Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems
Abstract
:1. Introduction
1.1. Literature Review
1.2. Motivation and Major Contributions of the Paper
- Since most of the BESS-controlling strategies are based on local information, they did not provide a system-level optimality guarantee. That is, the optimum amount of active power to be stored by each installed battery in the network is absent. This study tackles this point by providing a network-level optimization for BESS operation in grid-connected solar PV.
- Most of the studies use analytical approaches and local information to control the operation of the batteries. This research work, on the other hand, provides a charging/discharging strategy, taking into consideration all batteries in the distribution system to improve the voltage profile.
- In addition, different daily charging/discharging strategies have been proposed in the literature. Some of them proposed a load-following control method, while others utilized a droop-based method. Therefore, the proposed method examines a new controlling approach that shows its effectiveness.
- In addition, most papers have assumed predetermined sizes, numbers, and BESS locations in the distribution network. Their assumption may not be ideal since considering different sizes, numbers, or locations could result in better operation scenarios. Results indicate that the proposed voltage strategy is useful in the optimal planning of the BESS in the network.
2. Materials and Methods
2.1. Problem Description
2.2. Study Framework
- Step 1:
- The algorithm receives as input data the PV power output, load data of each house, and prespecified available BESS.
- Step 2:
- The NNA produces the initial population for the optimization problem. The explanation of NNA is presented in Section 3. NNA pattern solution populations are represented as a vector containing battery sizes in $\mathrm{kWh}$ $({M}_{\mathrm{max}})$ and $\mathrm{kW}$ $({S}_{\mathrm{max}})$ and location of each BESS, which are the variables to be optimized in the upper-level optimization problem. In other words, the vectors’ dimension is three times the number of BESS that are deployed in the network and ${S}_{\mathrm{max}}$ are real variables, while the location ($L$) is an integer variable as it represents the house number. If we have, for instance, three available batteries to be installed in the network, nine decision variables are optimized by NNA.$$v=\left[{S}_{\mathrm{max}}^{1}{S}_{\mathrm{max}}^{2}\dots {S}_{\mathrm{max}}^{N}{M}_{\mathrm{max}}^{1}{M}_{\mathrm{max}}^{2}\dots {M}_{\mathrm{max}}^{N}{L}^{1}{L}^{2}\dots {L}^{N}\right]$$
- Step 3:
- Using the LP, the BESS schedule for the following day is determined and solved in MATLAB (see Section 2.2). After that, the Open Distribution System Simulator (OpenDSS) software receives the ideal daily charge/discharge of BESS [31] and measures its effectiveness on the distribution grid. The role of OpenDSS is to model and simulate the distribution system. The power flow constraints are satisfied since the power flow is conducted in real-time simulation through the OpenDSS.
- Step 4:
- The evaluation is accomplished using the fitness function, Equation (2), which minimizes voltage deviation. Three-phase load flow in OpenDSS is used to obtain the voltage magnitudes. This technique is continued until the desired number of iterations has been reached.
2.3. Battery Modeling
- The PV system extracts the available power from the sun and produces solar energy.
- See if the produced energy can be utilized to supply the demand.
- Any excess energy is considered as the available energy to charge the BESS.
- If the BESS is completely charged, the surplus energy is exported to the main grid.
- The PV system generates little amounts of no solar energy.
- The BESS initially supplies the demand.
- If the load is completely supplied by BESS, the remaining energy in BESS is exported to the main grid.
- If the BESS is fully discharged, the load is supplied from the main grid.
3. Metaheuristic Optimization Algorithms
Neural Network Optimization Algorithm
- Step 1:
- Select the pattern solutions population $\left({N}^{Pop}\right)$ and set the lower $\left(low\right)$ and upper $\left(up\right)$ bounds and the maximum number of iterations of the algorithm $\left(ite{r}_{max}\right)$. In this study, the ${N}^{Pop}$ is set to 50. Since we have three variables to optimize, the $low$ and $up$ bounds are set to adhere to BESS rated capacity, rated power, and the number of buses in the examined test feeder. For $ite{r}_{max}$, 250 is used in this study.
- Step 2:
- Initiate the population of pattern solutions with the predetermined bounds using the following formula:$${X}_{mn}=low\left[n\right]+rand()\times \left(Upp\left[n\right]-low\left[n\right]\right)$$
- Step 3:
- Evaluate the fitness values of the initial population using Equation (2).
- Step 4:
- Randomly generate the weigh matrix, values of which are selected based on a uniform distribution between zero and one and the summation of these values should be below or equal to one. See Equations (14) and (15).$${{\displaystyle \sum}}_{j=1}^{{N}^{pop}}{w}_{ij}\left(t\right)=1,i=1,2,3,\dots .,{N}^{pop}$$$${w}_{ij}\in U\left(0,1\right),i,j=1,2,3,\dots .,{N}^{pop}$$
- Step 5:
- Determine the target solution $\left({X}^{Target}\right)$ and the associated target weight $\left({W}^{Target}\right)$. Since NNA has only one target response, the target solution and weight are selected from the population weight (weight matrix).
