# Improvement of the Grid-Tied Solar-Wind System with a Storage Battery for the Self-Consumption of a Local Object

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Literature Review and Problem Statement

_{W}= 12 kW and PV power P

_{PV}= 39.2 kW, with the maximum load power during the daytime P

_{L}= 12 kW, in [5]. In article [6], the researchers then presented dependencies P

_{PV}(t) and P

_{W}(t) that opened up the possibility of equalizing the total energy of RES generation during the day. Most works considered the use of electricity generation to power the DG. This helps to reduce the costs of energy consumed from the grid and makes it easier to solve the issue of how to balance powers in the system.

- to calculate the average monthly values of the energy generated by PV and WG at time intervals of the day, using archived data for the LO location point;
- to develop a technique for calculating the installed power of the equipment for the LO load schedule in question with a given reduction in electricity consumption from the grid;
- to formulate principles for the implementation of a schedule for the SB charge degree, accounting for the forecasted RES generation with one or two electricity payment tariffs;
- to perform simulation modeling of energy processes in the system for the daily cycle of work, with an assessment of the degree of reduction in costs for electricity consumed from the grid.

## 3. Materials and Methods

## 4. Research Results on Improving the Grid-Tied PV-WG System with a Battery

#### 4.1. Structure of PV-WG System

_{d}of the DC bus and formed the reference for the corresponding current [7]: VC

_{IPV}—PV current I

_{PV}, VC

_{IB}—SB current I

_{B}, VC

_{Ig}—current I

_{g}in PCC. Depending on the mode, the value of one of the currents was adjusted [7], while the rest were fixed.

#### 4.2. Analysis of RES Generation Based on Archival Data: The Accepted Load Schedule

_{PVR}= 1 kW and peak load P

_{L}= 200 W. The average values for the maximum load power P

_{L}at different times (Kyiv time) are presented in Table 1.

_{PVAVD}—per day, W

_{PV23}, W

_{PV34}, W

_{PV45}) and WG (W

_{WAVD}—per day, W

_{W56}, W

_{W62}, W

_{W24}) are given. Data from the archive [22] for Kyiv (latitude (decimal degrees): 50.27, longitude (decimal degrees): 30.31) were used for a PV power of 1 kW over five years and WG (1.8 kW, 3 m/s) over six years. For comparison, Table 2 also shows the PV generation values W

_{PVAVDK}for Košice (latitude (decimal degrees): 48.717, longitude (decimal degrees): 21.2530), which is close to Kyiv, with a slight increase in PV generation in winter relative to Kyiv.

#### 4.3. Implementation Variant for One-Tariff Plan

_{R}, where Q = ∫(I

_{B}·dt) and Q

_{R}is the rated capacity (in Ah). The process of accumulation of surplus energy of RES in the daytime period with transfer to the load in the evening is taken into account in reducing the consumption from the DG. Then, the SB energy balance cycle per day assumes a night charge (interval (t

_{6}, t

_{2})) from Q*

_{6}to Q*

_{2}(where: ΔQ*

_{62}= Q*

_{6}− Q*

_{2}) followed by a day’s discharge on a load from Q*

_{2}to Q*

_{6}. The energy balance for the night period is as follows

_{g62}—energy consumed from the grid; W

_{W62}—energy generated by WG; W

_{L62}—energy consumed by the load; ΔW

_{B62}= (0.01·W

_{B}·(Q*

_{2}− Q*

_{6}))/η

_{C}·η

_{B}—energy per SB charge; W

_{B}= U

_{B}·C

_{B}—SB energy capacity; U

_{B}—SB voltage; C

_{B}—SB capacity (Ah); η

_{C}—converter efficiency; η

_{B}—SB efficiency.

_{B26}= 0.01·W

_{B}·(Q*

_{6}− Q*

_{2})/η

_{C}·η

_{B}.

_{L}= (ΔW

_{B26}+ W

_{PV26}·η

_{C}+ W

_{W26}·η

_{C}+ W

_{g26}) + (W

_{g62}+ W

_{W62}·η

_{C}+ W

_{PV62}·η

_{C}+ ΔW

_{B62}).

_{g}= W

_{L}+ W

_{R62}·η

_{C}+ ΔW

_{B26}− ΔW

_{B62}, where W

_{R}= W

_{PV}+ W

_{W}is the total energy generated by RES.

_{L}= 3080 Wh; in summer—W

_{L}= 3140 Wh.

_{E}

_{P}—coefficient of PV power recalculation relative to the installed PV power of 1 kW; m—coefficient of WG power recalculation for the value used in Table 1 (1.8 kW).

_{6}, t

_{2})) W

_{W62}·η

_{C}/m + m

_{P}·W

_{PV62}·η

_{C}+ W

_{g62}= W

_{L62}+ W

_{B62}.

_{WMAX}= 1.1·P

_{WR}, and W

_{W62MAX}= P

_{WMAX}·(t

_{2}− t

_{6}). In this case, the value of W

_{W62MAX}exceeds the load consumption and there is no consumption from the grid, i.e., W

_{g62}= 0. We neglect the small PV generation in the morning hours, and then we get the condition

_{W62AV}are significantly lower than W

_{W62MAX}. In the period of spring-summer-autumn, as the PV generation in the daytime is sufficiently high, it is possible to avoid a night charge on the SB. Then, we can assume that the WG energy compensates for the consumption of the LO load, and m = W

_{W62}·η

_{C}/W

_{L62}. The maximum W

_{W62AV}during this period is in June (Table 2), when the value corresponds to m = 25.

