Research on the operation strategy of integrated optical storage and charging distribution network based on multi-objective optimization

The integrated photovoltaic storage and charging system is a new type of energy system integrating photovoltaic power generation, energy storage and charging, which has the advantages of high efficiency, environmental protection and energy saving. However, the optimization of the integrated photovoltaic storage and charging system is challenging due to its complex system structure and multiple objective requirements [1-4]. In order to realize the optimal performance of the system, multiple objectives must be considered, such as energy utilization, economy, environmental friendliness, and system reliability. Meanwhile, demand response is also an important factor to optimize the integrated optical storage and charging system, because it can achieve a positive response to the grid and improve the flexibility and controllability of the system [5-8].

As a product of the combined construction of photovoltaic, energy storage and charging station, the optical storage and charging integrated distribution grid is suitable for large centralized fast charging stations, industrial and commercial parks, commercial residences and other places. It utilizes the battery energy storage system to absorb the low valley electricity and support the fast charging load during the peak period, and at the same time is supplemented by the photovoltaic power generation system [9-12]. Through photovoltaic power generation and energy storage to optimize the energy allocation and reduce the cost of electricity, not only to achieve the local consumption of distributed power generation, but also to effectively reduce the load peak and valley difference of electric vehicle charging station [13-15], improve the system operating efficiency, enhance the social value of the enterprise and the development of the driving force, to occupy a small area, the advantages of relatively low investment, multiple sources of income, etc., to become an effective park integrated energy service construction It has become an effective model of integrated energy service construction in the park with its advantages of small footprint, relatively low investment, and multiple sources of revenue [16-18].

This paper takes the light storage and charging integrated microgrid system as the research object, firstly, designs the structure of the light storage and charging integrated microgrid system and briefly describes its operation process, analyzes the operation characteristics of photovoltaic, storage and electric vehicle in the microgrid and establishes a reasonable mathematical model. On this basis, this paper takes into account the customer’s satisfaction with electricity consumption, establishes an optimization model with the objective of peak shaving and valley filling under time-sharing tariffs according to the elasticity coefficient of electricity price, and proposes an optimal operation strategy that takes into account the peak and valley shifts of loads. In order to accurately configure the energy storage capacity, the photovoltaic output is equivalently determined, and then the optimization results are used to verify the distribution network operation strategy of this paper.

2

Multi-objective optimization-based operation of integrated optical storage and charging distribution network

2.1

Optical storage and charging integrated distribution network structure

Optical storage and charging integrated system is a high-tech green charging mode, which integrates the technologies of new energy, energy storage and intelligent charging in order to realize the basic balance between the local new energy production and electric vehicle power load. Optical storage and charging integration system is an important means to realize the coordination between new energy power production and electric vehicle charging, and has become one of the current research hotspots in the energy field.

Optical storage and charging integrated microgrid mainly includes independent and grid-connected two models. Independent microgrids are usually established in remote areas, and their main purpose is to provide normal and reliable power supply. Since these areas have a single and unstable power source, it is often necessary to equip diesel generators as a backup power source. In this case, the energy storage system plays an important role, as the main power supply can smooth the system power fluctuations and guarantee the system’s black start and other functions. In contrast, grid-connected microgrids are mainly used in industrial parks, communities and other areas with dense electricity consumption, and their main purpose is to better consume PV energy. Unlike stand-alone microgrids, there is no need for additional controllable power supply as the large power grid has become the support for grid-connected microgrids. In this case, the role of the energy storage system, in addition to including the obligations assumed by the stand-alone microgrid energy storage, can also perform power regulation functions such as peak shaving and valley filling. Therefore, the optical storage and charging integrated microgrid system can not only meet the power demand of different regions and power scenarios, but also contribute to the development of clean energy.

The structure of the optical storage and charging integrated microgrid system is shown in Figure 1, which mainly includes four main parts: photovoltaic (PV) power generation system, energy storage system, residential base loads and electric vehicle loads, and is connected to the AC bus (BUS) for energy transmission through different types of devices. The system is mainly maintained by the PV and the energy storage system together to ensure the normal operation of the system. When the photovoltaic storage energy is insufficient to meet the system load demand, the main power grid can be supplemented as a backup power source to ensure the stable operation of the system. In addition, when the large grid is disconnected, the system will be transformed into a typical independent microgrid. In practice, the grid-connected microgrid can be used in industrial parks and communities and other power-intensive occasions, able to consume more photovoltaic power without the need for additional controllable power supply, but also can be used for peak shaving and valley filling purposes.

