# 计及新能源的微电网优化策略研究Research on New Energy Micro Grid Optimizing Strategy

• 全文下载: PDF(1182KB)    PP.119-129   DOI: 10.12677/SG.2019.93013
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This paper considers wind, solar and storage in micro grid system. Firstly, the framework of micro grid system is analyzed and the mathematical models of each module in the system are established. Then the flexible load characteristics of adjustable load and translatable load are studied and modeled respectively. Finally, the NSGA2 multi-objective optimization algorithm is adopted to study micro grid operation cost and the service life of energy storage system as the objective function. An example is given to verify the feasibility and validity of the algorithm. Compared with conventional operation schemes, the method proposed in this paper can effectively reduce the operation cost of micro grid by optimizing load dispatch, and improve the service life of energy storage system to ensure the safe and economic operation of micro grid.

1. 引言

2. 风、光、储微电网系统的框架与模型

Figure 1. Framework diagram of micro grid

2.1. 光伏发电出力模型

${P}_{PV}={Y}_{pv}×{f}_{pv}×\frac{\stackrel{¯}{{G}_{T}}\left[1+{\alpha }_{p}\left({T}_{c}-{T}_{c,STC}\right)\right]}{\stackrel{¯}{{G}_{T,STC}}}$ (1)

2.2. 风力发电出力模型

${p}^{w}=\left\{\begin{array}{l}0\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}v<{v}_{in}或v>{v}_{out}\\ {p}_{r}^{w}\frac{v-{v}_{in}}{{v}_{r}-{v}_{in}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{v}_{in}\le v<{v}_{r}\\ {p}_{r}^{w}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{v}_{r}\le v\le {v}_{out}\end{array}$ (2)

${P}_{W}\left\{{p}^{w}=0\right\}=1-\mathrm{exp}\left[-{\left(\frac{{v}_{in}}{c}\right)}^{k}\right]+\mathrm{exp}\left[-{\left(\frac{{v}_{out}}{c}\right)}^{k}\right]$ (3)

${P}_{W}\left\{{p}^{w}={p}_{r}^{w}\right\}=\mathrm{exp}\left[-{\left(\frac{{v}_{r}}{c}\right)}^{k}\right]-\mathrm{exp}\left[-{\left(\frac{{v}_{out}}{c}\right)}^{k}\right]$ (4)

${f}_{W}\left({p}^{w}\right)=\frac{kl{v}_{in}}{c}{\left(\frac{\left(1+\eta l\right){v}_{in}}{c}\right)}^{k-1}\mathrm{exp}\left[-{\left(\frac{\left(1+\eta l\right){v}_{in}}{c}\right)}^{k}\right]$ (5)

2.3. 风力发电出力模型

1) 蓄电池充电过程：

$E\left(t\right)-\left(1-\alpha \right)E\left(t-1\right)={P}_{c}\left(t\right){f}_{c}{T}_{c}$ (6)

2) 蓄电池放电过程

$E\left(t\right)+{P}_{f}\left(t\right){f}_{f}{T}_{f}=\left(1-\alpha \right)E\left(t-1\right)$ (7)

3) 储能电池的荷电状态(State Of Charge, SOC)：

$\text{SOC}=\frac{E\left(t\right)}{{E}_{\mathrm{max}}}$ (8)

4) 蓄电池组放电电量：

${E}_{c}=\underset{i=1}{\overset{T}{\sum }}{P}_{bi}\cdot \Delta {t}_{i}$ (9)

${{P}^{\prime }}_{bi}=\left\{\begin{array}{l}|{P}_{bi}|\text{}{P}_{bi}<0\\ \text{0}{P}_{bi}\ge \text{0}\end{array}$ (10)

3. 柔性负荷建模研究

3.1. 可调整负荷模型

${T}_{in}\left(t+1\right)={T}_{out}\left(t+1\right)-\left({T}_{out}\left(t+1\right)-{T}_{in}\left(t\right)\right){\text{e}}^{\frac{-\Delta t}{RC}}$ (11)

${T}_{in}\left(t+1\right)={T}_{out}\left(t+1\right)-1000\eta PR-\left({T}_{out}\left(t+1\right)-1000\eta PR-{T}_{in}\left(t\right)\right){\text{e}}^{\frac{-\Delta t}{RC}}$ (12)

${t}_{s}=RC\mathrm{ln}\frac{{T}_{\mathrm{min}}-{T}_{out}}{{T}_{\mathrm{max}}-{T}_{out}}$ (13)

${t}_{f}=RC\mathrm{ln}\frac{1000\eta R+{T}_{\mathrm{max}}-{T}_{out}}{1000\eta R+{T}_{\mathrm{min}}-{T}_{out}}$ (14)

${P}_{e}=\frac{{t}_{s}}{{t}_{s}+{t}_{f}}P$ (15)