_{B}by excluding electricity consumption from the grid during the evening peak (t

_{5}, t

_{6}), which is possible due to the SB charge at a high generation of RES during the day

_{W56}= 5245 Wh, with a duration of 4 h, is in September. Accepting a value of DOD ≤ 80%, the maximum value is ΔQ*

_{56}= 80%. In accordance with (7), we get W

_{B}= 835 Wh. We accept with some margin the value W

_{B}= 896 Wh, which corresponds, for example, to C

_{B}= 35 Ah at U

_{B}= 25.6 V.

_{E}should not be lower than in winter with an average monthly generation. For December, the monthly averages are W

_{PV}= 1000 Wh and W

_{W}/m = 1517 Wh. We will determine the value ΔQ*

_{26}in (4) based on the possible value at P

_{WMAX}and the power load at night.

_{PVAVD}= 3800 Wh (according to Table 2, for April and September, the W

_{PVAVD}values are 3830 and 3850 Wh). The calculated m

_{P}and k

_{E}values are given in Table 3. We get identical values (k

_{E}= 2.63) during the year when m

_{P}= 0.555. For winter, an increase in m

_{P}does not produce a significant increase in k

_{E}. It can be accepted that m

_{P}= 0.6, which allows us to get k

_{E}= 2.73 in December, and k

_{E}= 3 in the period of spring-autumn (with W

_{PV}= 3800 Wh and no wind).

_{L}= 200 W, we get an installed PV power P

_{PVR}= 600 W (1.67 times lower than accepted in [17] with a value of 1 kW) and WG power P

_{WR}= 72 W. In a real installation, we have, for example, at P

_{L}= 5 kW—P

_{PVR}= 15 kW, P

_{WR}= 1.8 kW and W

_{B}= 22.4 kWh.

_{R}

_{23}·η

_{C}> W

_{L}

_{23}. Discharge of SB to compensate for load consumption is possible only after t

_{4}, when PV generation decreases. Charging of SB on the interval (t

_{6}, t

_{2}) is carried out provided that P

_{R}·η

_{C}> P

_{L}(P

_{g}= 0) is maintained and, in general, Q*

_{2}≥ Q*

_{6}. At P

_{R}·η

_{C}≤ P

_{L}value I

_{B}= 0, there is a consumption of the energy missing for the LO load from the grid. Value Q*

_{6}sets DOD ≤ 80% (Q*

_{6}≥ 20%). We set increment Q* on the interval (t

_{2}, t

_{4}) according to the RES generation forecast data

_{62}is determined. Only ΔQ* > 0 is accounted for. The calculation does not take into account that at certain intervals of time, P

_{R}·η

_{C}< P

_{L}and SB charging is not carried out; besides that, at other intervals, the charge will be higher than expected. The calculation is made according to the forecast for the next day (+1) at time t

_{4}. This makes it possible to reconcile the value Q*

_{6}= Q*

_{2R(+1)}− ΔQ*

_{62(+1)}with the value Q*

_{2R(+1)}= (Q*

_{4}− ΔQ*

_{24(+1)}) ≥ 20% (Q*

_{4}= 95%). This ensures a low degree of battery discharge (DOD

_{6}) with low RES generation, which contributes to an increase in the battery life.

_{4}) is done to identify the required value Q*

_{6}, if the current value Q*

_{t}> Q*

_{6}. This provides for the possibility of full or partial compensation for energy consumed by the load due to SB discharge. In accordance with the generation forecast, current (measured) values P

_{PVt}, P

_{Wt}and Q*

_{t}and the load schedule, the degree of discharge was calculated from the current point in time t to t

_{6}

_{t,6}≤ (Q*

_{t}− Q*

_{6}), which corresponds to a moment in time t

_{R}, full compensation for the LO consumption is carried out at reference i

_{g}= 0. In a condition where Q* ≤ Q*

_{6}discharge stops, if t

_{R}≥ t

_{5}and the discharge has not started, partial compensation is carried out by reference to the discharge current of SB I

^{1}

_{Bt}= (0.01·C

_{B}·(Q*

_{t}− Q*

_{6}))/(t

_{6}− t) until Q* ≤ Q*

_{6}is reached.

#### 4.4. Formation of Storage Battery SOC(t) Dependency in Two-Tariff Plan Case and Use of Night Battery Charge from the Grid

_{2R}value according to the forecast for the next day; ensuring that the SB charge runs to Q*

_{2R}; formation of a graph of the SB discharge in the evening. For the interval (t

_{3}, t

_{4}), SB discharge is excluded.

_{2R}value is calculated from the condition Q*

_{4}≥ 95% when the degree of SB discharge is limited at the end of the morning peak Q*

_{3MIN}, for example, Q*

_{3MIN}≥ (40 ÷ 50)% at Q*

_{6}≥ 20%. The purpose of the restriction is to reduce the number of deep discharge cycles of SB. Here, ΔQ*

_{24}is determined by (8).

_{24}≤ 0, there is not enough energy from RES; on the interval (t

_{3}, t

_{4}), consumption from the grid takes place and the Q*

_{2R}value is accepted as close to 100%. At P

_{R}·η

_{C}< P

_{L}SB, the charge current is set to I

^{1}

_{B}= 0.1·C

_{B}.

_{24}> 0, there is no consumption from the grid. If ΔQ*

_{23}< 0 (defined similarly to ΔQ*

_{24}), the SB is discharged; then, the Q*

_{2R}value should correspond to a condition where Q*

_{2R}≥ (Q*

_{4}− ΔQ*

_{24}) and Q*

_{2R}≥ (Q*

_{3MIN}+ |ΔQ*

_{23}|). If ΔQ*

_{23}≥ 0 (SB is charged), it should correspond to a condition where Q*

_{2R}≥ (Q*

_{4}− ΔQ*

_{24}) and Q*

_{2R}≥ 20%.