2.2

Mathematical model of optical storage-charging integrated microgrid system

Photovoltaic power generation is a non-polluting, sustainable and clean energy source, and its power generation structure consists of multiple photovoltaic cells connected in series and parallel to increase the output voltage and current of the photovoltaic, relying on the “photovoltaic effect” to convert the solar energy absorbed by the photovoltaic panels into electricity, and ultimately, the generated electricity is delivered to the DC loads through the lines or converted to AC power through the inverters. The power generated is eventually delivered to DC loads through lines or converted to AC power through inverters for transmission to the distribution grid. As the output current, voltage and power of the PV system are affected by light intensity and ambient temperature, the output current and voltage of the PV system are: (1) $I = I_{L} - I_{0} [\exp (\frac{q (U + I R_{s})}{A K T}) - 1]$

I_L denotes the photogenerated current, I₀ denotes the diode reverse saturation current, q denotes the electronic charge, U denotes the photovoltaic cell output voltage, I denotes the photovoltaic cell output current, R_S denotes the series resistance, and A, K, and T denote the Boltzmann’s constant, P–N junction ideality factor, and the absolute temperature, respectively.

When the photovoltaic panel temperature is certain, with the increase of light intensity, the output current also rises significantly, relative to the change in current, the output voltage with the increase in light intensity and rise is not obvious, while the increase in light intensity leads to an increase in the current, so the photovoltaic system output power is also increased with the increase in light intensity.

The photovoltaic power is mainly affected by the surface temperature of the photovoltaic panel, light intensity and the area of the photovoltaic panel, and its output power mathematical model is: (2) $P_{P V} (t) = η_{P V} * S * I_{P V} (t) * [1 - α * (T_{b} (t) - T_{w})]$

P_PV(t) represents t moments of photovoltaic output power, η_PV represents the conversion efficiency of photovoltaic power generation, S represents the area of photovoltaic panels, I_PV(t) represents the intensity of light irradiated in the photovoltaic panels at the time of t moments, α represents the temperature coefficient, T_b(t) represents t moments of surface temperature of photovoltaic panels, and T_w represents the standard temperature of batteries under the normal operating conditions of photovoltaic systems, which is taken to be 25°C.

Photovoltaic system in daily use will cause a certain loss of internal photovoltaic batteries, and photovoltaic panel life with the use of longer and faster decline, in order to facilitate the calculation of microgrid scheduling economy, this paper establishes the photovoltaic power generation cost model for: (3) $Q_{p v} = {Q^{'}}_{p v} (1 - Q_{g p})$ (4) $C_{p v} = \frac{μ_{p v} V_{p v} + o_{p v}}{Q_{p v}}$ (5) $F_{P V} (t) = P_{P V} (t) C_{p v}$

Q_pv and ${Q^{'}}_{p v}$ denote the actual and ideal annual power generation of the PV system, respectively, C_pv denotes the PV unit power generation cost, J_pv, V_pv and O_pv denote the construction cost, depreciation factor and operation cost of the PV system, respectively: F_PV(t) denotes the PV power generation cost of the t moments.

When the energy storage system participates in microgrid scheduling, the charging and discharging power and charge state of its batteries greatly affect the operation of the system: (6) $S O C_{E S S} (t + 1) = S O C_{E S S} (t) + \frac{P_{E S S, c h} (t) * η_{E S S} + Δ t}{E_{E S S}}$

The mathematical model of the discharge of the energy storage system is: (7) $S O C_{E S S} (t + 1) = S O C_{E S S} (t) + \frac{P_{E S S, d c h} (t) * Δ t}{E_{E S S} * η_{E S S}}$

In the formula, SOC_ESS(t) indicates the charging state of the storage battery at the moment of t, P_ESS,ch(t) and P_ESS,dch(t) indicate the charging and discharging power of the storage system at the moment of t, η_ESS indicates the charging and discharging efficiency of the storage system at the moment of t, and E_ESS indicates the total capacity of the battery of the energy storage system.