3.2. 可平移负荷模型

${P}_{lk,i}=\left\{\begin{array}{l}{P}_{ek}\text{}{t}_{ak}\le i\le {t}_{ak}+{t}_{c}\\ 0\text{}其他\end{array}$ (16)

${P}_{l,i}=\underset{{k}_{1}=1}{\overset{{N}_{1}}{\sum }}{P}_{l{k}_{1},i}+\underset{{k}_{2}=1}{\overset{{N}_{2}}{\sum }}{P}_{l{k}_{2},i}$ (17)

4. 微电网多目标优化调度模型

4.1. 数据变量的采集

4.2. 微电网调度策略与假设

1) 对柔性负荷的调度策略与假设如下：假设本文研究的负荷(空调、电瓶充电装置、洗衣机)全部可调，根据预估的模拟数据，将空调设备设定的室内温度上下限，电瓶充电装置的工作起止时间、洗衣机的工作起止时间作为决策变量在满足约束条件的情况下进行调节，以满足目标函数的要求。

2) 微电网与交流配网的购电/售电关系如下：在风光出力不能够满足可平移负荷需求时，储能放电/微电网向交流配网购电，当风光出力过剩时，储能充电/微电网向交流配网售电。微电网与电网的购电/售电关系是通过多目标算法寻找最优化解集后自动形成的。

3) 本文认为微电网的运营商是风力发电系统、光伏发电系统、储能系统的投资主体，即风光模块发出的电能与储能模块存储的电能无需缴纳费用，但需要考虑设备运行成本，由于是日调度策略，因此本文不考虑投资成本。

4.3. 目标函数

4.3.1. 运行费用

$\begin{array}{l}\mathrm{min}{f}_{1}=\underset{i=1}{\overset{T}{\sum }}\left[{K}_{w\left(w\right)}{P}_{wi}+{K}_{w\left(pv\right)}{P}_{pvi}+{K}_{w\left(ba\right)}{P}_{bai}\right]\Delta {t}_{i}\\ \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}+\underset{i=1}{\overset{T}{\sum }}\left[{C}_{in,i}\cdot \left({P}_{in,i}\cdot \Delta {t}_{i}\right)-{C}_{out,i}\cdot \left({P}_{out,i}\cdot \Delta {t}_{i}\right)\right]\end{array}$ (18)

4.3.2. 蓄电池组使用寿命

$\mathrm{min}{E}_{c}=\underset{i=1}{\overset{T}{\sum }}{{P}^{\prime }}_{bi}\cdot \Delta {t}_{i}$ (19)

4.4. 约束条件

1) 各组件功率平衡约束

${P}_{pvi}{\eta }_{DC/AC}+{P}_{wi}{\eta }_{AC/AC}=\frac{{P}_{e,i}}{{\eta }_{AC/AC}}+\frac{{P}_{l,i}}{{\eta }_{AC/AC}}+\frac{{P}_{ba,i}}{{\eta }_{DC/AC}}+\frac{{P}_{out,i}}{{\eta }_{AC/AC}}$ (20)

${P}_{pvi}{\eta }_{DC/AC}+{P}_{wi}{\eta }_{AC/AC}+{P}_{ba,i}{\eta }_{DC/AC}+{P}_{in,i}{\eta }_{AC/AC}=\frac{{P}_{e,i}}{{\eta }_{AC/AC}}+\frac{{P}_{l,i}}{{\eta }_{AC/AC}}$ (21)

2) 空调温度范围约束

${T}_{in,\mathrm{min}}\le {T}_{in}\left(t\right)\le {T}_{in,\mathrm{max}}$ (22)

3) 可平移负荷约束

$\begin{array}{l}{t}_{m}\le {t}_{ak}\\ {t}_{ak}+{t}_{c}\le {t}_{n}\end{array}$ (23)

$19:00\le {t}_{ak}\le {t}_{ak}+{t}_{c}\le 次日10:00$ (24)

${t}_{ak}-4:00\le {t}_{ak}\le {t}_{ak}+{t}_{c}\le {t}_{ak}+4:00$ (25)

4) 储能约束

$|{P}_{bi}|\le {P}_{bn}{\eta }_{dd}$ (26)

$10%\le {\text{SOC}}_{i}\le 100%$ (27)

5) 并网约束

$\begin{array}{l}{P}_{in,i}\le {P}_{an}{\eta }_{ad}\\ {P}_{out,i}\le {P}_{an}/{\eta }_{ad}\end{array}$ (28)

5. 模型求解与算例分析

5.1. 系统流程

Figure 2. System flow chart

5.2. 算例分析

5.2.1. 参数设定

Table 1. Price of purchasing and selling electricity

Figure 3. Wind power and photovoltaic output curve

5.2.2. 优化结果分析

Figure 5. Pareto curve

Figure 6. Flexible load optimization and energy storage charge and discharge scheduling

5.2.3. 优化前后的对比分析

Table 2. Comparisons between optimal scheduling scheme and conventional scheduling scheme

6. 结论

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