_{WMAX}. At night, electricity from the grid will be consumed. However, the situation should be excluded when P

_{R}·η

_{C}> P

_{L}and the SB is charged and cannot accept excess energy. According to the charging characteristic for the lithium-ion battery [29], this is the case when Q* ≥ Q*

_{d}= (90–92)%. Implementation of the charging process is made possible by controlling the SB charge current using the appropriate controller [7]:

- If Q*
_{2R}≤ Q*_{d}, the charge current does not depend on the state of charge. We refer the current at forecast intervals, for example, Δt = 1 h − I^{1}_{B}= (0.01·C_{B}·(Q*_{2R}− Q*_{t}))/(t − t_{2}). At the initial point in time t = t_{6}, the current value t, Q*_{t}and reference of the current I^{1}_{B}are then used; - When the RES generation is high and a night charge is not required (Q*
_{2R}≈ Q*_{6}) or the charge is negligible, and value [(W_{W62}+ W_{PV62})·η_{C}− W_{L62}] > 0, that value of the SB charge current is

- If Q*
_{2R}> Q*_{d}, the battery is charged in two stages: up to 90%, followed by charging at a current value according to the charging characteristic up to Q*_{2}≤ 95%, when I_{B}≥ (0.09–0.1)·C_{B}. In the case of maximum generation of WG (wind gust) and reduction of the load to 0, the value that can be obtained is I^{1}_{B}= P_{WMAX}·η_{C}/U_{B}≤ 0.083C_{B}.

_{4}) is similar to the option discussed above for one tariff with full compensation for consumption.

#### 4.5. System Operation Modes

_{6}, t

_{2}), at P

_{R}·η

_{C}≤ P

_{L}, VC

_{Ig}is active, which sets the value of the amplitude of the current in PCC I

^{1}

_{gm}≥ 0 and ensures the balance of power in the system. At the same time, there is consumption of missing energy from the grid P

_{g}= (P

_{R}·η

_{C}− P

_{L}) ≤ 0 for LO load, and the I

_{B}value is 0. Charging SB is possible only in the case of P

_{R}·η

_{C}≥ P

_{L}, then it is set to I

^{1}

_{gm}= 0 and VC

_{B}is active, which sets the current I

_{B}. Switching controllers is carried out as follows. With an increasing P

_{R}value, I

^{1}

_{gm}decreases to 0, which further leads to an increase in voltage U

_{d}, and when it reaches the threshold value, VC

_{IB}is turned on. Reverse switching at a decreasing P

_{R}is carried out according to the condition I

_{B}= 0 and with a reduction in U

_{d}below the threshold value. The presence of two conditions excludes the influence of transition processes. The modes of operation and the main expressions for implementation in the two-tariff condition are given in Table 5.

## 5. Modeling the Energy Processes in the Daily Cycle

_{L}(t), PV generation P

_{PVM}(t) (P

_{PVM}corresponds to the maximum power mode) and P

_{W}(t) were set in tabular form. To set the intervals of work (Table 4 and Table 5), variables were entered—t

_{62}, t

_{61}, t

_{12}, t

_{23}, t

_{35}, t

_{56}, t

_{71}—that took a value of 1 at the appropriate time interval. Variables f

_{R}and h

_{R}set the SB discharge time in the evening, with full f

_{R}and partial h

_{R}compensation for consumption from the grid, respectively. They were determined according to precalculated values W

_{R26}and W

_{L26}in the process of modeling, using the function RS-trigger and intermediate variable t

_{R}

_{PVF}(t), taking into account the regulation, was

_{B}(Q*)

_{C}, U

_{B}(Q*)

_{C}at I

_{B}≥ 0 and discharge U

_{B}(Q*)

_{R}at I

_{B}< 0. In this case, the charge current was

_{0}was the initial value, I

^{1}

_{B}= I

_{BηB}, if I

_{B}> 0 (SB charge) and I

^{1}

_{B}= I

_{B}/η

_{B}, if I

_{B}< 0 (SB discharge).

_{d}= 1 and night T

_{n}= 0.5. The coefficient of cost reduction used [16,17] k

_{E}= W*

_{LS}/W*

_{gS}, where W*

_{LS}was the relative cost of the total electricity consumed at a load of LO per day, and W*

_{gS}was the relative cost of electricity consumed by LO from the grid

_{P}= 0.6 and m

_{P}= 0.67.

_{L}, P

_{PV}, P

_{W}, P

_{g}, Q*, I

_{B}—for a December day (6 December 2014) with PV generation corresponding to the monthly average are presented in Figure 2 for the option of using the night charge of the battery from the grid. In this case, the SB charge with the consumption of the missing energy from the grid is carried out up to Q*

_{1}= 90% with a serving tool to Q*

_{2}= 93% due to excess WG energy. Since the WG generation in the evening is quite considerable, the SB discharge to compensate for energy consumption from the grid begins before the evening peak at 16.00. The values are as follows: Q*

_{6}= 23%, k

_{E}= 2.745 and at the night rate k

_{En}= 3.43.

_{E1}= 9.275. When using a night charge, three tariff options were considered: one rate k

_{E1}= 9.275; daily and peak tariffs T

_{P}= 1.5, k

_{EP}= 9.125; day and night tariffs T

_{n}= 0.5, k

_{En}= 11. The application of the peak tariff was not effective. Therefore, the use of the night tariff was considered.

_{PV}= 1320 Wh (27 July 2015) without a night SB charge from the grid are presented. The values are as follows: Q*

_{00}= 60%, Q*

_{MAX}= 94.32% and k

_{E1}= 2.796. In Figure 4b, oscillograms are then shown for a July day (4 July 2013) with PV generation corresponding to the monthly average value. In this case, the implementation with one or two tariffs is the same. A night SB charge from the grid is not used Q*

_{2}= Q*

_{6}= 20% (k

_{E}= 10.52, k

_{En}= 12.56); instead, SB discharge is used to compensate for energy consumption by the grid before the evening peak at 19.00. There is some underutilization of PV energy with a decrease in generation relative to the maximum power mode P

_{PVM}.

