Coordinated optimal allocation of park microgrids based on linear programming
This study proposes a set of comprehensive solutions to the economic and operational efficiency problems faced by park microgrids in new energy transformation. First, we recognise that park microgrids, as a key area of electricity distribution reform, have an energy mix dominated by wind power generation, but the mismatch between supply and demand timings needs to be addressed urgently. To this end, we propose to optimise energy use by deploying an energy storage system. The solution is carried out in three steps: 1. Economy element model construction and sensitivity analysis: for individual park microgrids, we constructed an economy element model with power supply cost as the dependent variable, and conducted sensitivity testing through regression analysis. We selected key indicators such as load characteristics, constructed an integrated microgrid operation model including 50kW/100kWh energy storage devices, and conducted economic comparative analysis with the traditional model. 2. Joint Park Economic Optimisation: Extending to the joint park, we repeated the construction and analysis process of the above economic elements, and further explored the operation mode after configuring the same energy storage device in the joint park, and conducted an economic comparison with the traditional mode, in order to find out the effect of scale effect on the cost. 3. Multi-objective linear programming model solution: In Problem 3, we introduced a more complex multi-objective linear programming model, which considered the wind and solar power generation data and time-sharing tariff strategy for 12 months of the year. Solved optimally using MATLAB's fmincon function, the strategy allows energy storage to store energy in the tariff troughs and release it in the peaks for cost minimisation. We identify the joint park load and the average cost of supply per unit of electricity as the key elements that affect the economics. In terms of model optimisation, it is proposed to find a balance between complexity and prediction error by tuning parameters, cross-validation and applying other machine learning models. Through model testing, we obtain a goodness-of-fit R² of 0.955, which shows that the model has high accuracy and reliability. This provides strong decision support for the planning and operation of microgrids in the park. This solution integrates data analysis, mathematical modelling and computer simulation, aiming to provide scientific guidance for the economy and sustainability of park microgrids.
New Industry And Energy
“Smart networks” and distributed smart grids
three months
The implementation of this solution is suitable for countries and regions around the world, especially those that are committed to improving energy efficiency, optimizing the use of renewable energy, and seeking to maximize cost effectiveness in the electricity market.
1. Optimising the energy structure: through the combination of wind power generation and energy storage systems, the solution is able to increase the proportion of renewable energy in the energy structure and promote the use of clean energy. 2. Improvement of energy self-sufficiency: The construction of the park microgrid enhances the park's control over energy supply, reduces dependence on external power grids, and improves energy security and stability. 3. Cost-benefit analysis: Through the construction of economic element models and sensitivity analysis, the solution is able to identify configuration options that maximise cost-effectiveness and provide investors with reasonable return expectations. 4. Balance of supply and demand: The solution takes into account the timing mismatch between load characteristics and power generation, and realises the balance of energy supply and demand through intelligent scheduling of the energy storage system. 5. Multi-objective optimisation: Adopting a multi-objective linear planning model, the solution is able to consider multiple objectives at the same time, such as cost minimisation, energy efficiency maximisation, etc., to achieve the maximisation of comprehensive benefits. 6. Flexibility and adaptability: The solution can be customised according to the specific conditions and needs of different parks, providing excellent flexibility and adaptability. 7. Technology integration: integrating data analysis, mathematical modelling, computer simulation and other technical means, the solution provides a systematic approach to processing. 8. Economy and sustainability at the same time: the solution not only focuses on short-term economic benefits, but also considers long-term sustainability to ensure environmental protection and social responsibility in energy use. 9. Model validation: The validity and reliability of the solution is demonstrated by the high goodness-of-fit R² values obtained through model testing. By implementing this solution, it is expected to achieve efficient operation of the campus microgrid, reduce energy costs and improve energy use efficiency while promoting environmental sustainability.
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