The battery life of the energy storage system is mainly determined by the charge state of the energy storage battery during charging and discharging and the number of charging and discharging times. Therefore, in order to protect the performance and life of the energy storage system, it is necessary to set limits on the maximum and minimum values of the state of charge of the energy storage battery to prevent overcharging and overdischarging of the energy storage battery. The state of charge (SOC) of the battery is expressed as the ratio of the remaining power of the energy storage system to the total rated capacity of the energy storage system, when SOC = 100% indicates that the energy storage battery is fully charged, and when SOC = 0% indicates that the energy storage battery is fully discharged. In practice, in order to extend the service life of the battery, when the charge state of the energy storage battery is lower than the minimum value of $S O C_{E S S}^{\min}$ , the energy storage system stops external discharge: when the charge state of the energy storage battery is higher than the maximum value of $S O C_{E S S}^{\max}$ , the energy storage system stops charging. The battery constraint of the energy storage system is: (8) $S O C_{E S S}^{\min} \leq S O C_{E S S} (t) \leq S O C_{E S S}^{\max}$

where $S O C_{E S S}^{\min}$ and $S O C_{E S S}^{\max}$ represent the minimum and maximum values of the charge state of the energy storage battery, respectively.

In the microgrid system, in order to calculate the economy of system scheduling, the replacement cost of the energy storage system needs to be considered, and the loss cost caused by the discharge is calculated based on the depth of discharge of the energy storage system at each moment, and the cost of charging and discharging loss of the energy storage battery is: (9) $D O D (t) = 1 - S O C_{E S S} (t)$ (10) $I_{c} (D O D (t)) = a * {(D O D (t))}^{- b}$ (11) $F_{E S S} (t) = \frac{c_{E S S, c a p} * P_{E S S} (t) \cdot Δ t}{E_{E S S} * I_{c} (D O D (t)) \cdot η_{E S S}^{2}}$

In the formula, DOD(t) indicates the discharge depth of the storage battery at t moments: I_c(DOD(t)) indicates the number of cycles of the storage system at t moments, a and b indicate the curve fitting coefficients of the degradation model of the storage battery, C_ESS,cap indicates the replacement cost of the storage system, and F_ESS(t) indicates the degradation cost of the storage system at t moments.

Orderly charging through rational optimal scheduling can reduce the load peak and valley differences and increase the economy and stability of distribution network operation, and the charging mathematical model is: (12) $P_{E V} (t) = \sum_{n = 1}^{N} (P_{E V C} (t) * C_{n, E V} (t))$ (13) $S O C_{E V} (t + 1) = S O C_{E V} (t) + \frac{P_{E V C} (t) γ Δ t}{E V R}$ (14) $0 \leq C_{n, E V} (t) \leq 1$

2.3

Multi-objective optimization model based on load peak-to-valley shift

It is assumed that the system can maximize the use of PV power, the PV output can be accurately predicted, while the state of charge SOC of the energy storage can be accurately uploaded to the energy management center by the monitoring equipment.

As an example, the multi-objective optimization model can be simplified and expressed as the following equation for finding the minimum value: (15) $\begin{matrix} \min f (x) = [f_{1} (x), f_{2} (x), \dots, f_{n} (x)] \\ s . t . h_{i} = 0 i = 1, 2, 3, \dots, m \\ g_{j} \leq 0 j = 1, 2, 3, \dots, l \end{matrix}$

f_n(x) is the nnd objective function, n is the number of objective functions, m is the number of equation constraints, h_i is the ith equation constraints, l is the number of inequality constraints, g_j is the jth inequality constraints.

This paper establishes the optimal dispatch objective function for EV charging loads and base loads to participate in demand response on the basis of the optimal operation strategy with optimization period T = 1,2,…,24. From zero to one a.m. as the first time period, and so on. On the basis of meeting the demand for electricity in the system, the optimization objectives of this paper are as follows:

1)

Minimize the peak-to-valley difference of load in the system

In the case of demand response by users, in order to smooth the load fluctuation, ensure the stability and reliability of system operation, and at the same time consider the economy of electricity consumption by users, the transferable load ΔP_Z is added to the optimization model, which is cut in the peak hours of the electricity price, and increased in the trough period of the electricity price, which means that it reduces the user’s electricity cost, and reduces the peak-valley difference of load in the system. Because the disorder of electric vehicle charging load makes the system load peak-valley difference obvious, which is not conducive to the stable economic operation of the system, so the load peak-valley difference is minimized as one of the optimization objectives.