_{P}= 0.6 and m

_{P}= 0.67 (ΔQ*

_{MAX}= Q*

_{MAX}, Q*

_{0}(Q*

_{0}—value at the time 00.00) are considered.

_{PVM}+ P

_{W})

_{f}and the actual generation values (P

_{PVM}+ P

_{W}) is considered. The problem is when there is a value of Q*

_{2R}≈ 20% and (P

_{PVM}+ P

_{W})

_{f}< (P

_{PVM}+ P

_{W}). In this case, the calculated value of Q*

_{2R}will be overestimated. This will lead to a decrease in the use of RES energy for consumption. So, for a March day at (P

_{PVM}+ P

_{W})

_{f}= 0.9(P

_{PVM}+ P

_{W}), there is a value where Q*

_{2R}= 35% (Figure 5a). At the same time, there is the value where k

_{E}= 10.24.

_{E}= 11.68. That is to say, it is possible to use a discharge during the hours of the morning peak according to the value of the actual generation of RES. With an overestimated forecast where Q*

_{2R}= 20%, the system works via the actual generation of RES.

## 6. Discussion

- use of WG at the average wind speed for the location of the LO as an auxiliary source of energy of low power. At the same time, WG increases the value of the total generation of RES in winter to a value that provides the desired reduction in consumption from the grid. This value is close to spring and autumn periods in the absence of wind;
- calculation of the installed power of the equipment for the accepted LO load schedule, with the desired reduction in electricity consumption from the grid, based on the monthly average values of the energy generated by PV and WG. The values are determined by time intervals per day using archival data for the LO location point;
- formation of a state of charge SOC(t) according to the short-term forecast of RES energy generation, taking into account the tariffication of payment for electricity and with a focus on minimizing the nighttime charge, as well as using only one deep discharge cycle per day.

- the object is accepted with the main load in the daytime in the presence of peak loads in the morning and evening hours when it is possible to charge the battery at night within the limit on consumption from the grid. The average value of the load power at intervals is close to the accepted schedule;
- the assessment of a possible reduction in the cost of paying for electricity during the year was somewhat simplified. During the simulation, the days were considered when the PV generation corresponded to the monthly average values for the set time intervals, paired with the actual WG generation for the selected days;
- implementation of the control system implies an “open” structure of the relevant regulatory channels;
- the simulation assumed that the graph of the power generated by PV corresponded to the forecast and did not change during the day.

- automation of the calculation of data for the average monthly generation of renewable energy sources;
- a study of the possibilities for correcting deviations in the values of the actual generation of renewable energy sources relative to the forecasted data, and possible changes to the current forecast during the day;
- improvement of the principles for the implementation of the control system.

## 7. Conclusions

_{E}. Boundary conditions of the absence of PV or WG generation were taken into account. This allowed us to achieve a reduction in the installed power of RES. For accepted values, the ratio P

_{PVR}:P

_{WR}:P

_{L}= 3:0.36:1 was possible. For comparison, the ratio in [5] was P

_{PVR}:P

_{WR}:P

_{L}= 3.27:1:1.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Nomenclature

PV | Photovoltaic battery |

WG | Wind generator |

RES | Renewable energy sources |

SB | Storage battery |

LO | Local object |

DG | Distribution grid |

PCC | Point of common coupling |

MPPT | Maximum power point tracking |

P_{PV} | PV power generation (W) |

P_{W} | Power generation of the wind generator (W) |

P_{L} | Load power (W) |

P_{g} | Power consumption from the grid (W) |

P_{PVR} | Rate (installed) power of photovoltaic battery (W) |

P_{WR} | Rate (installed) power of wind generator (W) |

U_{d} | DC link voltage (V) |

U_{B} | Battery voltage (V) |

U_{g} | Grid voltage (V) |

I_{B} | Current of battery (A) |

I_{g} | Current at point of common coupling with the grid (A) |

I_{PV} | Current of photovoltaic battery (A) |

W_{PV} | Energy generation of photovoltaic battery (Wh) |

W_{W} | Energy generation of wind generator (Wh) |

W_{R} | Total energy generation of photovoltaic battery and wind generator (Wh) |

W_{B} | Battery energy capacity (Wh) |

W_{g} | Energy consumption from the grid (Wh) |

W_{L} | Load energy consumption (Wh) |

W_{PVAVD} | Average value of energy, generated by photovoltaic battery per day (Wh) |

W_{WA VD} | Average value of energy, generated by wind generator per day (Wh) |

C_{B} or Q_{R} | Capacity of battery or rated state of charge (Ah) |

Q* | Battery state of charge (%) |

Q*_{d} | Battery state of charge when switching to constant voltage charge mode (%) |

