Power Dispatching Strategy Considering the Health Status of Multi-Energy Conversion Equipment in Highway Power Supply Systems

Abstract

In order to extend the service life of a highway power supply system and the level of new energy consumption, a power dispatching strategy considering the health status of multi-energy conversion equipment is proposed in this paper. Firstly, the energy and load forms of the highway power supply system are introduced, and the structure of the multi-energy conversion equipment, the topological structures of the DC–DC and DC–AC modules, and the operating characteristics are analyzed. Secondly, the module temperatures and output voltages are used as main parameters to establish the health indexes of DC–DC and DC–AC modules, and then the health index of the multi-energy conversion equipment is further calculated. Thirdly, the new energy consumption index is defined, and a multi-objective optimization model for power dispatching of highway power supply systems is established with the goal of improving the health index of multi-energy conversion equipment and the new energy consumption index. The case study shows that the power dispatching strategy in this paper can better control the temperature of each module, improve the health status of multi-energy conversion equipment, and have a high level of new energy consumption.

1. Introduction

Highways are important basic transportation facilities in the process of national economic construction and development. The power supply system plays a vital role in maintaining the normal operation of the highway system. Highway power supply systems are generally located in remote areas with a large power supply radius and weak connection with the power grid. On the other hand, the highway power supply systems have gradually become polymorphic in terms of energy supply and load type, with polymorphic energy and load access such as wind, solar, electricity storage, heat, and hydrogen. Especially with the popularization of new energy vehicles, the charging load of electric vehicles has grown rapidly, and the peak-to-valley difference of the power supply system has increased day by day. The highway power supply system has polymorphic power load, changeable external climate, and untimely maintenance, and the problem of power supply reliability is becoming increasingly prominent.

Currently, there are few studies on the power dispatching of highway power supply systems. The main studies focus on multi-energy complementary considering economic dispatching and new energy consumption. In terms of economic dispatching, reference [1] proposes a comprehensive economic model of the multi-microgrid for optimizing the power dispatching, and the source network load storage is taken into account. For the full consumption of photovoltaic (PV) power, reference [2] uses HOMER Grid software simulation to propose the economic benefit analysis method of PV plus battery energy storage systems (BESS) applied to the behind-the-meter (BTM) market, which is to consider the effective use of renewable energy. Reference [3] comprehensively considers the total operating cost of the system and the fluctuation of total power output and optimizes the dispatching of the wind power, PV power, and hydropower complementary systems. Reference [4] studies the economic efficiency of coal in the optimal dispatching of wind power, hydropower, and thermal power based on the randomness of wind power and load. Reference [5] considers the peak and frequency regulation of the system to establish a wind power, hydropower, and thermal power optimal dispatching model. Reference [6] studies the optimal dispatching of hydropower and wind power coordination based on influencing factors such as wind power, electricity prices, and water resources reserves. Reference [7] considers the charging and discharging power performance of an energy storage system to establish a wind power, hydropower, and energy storage system optimization dispatching model.

In terms of new energy consumption, in reference [8], the new operational conditions of conventional generators are taken into account for the safety and economics of the power system with a high proportion of renewable energy. Reference [9] establishes an optimal dispatching model for wind, solar, water, thermal, and energy storage. The objective function is to minimize the comprehensive costs of power generation, pollutant control, and renewable energy curtailment. The results show that the model can reduce the curtailment rate of renewable energy and the system operating costs and effectively smooth out power fluctuations. Reference [10] establishes a wind–solar–thermal–storage optimization dispatching model based on the optimal wind-solar energy curtailment rate, indicating that reasonable energy curtailment is conducive to improving the overall economic benefits of the system. Reference [11] establishes a wind–storage–hydro–thermal optimization dispatching model based on the load mean deviation method and multi-objective optimization solution strategy, effectively exerting the role of multi-energy complementarity and improving the consumption of clean energy. Reference [12] establishes a wind–hydro–thermal joint dispatching model by optimizing carbon emissions through electricity substitution and improving the consumption of new energy and environmental protection. Reference [13] proposes a multi-energy complementary optimization dispatching model for wind power, PV power, hydropower, thermal power, and energy storage with consideration of the impacts of the peak load regulation initiative of thermal power units. The research results show that the model proposed in [13] is helpful to improve the consumption of new energy. Reference [14] considers the randomness and volatility of renewable energy and quantifies the power balance problem of power system with high proportion renewables. Reference [15] focuses on power systems a with high proportion of renewable energy and proposes key measures to solve the problem of renewable energy consumption from multiple aspects, including the construction of complementary power sources, flexibility transformation of thermal power units, and demand-side response.