Objective function 1: (16) $\min C_{1} = \min (\max P_{i} - \min P_{i})$

Among them: (17) $P_{i} (t) = {\begin{matrix} P_{0} (t_{1}) - Δ P_{Z} (t_{1}) & t_{1} \in S_{1} \\ P_{0} (t_{2}) & t_{2} \in S_{2} \\ P_{0} (t_{3}) + Δ P_{Z} (t_{3}) & t_{3} \in S_{3} \end{matrix}$

Where P_i is the load after customer response, including electric vehicle load and other loads. S₁, S₂ and S₃ are the peak tariff hours, normal tariff hours and low tariff hours respectively. P₀(t₁), P₀(t₂), P₀(t₃) for the price of electricity before the peak period, the price of electricity in the ordinary period, the price of electricity before the user response in the valley, ΔP_Z(t₁) and ΔP_Z(t₂) for the price of electricity in the peak period and the price of electricity in the valley of the transfer of loads, according to the principle of scheduling peak shaving to fill in the valley, ΔP_Z(t₁) for the peak of the electricity load to cut the load, ΔP_Z(t₂) for the increase in the electricity consumption of the load in the valley, in the ordinary period, the transfer of loads is zero.

2)

Minimize the economic operation cost of the system

Objective function 2: (18) $\min C_{2} = \sum_{t = 1}^{T} [C_{P V} (t) + C_{E S S} (t) + C_{G} (t) + C_{L} (t)]$

In the formula, T is a dispatch cycle, i.e., a typical day length, and t=1 is the first time period from 0:00 to 1:00. C_PV(t) is the cost of PV power generation in time period t, C_ESS(t) is the O&M cost of energy storage in time period t, C_G(t) is the subsidy for power purchase and sale of the system in time period t, and C_L(t) is the load transfer subsidy for participation in demand response in time period t. The specific expression of each cost is as follows: (19) $C_{P V} (t) = k_{P V} P_{P V} (t)$

where k_PV is the PV power generation cost coefficient, and P_PV(t) is the PV power generation power in time period t.

(20)

C_{E E S} (t) = k_{E S S} | P_{E E S} (t) |

Where k_ESS is the battery maintenance coefficient, P_ESS(t) is the charging and discharging power in t time periods, which can be obtained according to the basic parameters of the energy storage equipment: (21) $C_{G} = k_{G} (t) P_{G} (t)$

Where k_G(t) is the selling or purchasing tariff for time period t, and P_G(t) is the power exchanged between the system and the main grid for time period t, which is positive for purchasing and negative for selling, and is determined by the transformer capacity of the system: (22) $C_{L} = k_{L} Δ P_{Z} (t)$

Where k_L is the compensation cost of the transferable load, ΔP_Z(t) is the power of the transferable load in the t time period, the tariff is ΔP_Z(t) = ΔP_Z(t₁) in the peak period, ΔP_Z(t) = ΔP_Z(t₂) in the trough period, and 0 in the usual period, and increases or reductions are taken as positive values.

Constraints:

1)

Power balance constraints:

(23)

P_{P V} (t) + P_{G} (t) + P_{E S S} (t) = P_{L} (t) + P_{E V} (t)

Where, P_G is the power exchanged between the system and the grid, the sale of power is negative, the purchase of power is positive, P_ESS is the charging and discharging power of the energy storage, charging is negative, discharging is positive.

2)

PV active output constraints:

(24)

P_{p v, \min} < P_{P V} (t) < P_{p v, \max} a

where P_pv_,min, P_pv_,max are the minimum and maximum power of PV power generation, respectively.

3)

Battery charge state and power constraints:

(25)

| P_{E S S} (t) | \leq P_{e s s, \max}

(26)

S O C_{\min} < S O C (t) < S O C_{\max}

where P_ess_,max, is the maximum charging and discharging power of the energy storage, and SOC(t) is the state of charge of the energy storage battery.

4)

System and large grid transmission capacity constraints:

(27)

| P_{G} (t) | < P_{G \max}

where P_G_max is the maximum exchange power between the microgrid and the larger grid, and the transfer power between the system and the larger grid is affected by the distribution substation capacity and cannot exceed the maximum value of the transfer capacity.