η_{C} | Overall efficiency of converter and inverter (p.u.) |

η_{B} | Battery efficiency (p.u.) |

m_{P} | Coefficient of recalculation of photovoltaic battery power (p.u.) |

m | Coefficient of recalculation of wind power (p.u.) |

k_{E} | Cost reduction factor for the energy consumed from the grid (p.u.) |

t, t_{1}, t_{2}… t_{7} | Time points for load schedule [h] |

T_{d} | Day tariff rate (p.u.) |

T_{n} | Night tariff rate (p.u.) |

## References

- Kartite, J.; Cherkaoui, M. Study of the different structures of hybrid systems in renewable energies: A review. Energy Procedia
**2019**, 157, 323–330. [Google Scholar] [CrossRef] - Ramoji, S.K.; Rath, B.B.; Kumar, D.V. Optimum Design of a Hybrid PV/Wind Energy System Using Genetic Algorithm (GA). IOSR J. Eng.
**2014**, 4, 38–48. [Google Scholar] [CrossRef] - Kumar, N.M.; Chopra, S.S.; Chand, A.A.; Elavarasan, R.M.; Shafiullah, G.M. Hybrid Renewable Energy Microgrid for a Residential Community: A Techno-Economic and Environmental Perspective in the Context of the SDG7. Sustainability
**2020**, 12, 3944. [Google Scholar] [CrossRef] - Kamjoo, A.; Maheri, A.; Putrus, G.A.; Dizqah, A.M. Optimal Sizing of Grid-Connected Hybrid Wind-PV Systems with Battery Bank Storage. In Proceedings of the World Renewable Energy Forum, WREF 2012, Including World Renewable Energy Congress XII and Colorado Renewable Energy Society (CRES), Denver, CO, USA, 13–17 May 2012; pp. 2492–2499. [Google Scholar]
- Menniti, D.; Pinnarelli, A.; Sorrentino, N. A Method to Improve Microgrid Reliability by Optimal Sizing PV/Wind Plants and Storage Systems. In Proceedings of CIRED 2009—The 20th International Conference and Exhibition on Electricity Distribution—Part 2, Prague, Czech Republic, 8–11 June 2009; p. 1. [Google Scholar] [CrossRef] [Green Version]
- Yang, B.; Guo, Y.; Xiao, X.; Tian, P. Bi-level Capacity Planning of Wind-PV-Battery Hybrid Generation System Considering Return on Investment. Energies
**2020**, 13, 3046. [Google Scholar] [CrossRef] - Shavolkin, O.; Shvedchykova, I. Improvement of the Multifunctional Converter of the Photoelectric System with a Storage Battery for a Local Object with Connection to a Grid. In Proceedings of the 2020 IEEE KhPI Week on Advanced Technology, KhPIWeek 2020, Kharkiv, Ukraine, 5–10 October 2020; pp. 287–292. [Google Scholar] [CrossRef]
- Shavelkin, A.; Jasim, J.M.J.; Shvedchykova, I. Improvement of the current control loop of the single-phase multifunctional grid-tied inverter of photovoltaic system. East. Eur. J. Enterp. Technol.
**2019**, 6, 14–22. [Google Scholar] [CrossRef] - Belaidi, R.; Haddouche, A. A multi-function grid-connected PV system based on fuzzy logic controller for power quality improvement. Przeg. Elektr.
**2017**, 93, 118–122. [Google Scholar] [CrossRef] [Green Version] - Vigneysh, T.; Kumarappan, N. Grid interconnection of renewable energy sources using multifunctional grid-interactive converters: A fuzzy logic based approach. Electr. Power Syst. Res.
**2017**, 151, 359–368. [Google Scholar] [CrossRef] - Lliuyacc, R.; Mauricio, J.M.; Gomez-Exposito, A.; Savaghebi, M.; Guerrero, J.M. Grid-forming VSC control in four-wire systems with unbalanced nonlinear loads. Electr. Power Syst. Res.
**2017**, 152, 249–256. [Google Scholar] [CrossRef] [Green Version] - Shavolkin, O.; Shvedchykova, I.; Kravchenko, O. Three-Phase Grid Inverter for Combined Electric Power System with a Photovoltaic Solar Battery. In Proceedings of the 2019 IEEE International Conference on Modern Electrical and Energy Systems, MEES 2019, Kremenchuk, Ukraine, 23–25 September 2019; pp. 318–321. [Google Scholar] [CrossRef]
- Shavolkin, O.; Shvedchykova, I. Improvement of the Three-Phase Multifunctional Converter of the Photoelectric System with a Storage Battery for a Local Object with Connection to a Grid. In Proceedings of the 25th IEEE International Conference on Problems of Automated Electric Drive. Theory and Practice, PAEP 2020, Kremenchuk, Ukraine, 21–25 September 2020; p. 9240789. [Google Scholar] [CrossRef]
- Conext, S.W. Hybrid Inverter. Available online: https://www.se.com/ww/en/product-range-presentation/61645-conext-sw/ (accessed on 1 May 2022).
- Zsiborács, H.; Pintér, G.; Vincze, A.; Birkner, Z.; Baranyai, N.H. Grid balancing challenges illustrated by two European examples: Interactions of electric grids, photovoltaic power generation, energy storage and power generation forecasting. Energy Rep.
**2021**, 7, 3805–3818. [Google Scholar] [CrossRef] - Shavelkin, A.A.; Gerlici, J.; Shvedchykova, I.O.; Kravchenko, K.; Kruhliak, H.V. Management of power consumption in a photovoltaic system with a storage battery connected to the network with multi-zone electricity pricing to supply the local facility own needs. Electr. Eng. Electromech.
**2021**, 2, 36–42. [Google Scholar] [CrossRef] - Shavolkin, O.; Shvedchykova, I.; Demishonkova, S.; Pavlenko, V. Increasing the efficiency of hybrid photoelectric system equipped with a storage battery to meet the needs of local object with generation of electricity into grid. Przeg. Elektr.
**2021**, 97, 144–149. [Google Scholar] [CrossRef] - Forecast.Solar. Available online: https://forecast.solar/ (accessed on 1 May 2022).
- Iyengar, S.; Sharma, N.; Irwin, D.; Shenoy, P.; Ramamritham, K. SolarCast—An Open Web Service for Predicting Solar Power Generation in Smart Homes. In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys 2014, Memphis, TN, USA, 3–6 November 2014; pp. 174–175. [Google Scholar] [CrossRef]
- Central Geophysical Observatory Named after Boris Sreznevsky. Available online: http://cgo-sreznevskyi.kyiv.ua/index.php?lang=en&fn=k_klimat&f=kyiv (accessed on 1 July 2022).
- Vetrogenerator. Available online: http://vetrogenerator.com.ua/base/map/12-karta-vetrov-ukrainy.html (accessed on 1 July 2022).
- Photovoltaic Geographical Information System. Available online: https://re.jrc.ec.europa.eu/pvg_tools/en/tools.html#SA (accessed on 1 May 2022).
- Nicolson, M.L.; Fell, M.J.; Huebner, G.M. Consumer demand for time of use electricity tariffs: A systematized review of the empirical evidence. Renew. Sustain. Energy Rev.
**2018**, 97, 276–289. [Google Scholar] [CrossRef] - Davis, M.J.M.; Hiralal, P. Batteries as a Service: A New Look at Electricity Peak Demand Management for Houses in the UK. Procedia Eng.
**2016**, 145, 1448–1455. [Google Scholar] [CrossRef] - Badawy, M.O.; Cingoz, F.; Sozer, Y. Battery Storage Sizing for a Grid Tied PV System Based on Operating Cost Minimization. In Proceedings of the ECCE 2016—IEEE Energy Conversion Congress and Exposition, Milwaukee, WI, USA, 18–22 September 2016; p. 7854896. [Google Scholar] [CrossRef]
- Krishnamoorthy, M.; Periyanayagam, A.D.V.R.; Santhan Kumar, C.; Praveen Kumar, B.; Srinivasan, S.; Kathiravan, P. Optimal Sizing, Selection, and Techno-Economic Analysis of Battery Storage for PV/BG-based Hybrid Rural Electrification System. IETE J. Res.
**2020**, 1–16. [Google Scholar] [CrossRef] - Traore, A.; Taylor, A.; Zohdy, M.; Peng, F. Modeling and Simulation of a Hybrid Energy Storage System for Residential Grid-Tied Solar Microgrid Systems. J. Power Energy Eng.
**2017**, 5, 28–39. [Google Scholar] [CrossRef] [Green Version] - VaultTec. Vertical WG. Available online: https://vaulttec.org.ua/p559274110-vertikalnyj-vetrogenerator-kvt.html (accessed on 1 May 2022).
- Data Sheet. Lithium Iron Phosphate Battery (LiFePO4) 12.8V 150Ah. Available online: https://www.eco-greenenergy.com/wp-content/uploads/2021/12/EGE-LiFePO4-Battery-12.8V150Ahd.pdf (accessed on 1 May 2022).