In summary, current power dispatching strategies rarely pay attention to the operating status of key equipment in the power grid. The health index method originated from the assessment of human health status and is also used to indicate the expected performance of the evaluated object [16]. It has been applied to measure and indicate the comprehensive performance of objects in many fields, such as spacecraft, aircraft engines, rocket engines, bridges, and other physical systems [17,18,19]. The concept of electric equipment health was first proposed by British scholar D. Hughess in 2003. After years of development, electric equipment health has gradually become a concept and expanded to multiple areas of power supply reliability. Most of the health analyses in the power industry use evaluation methods based on expert experiences [20,21,22,23,24].

Multi-energy conversion equipment is the core power electronic equipment in highway power supply systems. Current research ignores the impacts of the converter operating status, especially temperature, on the health status of the converter. Therefore, this paper takes a highway power supply system in southwest China as the research background and the multi-energy conversion equipment, the core equipment of the power supply system, as the research object. It analyzes the factors affecting and evaluating the reliable operation of multi-energy conversion equipment, establishes the health index of the multi-energy conversion equipment and the multi-objective optimization model of power dispatching based on the health index, and extends the service life of the multi-energy conversion equipment on the basis of meeting the energy demand of the highway power system.

The remainder of this paper is structured as follows: Section 2 introduces the basic functions and structure of the multi-energy conversion equipment of the highway power supply system, the DC–DC and DC–AC module topologies, analyzes the fever conditions of the modules, and proposes a method for calculating the health index of the multi-energy conversion equipment based on the temperatures and voltages. Section 3 establishes a multi-objective power control strategy that comprehensively considers the health status of multi-energy conversion equipment and the consumption level of new energy. The constraints are the module power limitations, state of charge (SOC) of energy storage system constraints, and hydrogen production constraints. Section 4 is the case study, which verifies and analyzes the power dispatching strategy of the highway power supply system proposed in this paper. The result shows that the power dispatching strategy proposed in this paper can improve the health index of multi-energy conversion equipment while meeting the power demand of highway service areas and, at the same time, has a higher level of new energy consumption.

2. Health Status Analysis of Multi-Energy Conversion Equipment

2.1. Multi-Energy Conversion Equipment of Highway Power Supply Systems

Multi-energy conversion equipment is manufactured by BJ-NEGO Automation Technology Co., Ltd. in Beijing, China. As the core electric device of the highway power supply system, the multi-energy conversion equipment takes power conversion as its main task and is supported by the power system to achieve multi-energy conversion and coordinated power dispatching based on wind power, PV power, power grid electricity power, and an energy storage system. The internal modules of the multi-energy conversion equipment are divided into two categories according to the circuit topology: DC–DC modules and a DC–AC module. The multi-energy conversion equipment adopts a rack mode, and multiple DC–DC modules are inserted into the racks vertically or horizontally to ensure its heat dissipation capacity and neatness. Among them, the DC–DC modules are connected to the 1500 V high-voltage DC bus, which is responsible for the energy access and control of wind power, PV power, energy storage, fuel cells, and DC loads. Wind power and PV power are the main power sources for highway power supply systems. The energy storage system is responsible for the stability of DC and cooperates with system scheduling to implement multi-objective control strategies. The bidirectional DC–AC module is connected to the 690 V AC bus, which is responsible for the DC-to-AC inversion, connected to the external power grid, and supplies power to each energy-consuming unit in the highway power supply system. The structure of the highway power supply system with multi-energy conversion equipment is shown in Figure 1.

The rated capacity of a single DC–DC module in the multi-energy conversion equipment is 270 kW. It adopts a three-phase interleaved parallel three-level buck–boost converter. The topology is shown in Figure 2a. Each module consists of 12 insulate-gate bipolar transistors (IGBT). The power consumption of a single IGBT is 321.2 W, and the total power consumption of IGBTs is 3854.4 W. According to the simulation condition of ambient temperature (air inlet) of 55 °C, the maximum temperature simulation result of IGBT is 122.5 °C. The rated capacity of the bidirectional DC–AC module is 1.725 MW. It adopts an I neutral point clamped (I-NPC) three-level inverter. The topology is shown in Figure 2b. The module consists of 12 IGBTs. The total module loss is 6208.8 W and the maximum temperature is 108.9 °C.

