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A Metaheuristic-Based Micro-Grid Sizing Model with Integrated Arbitrage-Aware Multi-Day Battery Dispatching
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DOI：10.3390/su141912941.png)
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Rule-based micro-grid dispatch strategies have received significant attention over the last two decades. However, a recent body of literature has conclusively shown the benefits of operational scheduling optimisation while optimally sizing micro-grids. This is commonly referred to as micro-grid design and dispatch co-optimisation (MGDCO). However, as far as can be ascertained, all the existing MGDCO models in the literature consider a 24-h-resolved day-ahead timeframe for the associated optimal energy scheduling processes. That is, intelligent, look-ahead energy dispatch strategies over multi-day timeframes are generally absent from the wider relevant literature. In response, this paper introduces a novel MGDCO modelling framework that integrates an arbitrage-aware linear programming-based multi-day energy dispatch strategy into the standard metaheuristic-based micro-grid investment planning processes. Importantly, the model effectively extends the mainstream energy scheduling optimisation timeframe in the micro-grid investment planning problems by producing optimal dispatch solutions that are aware of scenarios over three days. Based on the numeric simulation results obtained from a test-case micro-grid, the effectiveness of the proposed optimisation-based dispatch strategy in the micro-grid sizing processes is verified, while retaining the computational tractability. Specifically, comparing the proposed investment planning framework, which uses the formulated 72-h dispatch strategies, with the business-as-usual MGDCO methods has demonstrated that it can reduce the micro-grid's whole-life cost by up to 8%. Much of the outperformance of the proposed method can be attributed to the effective use of the behind-the-meter Li-ion battery storage, which improves the overall system flexibility.
Keyword:
micro-grids
optimal sizing
optimal dispatching
energy management
metaheuristics
linear programming
期刊
IF:
3.3
论文数:
10.7W
被引数:
28.4W
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