**Figure 3.**Oscillograms for February day: (

**a**) Using the night SB charge. (

**b**) Without the night SB charge from the grid.

**Figure 4.**Oscillograms for July day: (

**a**) Cloudy day. (

**b**) Day with PV generation corresponding to the monthly average value.

**Figure 5.**Oscillograms for March day when the forecasted RES generation is 10% lower than the actual generation: (

**a**) no SB discharge in the morning peak; (

**b**) SB discharge in the morning peak.

Interval | (t_{1}, t_{2}) | (t_{2}, t_{3}) | (t_{3}, t_{4}) | (t_{4}, t_{5}) | (t_{5}, t_{6}) | (t_{6}, t_{7}) | (t_{7}, t_{1}) |
---|---|---|---|---|---|---|---|

Months | Load Power P_{L} | ||||||

May, June, July, August (t _{1} = 7.00, t_{2} = 8.00, t_{3} = 11.00, t_{4} = 16.00, t_{5} = 20.00, t_{6} = 23.00, t_{7} = 24.00) | P_{L12} = 0.3P_{L} | P_{L23} = P_{L} | P_{L34} = 0.9P_{L} | P_{L45} = 0.8P_{L} | P_{L56} = P_{L} | P_{L67} = 0.3P_{L} | P_{L71} = 0.2P_{L} |

November, December, January, February (t _{1} = 6.00, t_{2} = 8.00, t_{3} = 10.00, t_{4} = 15.00, t_{5} = 17.00, t_{6} = 21.00, t_{7} = 23.00) | P_{L12} = 0.3P_{L} | P_{L23} = P_{L} | P_{L34} = 0.9P_{L} | P_{L45} = 0.8P_{L} | P_{L56} = P_{L} | P_{L67} = 0.3P_{L} | P_{L71} = 0.3P_{L} |

March, April, September, October (t _{1} = 6.00, t_{2} = 8.00, t_{3} = 10.00, t_{4} = 15.00, t_{5} = 18.00, t_{6} = 22.00, t_{7} = 23.00) | P_{L12} = 0.3P_{L} | P_{L23} = P_{L} | P_{L34} = 0.9P_{L} | P_{L45} = 0.8P_{L} | P_{L56} = P_{L} | P_{L67} = 0.3P_{L} | P_{L71} = 0.3P_{L} |

Month | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. |
---|---|---|---|---|---|---|---|---|---|---|---|---|

W_{PVAVDK}, kWh | 1.34 | 2.12 | 3.47 | 4.171 | 0.98 | 1.81 | 2.83 | 3.83 | 4.26 | 4.48 | 4.37 | 1.14 |

W_{PVAVD}, kWh | 0.98 | 1.81 | 2.83 | 3.83 | 4.26 | 4.48 | 4.37 | 4.16 | 3.52 | 2.49 | 0.96 | 1.03 |

W_{PV}_{23}, kWh | 0.166 | 0.31 | 0.55 | 0.52 | 1.15 | 1.17 | 1.13 | 1.08 | 0.56 | 0.33 | 0.22 | 0.21 |

W_{PV}_{34}, kWh | 0.78 | 1.31 | 1.84 | 2.38 | 2.43 | 2.27 | 2.5 | 2.45 | 2.27 | 1.74 | 0.69 | 0.8 |

