs. We conclude that the modeling tools should evolve and refine their optimization strategies to capture the total benefits of using demand response to improve energy systems performance.