5)

Transfer load power constraint:

(28)

Δ P_{z \cdot \min} < Δ P_{Z} (t) < Δ P_{z \cdot \max}

Considering the customer’s electricity experience, the amount of load transferred in each time period is not allowed to exceed the maximum load transfer.

6)

Total load transfer constraints:

(29)

\sum_{t_{1}}^{r_{t} \in S_{1}} Δ P_{Z} (t_{1}) = \sum_{t_{3}}^{t_{3} \in S_{3}} Δ P_{Z} (t_{3})

According to the principle of load shifting, the load shifted into the tariff valley and the load shifted out of the tariff peak should be equal in the same cycle T.

3

Multi-objective optimization algorithm analysis and validation

3.1

PV Output Curve Establishment

The example data in this paper come from a group of 500KW PV units in a 40MW PV power station in area H, including the real PV power generation and environmental data for one year in 2021, and by analyzing the PV data and determining the PV output in this paper, the PV daily power generation power generation status is shown in Fig. 2. It can be seen that the daily PV power generation in area H in 2021 is between 700~3500kWh, and the difference in monthly power generation is not big, for smaller PV power plants once the installed PV capacity is determined, its monthly power is enough to support the consumption of loads and the monthly fluctuation of PV and the impact on the grid is relatively small, but for the larger the installed capacity of the PV power plant, the more violent the fluctuation of the influence of the environment, the difference of the cumulative power generation will make the PV power generation more and more difficult, and the difference of the cumulative power generation will make the PV power generation more and more difficult. However, the larger the installed capacity of the PV plant, the more drastic the fluctuations are due to the environmental impacts, and the difference in the cumulative power generation will cause the PV power to flow into the grid, and the voltage, current, and power quality of the grid will be affected, destroying the security and reliability of the grid.

Photovoltaic power generation environmental conditions as shown in Figure 3, it can be seen that the PV daily power generation and PV panel irradiance and temperature related, and in line with the obtained power and light intensity and temperature change rule. Among them, the highest light intensity and PV panel temperature is concentrated in July, August and September, that is, the summer of the year, irradiance and panel temperature were 31MJ/m² and 32° respectively. The lower light intensity and negative temperature at both ends show a clear seasonal variation rule, but since PV power generation is greatly affected by weather factors, the weather impact needs to be considered when determining PV output and installed capacity.

PV output power in different seasons of different weather output curve is different, in order to accurately configure the energy storage capacity, the need for PV output power equivalent determination, taking into account the PV penetration rate and the impact of grid abandonment, the four seasons of sunny day PV output power of the average solution. PV equivalent output results are shown in Figure 4, the equivalent PV output curve as the basis for the optical storage configuration, when the PV output efficiency is 0.85.

3.2

Analysis of optimization results

In order to verify the effectiveness of the optimization model proposed in this paper, four strategy scenarios are set up for comparative analysis. Strategy 1 is to take the optimization model proposed in this paper as the baseline scenario. Strategy 2 is based on strategy 1, and flexible load clusters are not involved in optimized scheduling. Strategy 3 is based on strategy 2, without considering SOP access operation. Strategy 4 is based on strategy 3 without considering VPP interconnection with the distribution network.

The comparison results of different strategy scenarios are shown in Table 1. Comparing strategies 3 and 4, it can be known that when the VPP is interconnected with the distribution grid via the contact line, the distribution grid operating cost is reduced by 7.3% due to the distribution grid purchasing power from the VPP in the vicinity of the VPP, the voltage offset is increased by 3.22 pu, and the VPP operating cost is reduced by 1349.89 yuan, and the interconnection of the VPP and the distribution grid can significantly reduce the distribution grid and VPP operating cost and peak-to-valley difference by 0.36 Kw. However, it increases the voltage offset and peak-to-valley difference.

Table 1.