W_{PV}_{45}, kWh | 0.038 | 0.16 | 0.32 | 0.84 | 0.53 | 0.63 | 0.61 | 0.53 | 0.72 | 0.39 | 0.03 | 0.021 |

W_{WAVD}, kWh | 37.51 | 35.72 | 38.531 | 33.485 | 29.789 | 32 | 29.8 | 28.429 | 32.97 | 31.897 | 34.122 | 37.93 |

W_{W}_{56}, kWh | 6.33 | 5.88 | 6.32 | 5.53 | 3.3 | 3.35 | 3.29 | 3.17 | 5.245 | 5.3 | 5.5 | 6.32 |

W_{W}_{62}, kWh | 17 | 16.3 | 15.55 | 13.3 | 10.05 | 10.1 | 9.7 | 9.5 | 12.9 | 12.6 | 14.9 | 17.1 |

W_{W}_{24}, kWh | 9.4 | 9.06 | 11.75 | 10.2 | 11.15 | 12.34 | 11.37 | 10.45 | 10.56 | 9.86 | 9.24 | 9.67 |

**Table 3.**Dependence of coefficient of electricity cost reduction k

_{E}on the coefficient of PV power recalculation m

_{p}.

Coefficient | Calculated Values, p.u. | ||||
---|---|---|---|---|---|

m_{p} | 0.555 | 0.598 | 0.63 | 0.67 | 0.716 |

k_{E} (December) | 2.63 | 2.73 | 2.808 | 2.9 | 3 |

k_{E} (Spring-Autumn) | 2.63 | 3 | 3.4 | 4 | 5.09 |

Interval Variable | (t_{6}, t_{2}),t _{6}_{2} | (t_{2}, t_{R}),t _{2R} | t_{R} < t_{5}, (t_{R}, t_{6}),f _{R} | t_{R} ≥ t_{5}, (t_{5}, t_{6}),h _{R} | |
---|---|---|---|---|---|

P_{g} | If P_{R}·η_{C} > P_{L},I ^{1}_{gm} = 0, P_{g} = 0 | If P_{R}·η_{C} > P_{L},I ^{1}_{gm} = 0, P_{g} = 0 | If P_{R}·η_{C} > P_{L},I ^{1}_{gm} = 0, P_{g} = 0 | I^{1}_{gm} = 0, P_{g} = 0 | ${\mathrm{VC}}_{\mathrm{Ig}}\text{}\to {{I}^{1}}_{\mathrm{gm}}\text{}\ge \text{}0,\phantom{\rule{0ex}{0ex}}{P}_{\mathrm{g}}={P}_{\mathrm{B}}-{P}_{\mathrm{L}}\le 0\phantom{\rule{0ex}{0ex}}{P}_{\mathrm{B}}=\frac{\mathsf{\Delta}Q{*}_{56}{W}_{\mathrm{B}}{\eta}_{\mathrm{C}}{\eta}_{\mathrm{B}}}{100({t}_{6}-{t}_{5})}$ |

If P_{R}·η_{C} ≤ P_{L},VC _{Ig} → I^{1}_{gm} ≥ 0,P _{g} = (P_{R}η_{C} − P_{L}) ≤ 0 | If P_{R}·η_{C} ≤ P_{L},VC _{Ig} → I^{1}_{gm} ≥ 0,P _{g} = (P_{R}η_{C} − P_{L}) ≤ 0 | If P_{R}·η_{C} ≤ P_{L},VC _{Ig} → I^{1}_{gm} ≥ 0,P _{g} = (P_{R}η_{C} − P_{L}) ≤ 0 | |||

Reference I ^{1}_{PV} | MPPT P _{PV} = P_{PVM} | MPPT P _{PV} = P_{PVM} | VC_{IPV} →I^{1}_{PV} →P_{PVF},P _{PVF}η_{C} + P_{W}η_{C} = P_{L} + P_{B} | MPPT P _{PV} = P_{PVM} | MPPT P _{PV} = P_{PVM} |

MPPT P_{PV} = P_{PVM} | |||||

Reference I ^{1}_{B} | ${\mathrm{VC}}_{\mathrm{IB}}\text{}\to {{I}^{1}}_{\mathrm{B}},\phantom{\rule{0ex}{0ex}}{I}_{B}=\frac{{P}_{\mathrm{R}}{\eta}_{\mathrm{C}}-{P}_{\mathrm{L}}}{{U}_{\mathrm{B}}}$ | ${\mathrm{VC}}_{\mathrm{IB}}\text{}\to {{I}^{1}}_{\mathrm{B}}\phantom{\rule{0ex}{0ex}}{I}_{B}=\frac{{P}_{\mathrm{R}}{\eta}_{\mathrm{C}}-{P}_{\mathrm{L}}}{{U}_{\mathrm{B}}}$ | I_{B}= I_{B}(Q*) | VC_{IB} → I^{1}_{B},I _{B} = P_{L}/U_{B} | I^{1}_{B}= P_{B}/U_{B} |

I_{B} = 0 | I_{B} = 0 | I_{B} = 0 | |||

SOC | Q*_{6}→ Q*_{2} | Q* ≤ Q*_{d} | Q* > Q*_{d} | Q* → Q*_{6} | Q* → Q*_{6} |

Interval Variable | (t_{1}, t_{2}),t _{6}_{2} | (t_{2}, t_{3}),t _{23} | (t_{3}, t_{R}),t _{3R} | (t_{R}, t_{6}),t _{56} | |
---|---|---|---|---|---|

