Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids. In this paper, an improved energy management strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids, and the optimal allocation of energy storage capacity is carried out by using this strategy. Firstly, the structure and model of microgrid are analyzed, and the output model of wind power, photovoltaic and energy storage is established. Then, considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage, an optimization objective function is established, and an improved energy management strategy is proposed on this basis. Finally, a physical model is built in MATLAB/Simulink for simulation verification, and the energy management strategy is compared and analyzed on sunny and rainy days. The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity. The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days, reduce the interactive power cost between the microgrid system and the power grid. After analyzing the change of energy storage power with cost, we obtain the best energy storage capacity and energy storage power.

In recent years, renewable energy sources such as PVs and wind power have rapidly emerged in the field of microgrids, but with the continuous expansion of power generation capacity, the randomness and volatility of their output have greatly hindered the progress of this field [

For the capacity configuration of energy storage, there have been relevant researches at home and abroad with various methods. Reference [

Most of the above methods start from improving hybrid energy storage and dispatching strategies, and have achieved good results in the optimization of stability and economy [

Based on the above research, an improved energy management strategy considering real-time electricity price combined with state of charge is proposed for the optimal configuration of wind-solar storage microgrid energy storage system, and solved by linear programming [

The wind-solar-storage microgrid system is mainly composed of wind power system, PV system, energy storage system, energy management system and energy conversion device [

The remaining power _{B} (t)_{B}(t) < 0)

When the energy storage battery is discharged (_{B}(t)

In the formula:

To simplify PV generation, the PV output power model is defined as:

The actual output active power of the wind turbine is shown in

The microgrid has the smallest electricity cost in a day. The daily electricity cost when the microgrid is connected to the grid includes the electricity cost purchased by the microgrid from the main grid and the electricity revenue sold to the main grid and battery charging and discharging costs. Therefore, the electricity cost function of the microgrid in one day can be expressed as:

After the optimization objective function is determined, certain constraints need to be met. According to the established economic and power supply reliability objective functions, the constraints are set as wind power, PV power generation output power constraints, voltage constraints, energy storage charge and discharge constraints, and capacity constraints. and load power balance constraints [

(1) Voltage constraints

(2) Energy storage system charge and discharge power constraints

(3) Charge and discharge state transition constraints

In the formula:

(4) Power load balancing constraints

In the formula:

(5) SOC constraints

(6) Transmission line power constraints

The interactive power range between the microgrid and the main network is within the power limit of the transmission line to ensure the safe operation of the system.

In order to ensure the economy of microgrid operation, the relationship between the real-time electricity price of micro-source and the real-time electricity price of the main grid needs to be compared in the grid-connected operation control strategy. After reviewing the data, it was found that the wind photovoltaic price was slightly higher than the minimum electricity price of the grid, 17 Cents/kWh, and did not change much with time. In order to simplify the model, this paper sets the wind and photovoltaic price as a constant, and the purchase and sale price of the main network is the same. In order to reduce the number of battery charge and discharge, improve battery service life, realize peak-valley electricity price arbitrage, and reduce costs, this paper proposes an improved energy management strategy based on real-time electricity price combined with charging state. Adopt a strategy that combines the state of charge with the price of electricity. Because the grid electricity price is generally low when the wind and solar are strong, and high when the wind and solar are insufficient. For microgrids, this paper sets the idea of selling electricity to the main grid as much as possible during periods of high electricity prices, and self-meeting demand as much as possible during periods of low electricity prices. It can prevent frequent charging and discharging of batteries, extend service life, and reduce the interactive power consumption cost between the microgrid system and the main network through peak-valley electricity prices.

During the energy optimization process, the difference between the electric energy provided by the distributed power source and the electric energy required by the load is shown in

According to the electricity price data of a certain place in Gansu, as shown in

Type of electricity price | Time(s) |
---|---|

High electricity prices | 0–0.2 × 10^{4}^{4}–2.5 × 10^{4}^{4}–8.3 × 10^{4} |

Usual electricity prices | 0.2 × 10^{4}–1.5 × 10^{4}^{4}–5.9 × 10^{4}^{4}–8.6 × 10^{4} |

Different management strategies are adopted for different electricity price periods. During periods of high electricity prices, the concrete management strategies adopted are shown in the

When the grid electricity price is at a high level and the wind power and solar power in the microgrid are greater than the load power, priority is given to dispatching the energy storage device to sell the full power generation to the main grid until the minimum value of SOC is reached, while the excess wind energy and photovoltaic power are also connected to the grid to sell electricity until the power limit of the transmission line is reached; Energy greater than the power of the transmission line charges idle batteries, and eventually excess power is discarded [

When the grid electricity price is in the normal period and the wind power and solar power generation power is greater than the load power, the energy storage is prioritized to charge until the maximum value of the SOC, and the excess power can be sold to the grid; If wind and solar power generation is less than the load power, the main grid is dispatched to meet the charging of the load and energy storage up to the maximum value of the SOC.

A summary of the decision variables and their status is shown in

Decision variables | State |
---|---|

Continuous | |

Continuous | |

Continuous |

The physical model of the wind-solar-storage microgrid is built in MATLAB/Simulink, and the bus voltage reference is 5000 V. The rated power of the wind and the PV are both 450 kW. The battery state of charge limits is SOC_{max} = 0.8, SOC_{min} = 0.2,

Parameters | Value |
---|---|

S_{panel} (m^{2}) |
2500 |

0.3 | |

_{p} |
0.3 |

SOC_{min} |
0.2 |

SOC_{max} |
0.8 |

Initial battery rated capacity (kWh) | 2500 |

Fan blade diameter (m)_{1} (m/s) |
18 |

_{e} (m/s) |
12 |

_{2} (m/s) |
17 |

0.45 | |

P_{max} (kW) |
900 |

In the parameter settings of Simulink simulation model, the photovoltaic panel area is 2500 square meters, the photoelectric conversion efficiency is 0.3, the wind power conversion efficiency is 0.3, the upper and lower limits of state of charge are 0.8 and 0.2, the initial battery rated capacity is 2500 kWh, the diameter of wind blades is 18 m, the battery penalty factor is 0.45 and the wind speed range of the fan is shown.

