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Microgrid Optimization Scheduling Based on Improved Genetic Annealing Algorithm
DOI: 10.12677/AEPE.2020.81001, PDF, HTML, XML, 下载: 639  浏览: 1,840

Abstract: This paper studies the optimization of microgrid operation. In order to obtain the optimal operation strategy of the microgrid system and reduce the cost of the microgrid during operation, the general mathematical model of the microgrid operation is established, and the annealing function in the genetic annealing algorithm is improved, which makes the convergence speed of the genetic annealing algorithm faster. The improvement leads to the genetic annealing algorithm being more efficient. Then, the article uses Genetic Algorithm (GA), Simulated Annealing Algorithm (SA) and Improved Genetic Annealing Algorithm (GSAA) to optimize the microgrid operation model in grid-connected mode and the microgrid operation model in island mode. Finally, the two micro-grid optimization results and operation strategies of grid-connected mode and island mode are analyzed. The simulation results show that the improved algorithm has the characteristics of fast convergence and lower operating cost.

1. 引言

2. 问题描述

${P}_{k}\left[n\right]={f}_{k}\left({P}_{k}\left[n-1\right],{x}_{k}\left[n-1\right],n-1\right),\left(k=1,\cdots ,N\right)$ (1)

${S}_{k}:{a}_{k}\le {b}_{k}$ (2)

$\mathrm{min}F=\mathrm{min}\left({C}_{0}+\underset{m=1}{\overset{N}{\sum }}\underset{n=1}{\overset{Y}{\sum }}{C}_{1}\left[m,n\right]\right)$ (3)

${C}_{0}=\underset{m=1}{\overset{N}{\sum }}{C}_{m}^{0}$ (4)

${C}_{1}\left[m,n\right]={C}_{OM}\left[m,n\right]+{C}_{FUEL}\left[m,n\right]+{C}_{EM}\left[m,n\right]+{C}_{grid}\left[m,n\right]$ (5)

${C}_{OM}\left[m,n\right]=Q\left[m\right]{P}_{m}\left[n\right]$ (6)

${C}_{FUEL}\left[m,n\right]={\partial }_{m}\left[n\right]{F}_{m}\left[n\right]$ (7)

${C}_{EM}\left[m,n\right]=\underset{j=1}{\overset{Q}{\sum }}{\beta }_{j}\left[m,n\right]{C}_{j}^{EM}\left[m,n\right]$ (8)

${C}_{grid}\left[m,n\right]={\gamma }_{m}\left[n\right]{E}_{m}^{grid}\left[n\right]$ (9)

Figure 1. System structure

3. 改进的优化算法

GA和模拟SA都是经典的寻优算法，但是GA前期个体的差异较大，父代与子代的适应度成正比，使得整个解空间都充斥着前期优秀的个体以及他们的子代，从而导致算法停滞不前，过早收敛，最终陷入局部最优。SA由于Metropolis准则的存在，使其具有较强的局部搜索能力且能够跳出局部最优解。

${T}_{NEW}=KT$ (12)

${T}_{NEW}=\mathrm{exp}\left(\frac{T-{T}_{0}}{{T}_{0}}\right)T$ (13)

Figure 2. GSAA flowchart

4. 算例分析

4.1. 并网型微电网算例

${P}_{pv}+{P}_{wt}+{P}_{ba}+{P}_{G}={P}_{L}$ (14)

${P}_{G}{}_{\mathrm{min}}\le {P}_{G}\le {P}_{G}{}_{\mathrm{max}}$ (15)

4.1.1. 光伏发电系统

${P}_{pv}={P}_{str}\frac{{G}_{C}}{{G}_{STC}}\left[1+K\left({T}_{C}-{T}_{STC}\right)\right]$ (16)

${T}_{C}={T}_{1}+30×\frac{{G}_{C}}{1000}$ (17)

${P}_{pv}{}_{{}_{\mathrm{min}}}\le {P}_{pv}\le {P}_{pv}{}_{{}_{\mathrm{max}}}$ (18)

4.1.2. 风机系统

${P}_{WT}=\left\{\begin{array}{ll}0\hfill & \text{\hspace{0.17em}}0\le u\le {u}_{ci}\hfill \\ {P}_{WT,rate}\left(u-{u}_{ci}\right)/\left({u}_{r}-{u}_{ci}\right)\hfill & {u}_{ci}\le u\le {u}_{r}\hfill \\ {P}_{WT,rate}\hfill & {u}_{r}\le u\le {u}_{co}\hfill \\ 0\hfill & \text{\hspace{0.17em}}\text{\hspace{0.17em}}{u}_{co}\le u\hfill \end{array}$ (19)

${P}_{wt}{}_{{}_{\mathrm{min}}}\le {P}_{wt}\le {P}_{wt}{}_{{}_{\mathrm{max}}}$ (20)

4.1.3. 蓄电池

$\left\{\begin{array}{l}{E}_{k}\left[n\right]={E}_{k}\left[n-1\right]+{P}_{k}\left[n\right]\Delta t{\eta }_{c}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{P}_{k}\left[n\right]\ge 0\\ {E}_{k}\left[n\right]={E}_{k}\left[n-1\right]+\frac{{P}_{k}\left(t\right)\Delta t}{{\eta }_{d}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{P}_{k}\left[n\right]<0\end{array}$ (21)

$SOC=\frac{{E}_{k}\left[n\right]}{{E}_{k}\left[n-1\right]}$ (22)

${P}_{ba}{}_{{}_{\mathrm{min}}}\le {P}_{ba}\le {P}_{ba}{}_{{}_{\mathrm{max}}}$ (23)

$SO{C}_{\mathrm{min}}\le SOC\le SO{C}_{\mathrm{max}}$ (24)

4.2. 结果及分析

Table 1. Electricity price list

*时段规定：谷时段：0:00~8:00，22:00~0:00；平时段：8:00~9:00，12:00~19:00； 峰时段：9:00~12:00，20:00~22:00。

Table 2. Cost comparison chart

Figure 3. Iteration curve

Figure 4. The power of each system

4.3. 孤岛算例分析

${C}_{DE}=\partial +\beta {P}_{DE}+\gamma {P}_{DE}^{2}$ (25)

Table 3. Greenhouse gas emissions and penalty parameters

Table 4. Micro source parameters

Table 5. Cost comparison chart

Figure 5. The power of each system

Figure 6. Iteration curve

5. 结论

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