P_{g} | P_{g}≤ 0,P _{g} = (P_{R}η_{C} − P_{B} − P_{L}) | I^{1}_{gm} = 0, P_{g} = 0 | If P_{R}η_{C} ≥ P_{L},I ^{1}_{gm} = 0, P_{g} = 0 | If P_{R}η_{C} ≥ P_{L},I ^{1}_{gm} = 0, P_{g} = 0 | I^{1}_{gm} = 0, P_{g} = 0 |

If P_{R}η_{C} < P_{L},VC _{Ig} → I^{1}_{gm}, ≥ 0,P _{g} = P_{PV}η_{C} − P_{L} − P_{B} ≤ 0 | If P_{R}η_{C} < P_{L},VC _{Ig} → I^{1}_{gm} ≥ 0,P _{g} = P_{PV}η_{C} − P_{L} − P_{B} ≤ 0 | ||||

Reference I ^{1}_{PV} | MPPT P _{PV} = P_{PVM} | MPPT P _{PV} = P_{PVM} | MPPT P _{PV} = P_{PVM} | VC_{IPV} →I^{1}_{PV} → P_{PVF}P _{PVF}η_{C} + P_{W}η_{C} = P_{L} + P_{B} | MPPT P _{PV} = P_{PVM} |

MPPT P_{PV} = P_{PVM} | |||||

Reference I ^{1}_{B} | VC_{IB} → I^{1}_{B} ≥ 0,if Q* ≤ Q* _{d}, I_{B} = I_{BREF}if Q* > Q* _{d}, I_{B} ≤ I_{B}(Q*) | ${\mathrm{VC}}_{\mathrm{IB}}\text{}\to {{I}^{1}}_{\mathrm{B}},\phantom{\rule{0ex}{0ex}}{I}_{\mathrm{B}}=\frac{{P}_{\mathrm{R}}{\eta}_{\mathrm{C}}-{P}_{\mathrm{L}}}{{U}_{\mathrm{B}}}$ | ${\mathrm{VC}}_{\mathrm{IB}}\text{}\to {{I}^{1}}_{\mathrm{B}},\phantom{\rule{0ex}{0ex}}{I}_{\mathrm{B}}=\frac{{P}_{\mathrm{R}}{\eta}_{\mathrm{C}}-{P}_{\mathrm{L}}}{{U}_{B}}\ge 0$ | I_{B} = I_{B}(Q*) | VC_{IB} → I^{1}_{B}I _{B} = P_{L}/U_{B} |

I^{1}_{B} = 0.1C_{B} | I^{1}_{B} = 0.1C_{B}, I_{B} = I_{B}(Q*) | ||||

SOC | Q*_{6}→ Q*_{2} | Q*_{2}→ Q*_{3} | Q* ≤ Q*_{d} | Q* > Q*_{d} | Q* → Q*_{6} |

Indicator | Jan. | Feb. | Mar. | Apr. | May | Jun. | Jul. | Aug. | Sep. | Oct. | Nov. | Dec. |
---|---|---|---|---|---|---|---|---|---|---|---|---|

One tariff, without a night charge from the grid, m_{P} = 0.67 (+ consumption from the grid is practically absent) | ||||||||||||

ΔQ*_{MAX}, % | 17.03 | 50.8 | 80 | 80 | 60 | 80 | 80 | 80 | 80 | 80 | 12.6 | 9.32 |

k_{E1} | 3.904 | 13.49 | 15.39 | 32.14 | + | 14.77 | 11.31 | 40.54 | 4.34 | 8.164 | 3.8 | 3.132 |

One tariff, without a night charge from the grid, m_{P} = 0.6 | ||||||||||||

ΔQ*_{MAX}, % | 12.1 | 40.8 | 69 | 80 | 80 | 80 | 80 | 80 | 71 | 78.5 | 12.3 | 6.72 |

k_{E1} | 3.637 | 9.275 | 9.67 | 26.48 | + | 14.16 | 10.36 | 32.86 | 3.807 | 7.356 | 3.53 | 2.93 |

Night charge, two tariffs, m_{P} = 0.6 | ||||||||||||

k_{E1} | 3.2 | 7.637 | 9.811 | 26.8 | + | 16.28 | 10.52 | + | 3.706 | 7.66 | 3.149 | 2.745 |

k_{En} | 3.678 | 11 | 13.24 | 27.23 | + | 27.49 | 12.56 | + | 4.716 | 7.276 | 3.505 | 3,43 |

Night charge, two tariffs, m_{P} = 0.67 | ||||||||||||

k_{E1} | 3.393 | 11.9 | 15.39 | 32.3 | + | 16.37 | 11.25 | + | 4.447 | 8.164 | 3.321 | 2.918 |

k_{En} | 3.933 | 16 | 20.64 | 33.4 | + | 27.73 | 13.65 | + | 5.483 | 7.764 | 3.736 | 3.64 |

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**MDPI and ACS Style**

Shavolkin, O.; Shvedchykova, I.; Kolcun, M.; Medved’, D.
Improvement of the Grid-Tied Solar-Wind System with a Storage Battery for the Self-Consumption of a Local Object. *Energies* **2022**, *15*, 5114.
https://doi.org/10.3390/en15145114

**AMA Style**

Shavolkin O, Shvedchykova I, Kolcun M, Medved’ D.
Improvement of the Grid-Tied Solar-Wind System with a Storage Battery for the Self-Consumption of a Local Object. *Energies*. 2022; 15(14):5114.
https://doi.org/10.3390/en15145114

**Chicago/Turabian Style**

Shavolkin, Olexandr, Iryna Shvedchykova, Michal Kolcun, and Dušan Medved’.
2022. "Improvement of the Grid-Tied Solar-Wind System with a Storage Battery for the Self-Consumption of a Local Object" *Energies* 15, no. 14: 5114.
https://doi.org/10.3390/en15145114