- Step 6:
- Create a new pattern solution $\left({X}^{new}\right)$ and update the pattern solutions using Equations (16) and (17).$$\overrightarrow{{X}_{j}^{New}}\left(t+1\right)={\displaystyle \sum}_{i=1}^{{N}^{pop}}{w}_{ij}\left(t\right)\times \overrightarrow{{X}_{i}}\left(t\right),j=1,2,3,\dots .,{N}^{pop}$$$$\overrightarrow{{X}_{i}}\left(t+1\right)=\overrightarrow{{X}_{i}}\left(t\right)+\overrightarrow{{X}_{i}^{New}}\left(t+1\right),i=1,2,3,\dots .,{N}^{pop}$$
- Step 7:
- Update the weight matrix $\left(W\right)$ using Equation (18), taking into consideration the constraints in Equations (14) and (15).$$\overrightarrow{{W}_{i}^{Updated}}\left(t+1\right)=\overrightarrow{{W}_{i}}\left(t\right)+2\times rand\times \left(\overrightarrow{{W}_{i}^{Target}}\left(t\right)-\overrightarrow{{W}_{i}}\left(t\right)\right),i=1,2,3,\dots .,{N}^{pop}$$
- Step 8:
- Evaluate the bias condition. If $rand\le \beta $ conducts that bias operator, see Equation (19) for both the new position of pattern solutions and the updated weight matrix.$$\beta \left(t+1\right)=\beta \left(t\right)\times 0.99,t=1,2,3,\dots .,ite{r}_{max}$$
- Step 9:
- Employ the transfer function operator $\left(TF\right)$ to update the new position of pattern solutions $\left({X}_{i}^{*}\right)$ if $rand>\beta $, using the following equation:$$\overrightarrow{{X}_{i}^{*}}\left(t+1\right)=TF\left(\overrightarrow{{X}_{i}}\left(t+1\right)\right)=\overrightarrow{{X}_{i}}\left(t+1\right)+2\times rand\times \left(\overrightarrow{{X}_{i}^{Target}}\left(t\right)-\overrightarrow{{X}_{i}}\left(t+1\right)\right),i=1,2,3,\dots .,{N}^{pop}$$
- Step 10:
- Find the fitness values of the updated pattern solutions using the objective function, Equation (2).
- Step 11:
- Update the target solution and its associated target weight.
- Step 12:
- Update the value of $\beta $ using the reduction formulation, Equation (19).
- Step 13:
- Check that the stop criteria are met; otherwise, the algorithm should go back to Step 6.
4. Results and Discussion
4.1. Test System
4.2. Study Dataset
4.3. Case Studies and Simulation Results
4.4. Voltage before and after PV Systems
4.5. Analysis of Optimal Sizing and Allocation
4.6. BESS Operation
4.7. Transformer Loading and System Losses
4.8. Network Voltage with BESS
5. Conclusions and Future Work
- The simulation results show that the overvoltage problems appear in the distribution network with high PV integration represented by rooftop PV units.
- Optimal sizing, placement, and operating through utilizing the proposed voltage mitigation method helped overcome the voltage problems.
- In terms of the locations of the BESS units, the end of the feeder homes are the ideal sites to install batteries as they encounter the worst voltage scenarios.
- In addition, results showed that the optimal siting and sizing of the available BESS are highly impacted by network topology, load profiles, and the amount of PV power that flows into the grid.
- The developed BESS management strategy successfully minimizes the transformer burden; thus, it never exceeds the distribution system’s capacity limit. Therefore, the optimal installation of batteries in distribution grids can assist in delaying any scheduled transformer upgrades.
- Embracing and optimal operation of BESS in the smart grids will permit ideal resource usage and increase the PV hosting capacity in the LV grids.
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Specification | Parameter | Value |
---|---|---|
AC voltage | $AC$ | 120/240 V |
Usable energy | ${M}_{max}$ | 13.5 kWh |
Maximum charging/discharging power | ${S}_{max}$ | 5 kW |
Minimum charging/discharging power | ${S}_{min}$ | 0 kW |
Maximum state of charge | $SO{C}_{max}$ | 100% |
Minimum state of charge | $SO{C}_{min}$ | 10% |
Round trip efficiency | $\eta $ | 90% |
Rank | ${\mathit{S}}_{\mathit{m}\mathit{a}\mathit{x}}\left(\mathit{k}\mathit{w}\right)$ | ${\mathit{M}}_{\mathit{m}\mathit{a}\mathit{x}}\left(\mathit{k}\mathit{W}\mathit{h}\right)$ | Location (Bus) |
---|---|---|---|
1 | 5 | 13.5 | 53 |
2 | 5 | 13.5 | 50 |
3 | 5 | 13.5 | 55 |
4 | 5 | 13.5 | 45 |
5 | 5 | 13.5 | 54 |
6 | 5 | 13.5 | 29 |
7 | 5 | 13.5 | 51 |
8 | 5 | 13.5 | 31 |
9 | 5 | 13.5 | 41 |
10 | 5 | 13.5 | 25 |
11 | 5 | 13.5 | 43 |
12 | 5 | 13.5 | 35 |
13 | 5 | 13.5 | 33 |
14 | 5 | 13.5 | 30 |
15 | 5 | 13.5 | 36 |
16 | 5 | 13.5 | 39 |
17 | 5 | 13.5 | 49 |
18 | 5 | 13.5 | 40 |
19 | 5 | 13.5 | 48 |
20 | 5 | 13.5 | 46 |
21 | 5 | 11.6 | 52 |
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Alrashidi, M. Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems. Appl. Sci. 2022, 12, 9083. https://doi.org/10.3390/app12189083
Alrashidi M. Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems. Applied Sciences. 2022; 12(18):9083. https://doi.org/10.3390/app12189083
Chicago/Turabian StyleAlrashidi, Musaed. 2022. "Community Battery Storage Systems Planning for Voltage Regulation in Low Voltage Distribution Systems" Applied Sciences 12, no. 18: 9083. https://doi.org/10.3390/app12189083