Under the sunny and cloudy scenarios, the optimal operation and economic costs of microgrid energy storage under the control of traditional energy management strategy and improved energy management strategy were analyzed, and the energy storage capacity under the improved strategy was obtained.

(1) Traditional energy management strategies

It can be seen from

(2) Improve energy management strategies

It can be seen from

And observing the output of each unit of the system, it can be seen that in the two high electricity price time periods, the wind and solar power generation is less than the load demand, but the battery is full power generation, and under the premise of meeting the load, the surplus power is supplied to the grid, which better responds to the improved energy management strategy set above, realizes the arbitrage of peak and valley electricity prices, and reduces the interactive power cost between the microgrid and the main network. During the trough period of electricity prices, the main network can be dispatched as much as possible to meet the load of the microgrid and charge energy storage, which can accumulate electricity for the subsequent period of high electricity prices.

Comparing

(1) Traditional energy management strategies

It can be seen from

(2) Improve energy management strategies

It can be seen from

Observing SOC changes under improved energy management strategies on sunny and cloudy days, it was found to be almost identical. Analyzing the output of each unit in both cases, it is found that this is in line with the proposed strategy for improving energy management. During the first period of high electricity prices (0–0.2 × 10^{4} s), the load demand is greater than the wind and solar output power, so the battery is prioritized to discharge at full power, and the load demand is met, and the excess wind and solar power is sold to the main network. Then comes the first normal electricity price period (0.2 × 104–1.5 × 10^{4}), the wind and solar power is not enough to meet the load demand, purchase the main grid power to meet the load and charge the battery at full power. In the second high electricity price period (1.5 × 104–2.5 × 10^{4}), similar to the situation in the first high electricity price period, the load demand is not met, the battery is prioritized to meet the load, and the rest of the wind and solar are sold to the main network. At the end of this period, although the state of charge of the battery has not reached the minimum, the electricity price returns to normal and the discharge still stops. The rest of the tariff periods are similar to those described above and will not be repeated here. It can be seen from the analysis that the real-time electricity price combined with the state-of-charge EMS has a strong control effect on the microgrid and the battery, and the robustness is nice.

It can be seen from

It can be seen from

In microgrids, the power and capacity of energy storage configuration are related to economic costs. This section optimizes battery configuration costs by adding the objective function described above. Taking the rated power of the battery configuration as the decision-making variable, the maximum operating time of 2 or 4 h stipulated by the policy of Gansu Province was selected for optimal configuration. The initial configuration cost is 200 $/kWh, which translates to 0.055 $ per day based on the maximum battery life of 10 years. The microgrid cost explained in the previous section plus the initial configuration cost of energy storage is the total cost, and the total cost of the initial energy storage size is shown in

Scenario | Initial battery size | Cost ($) |
---|---|---|

Sunny | 400 kW/2500 kWh | 519.01 |

Cloudy | 400 kW/2500 kWh | 766.30 |

As can be seen from

The comparison chart and table show that whether on sunny or cloudy days, the cost of configuring a 4-h battery is lower than that of a 2-h battery, and it is also lower than the cost of the initial energy storage size. Although the initial capacity cost of configuring a 2-h battery is lower, the cost of interacting with electricity due to short operating time in the microgrid in this study is greatly increased. Configuring a 4-h battery can well meet the time requirements of the microgrid to respond to real-time electricity prices. Considering the lack of clouds and rain in Gansu Province, a 150 kW/600 kW battery was selected as the optimal configuration for microgrid energy storage.

Aiming at the optimization problem of economic operation in wind-solar microgrid, this paper establishes a model, takes the interactive electricity cost of microgrid and main network as the objective function, proposes an improved energy management strategy based on real-time electricity price combined with state of charge, and compares and analyzes the optimization configuration results under sunny and cloudy days. The following conclusions are drawn:

(1) In the wind-solar-storage microgrid, the real-time electricity price combined with the state-of-charge energy management strategy can coordinate the random fluctuations of wind power, photovoltaics and loads, and can more intuitively reflect the optimal adjustment sequence of each micro-source, and significantly improve the overall economic benefits of the microgrid;

(2) When the light conditions are insufficient, improving the energy management strategy can optimize the charging and discharging of the energy storage system, reduce the frequent fluctuations of the state of charge of the energy storage, overcome the shortcomings of the battery that weakens its function according to the fixed charging and discharging rules, and realize the battery’s effect on the main network.

Charging power of the energy storage

Charging efficiency

Discharging efficiency

Area of photovoltaic panels

Photovoltaic conversion efficiency

Solar irradiance

Air density

Area swept by the blades of the wind turbine

Wind energy utilization efficiency

Wind speed in front of the wind rotor

Power sold to the grid

Selling price

Electricity purchased from the main grid

Purchase electricity prices

Interactive power between microgrid and the main network

Maximum transmission line power

This paper is a phased achievement of Gansu Province’s Major Science and Technology Project (W22KJ2722005) “Research on Optimal Configuration and Operation Strategy of Energy Storage under “New Energy + Energy Storage” Mode”.

The authors declare that they have no conflicts of interest to report regarding the present study.