The effect of the difference in different situations

Situational pattern	Distribution cost	Voltage offset(pu)	VPP cost	Peak valley(kW)
1	37365.72	42.31	658830.84	2.692
2	38316.55	41.85	658830.84	2.657
3	41035.38	42.42	657480.24	2.754
4	44301.32	39.20	658830.13	2.394

Comparing strategies 2 and 3, it can be seen that after the introduction of SOP in the distribution network, SOP improves the tidal current distribution, the voltage offset of the distribution network decreases from 42.42 to 41.85, and the operating cost is reduced. Although the operating cost of VPP increases to 658830.84 Yuan, the comprehensive cost of the system decreases to 38316.55 Yuan, and the introduction of SOP in the distribution network can significantly improve the voltage quality of the distribution network and reduce the comprehensive cost of the whole system.

Comparing strategies 1 and 2, it can be seen that when scheduling flexible load clusters, the voltage offset of the distribution network increases by 0.46, the operating cost decreases by 950.83 yuan, and the operating cost of VPP remains unchanged, and the participation of flexible load clusters in scheduling can effectively reduce the operating cost of the distribution network. Comprehensively, it can be seen that when SOP and VPP are accessed under the optimization model of this paper and flexible load cluster scheduling, it can not only meet the requirements of users’ power quality, but also reduce the system operating cost and optimize the operation of distribution network.

4

Conclusion

Aiming at the development needs of the optical storage and charging integrated distribution network, this paper carries out a multi-objective optimization operation research on the optical storage and charging integrated system in order to realize the economic optimization of the distribution network, and summarizes the work and conclusions of the whole paper as follows:

This paper analyzes the working characteristics of the photovoltaic storage and charging integrated microgrid system, establishes an optimization model with load peak shaving and valley filling as the objective, and proposes an optimization operation strategy that considers load peak and valley transfer. This paper analyzes the PV data and determines the PV output of this paper. Among them, the daily PV power generation is between 700~3500kWh, and the highest light intensity and PV panel temperature are concentrated in July, August and September, which are the summer months of the year, with irradiance and panel temperature of 31MJ/m² and 32°C, respectively. By averaging the PV output power of the PV output power of the four sunny days of the seasons and solving the problem, the PV output efficiency after the PV equivalence is 0.85 and the PV output efficiency after the equivalence is 0.85. The PV output curve of the PV output power is taken as the photovoltaic storage curve of the photovoltaic storage system. The equivalent PV output curve is used as the basis for the photovoltaic storage configuration.

The interconnection of VPP with the distribution network can significantly reduce the operating cost of the distribution network and VPP, but the peak-to-valley difference is increased by 0.36Kw and the voltage offset is increased. Comparing strategies 2 and 3, it can be seen that after the introduction of SOP in the distribution network, the voltage offset of the distribution network decreases from 42.42 to 41.85, and the operating cost decreases, but the comprehensive cost of the system decreases to 38316.55 yuan, and the introduction of SOP in the distribution network can significantly improve the voltage quality of the distribution network, and reduces the comprehensive cost of the whole system. When scheduling for flexible load clusters, the voltage offset of the distribution network increases by 0.46, the operating cost decreases by 950.83 yuan, the operating cost of VPP remains unchanged, and the participation of flexible load clusters in scheduling can effectively reduce the operating cost of the distribution network. Comprehensively, it can be seen that when SOP and VPP are accessed under the optimization model of this paper and flexible load cluster scheduling, it can not only meet the requirements of power quality of users, but also reduce the system operating cost and optimize the operation of the distribution network.

Funding:

State Grid Hebei Electric Power Co., Ltd. Technology Project Funding - Research and Development of Local Consumption Devices for Integrated Photovoltaic, Storage, and Charging Systems and Control Strategy Research-kj2023-032.

Idioma:: Inglés

Calendario de la edición:: 1 veces al año
Temas de la revista:: Ciencias de la vida, Ciencias de la vida, otros, Matemáticas, Matemáticas aplicadas, Matemáticas generales, Física, Física, otros

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Research on the operation strategy of integrated optical storage and charging distribution network based on multi-objective optimization

Yu Zhang

Shicheng Huang

Bo Yuan

Jinyue Shi

Hao Sun

Publicado en línea: 17 mar 2025

Recibido: 22 oct 2024

Aceptado: 01 feb 2025

DOI: https://doi.org/10.2478/amns-2025-0216

Palabras claveMulti-objective optimization, Load peak-to-valley transfer, Microgrid system, Optical storage charging integration

© 2025 Yu Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Multi-objective optimization, Load peak-to-valley transfer, Microgrid system, Optical storage charging integration