2.2. Health Index of Multi-Energy Conversion Equipment Based on Operating Status

In the early stage of highway power supply system operation, the historical operation and maintenance data of multi-energy conversion equipment are lacking, and it is impossible to establish the outage model of each component like the traditional reliability analysis method. Therefore, this paper evaluates the operating status of multi-energy conversion equipment based on the operating parameters such as the temperature of each module of multi-energy conversion equipment and the AC and DC bus voltages and establishes the health index of multi-energy conversion equipment.

The closer the module’s operating temperature is to the lower limit of the normal operating temperature, the better the module’s operating status is and the greater the adjustable power of the module is. The closer the module temperature is to the upper limit of the normal operating temperature, the worse the module’s operating status is. When it exceeds the upper limit, it means that the module may be abnormal. The module health index based on temperature is as follows:

$$h_{\mathrm{T}}=\left\{\begin{array}{ll}1,& T\le T_{\min }\\ 1-{\displaystyle \frac{T-T_{\min }}{T_{\max }-T_{\min }}},& T_{\min }<T<T_{\max }\\ 0,& T\ge T_{\max }\end{array}\right.$$

(1)

where h_T is the health index of DC–DC module or DC–AC module based on temperature; T is the DC–DC or DC-AC module operating temperature measured; and [T_min, T_max] is the module normal operating temperature range.

The DC and AC voltages output by DC–DC and DC–AC modules should be within a reasonable range. When the output voltage deviates from the normal operating voltage range, it indicates that the operating status of the module may deteriorate. When the output voltage exceeds the module operating voltage limit, it means that the module may be abnormal. The module health index based on the module AC or DC voltage is as follows:

$$h_{\mathrm{V}}=\left\{\begin{array}{ll}0,& V\le V_{\min }\\ 1-{\displaystyle \frac{V_{\mathrm{a}}-V}{V_{\mathrm{a}}-V_{\min }}},& V_{\min }<V<V_{\mathrm{a}}\\ 1,& V_{\mathrm{a}}<V<V_{\mathrm{b}}\\ 1-{\displaystyle \frac{V-V_{\mathrm{b}}}{V_{max}-V_{\mathrm{b}}}},& V_{\mathrm{b}}<V<V_{\max }\\ 0,& V\ge V_{\max }\end{array}\right.$$

(2)

where V is the operating output voltage of the DC–DC or DC–AC module; [V_a, V_b] is the normal range of the module output voltage; and [V_min, V_max] is the allowable operating voltage of the module.

The operating parameters of the DC–DC module are the IGBT temperature and the DC bus output voltage. The health index of a single DC–DC module is calculated as follows:

$$h_{\mathrm{DC}}=\alpha h_{\mathrm{T}}+\beta h_{\mathrm{V}}$$

(3)

where α and β are weight coefficients based on temperature and DC voltage health indicators, respectively.

The main operating parameters of the DC–AC module are IGBT temperature, AC side inductor temperature, and AC output voltage. The health index of a single DC–AC module is calculated as follows:

$$h_{\mathrm{AC}}={\alpha }_1{h}_{\mathrm{T}1}+{\alpha }_2{h}_{\mathrm{T}2}+\beta h_{\mathrm{V}}$$

(4)

The multi-energy conversion equipment consists of multiple DC–DC modules and one DC–AC module. The comprehensive health index of a single multi-energy conversion equipment is calculated as follows:

$$H={\displaystyle \sum _{i=1}^N{\alpha }_i}{h}_{\mathrm{DC},i}+\beta h_{\mathrm{AC}}$$

(5)

where N is the number of DC–DC modules in the multi-energy conversion equipment.

3. Multi-Objective Optimization Model for Power Dispatching Strategy of Highway Power Supply System Considering Equipment Health and New Energy Consumption

The power dispatching of highway power supply systems must not only consider the health status of multi-energy conversion equipment but also take into account the power supply system’s consumption of new energy, make full use of the new energy power generation in the power supply system, improve the energy self-consistency rate of the power supply system, and reduce the cost of purchasing electricity from the external power grid. Therefore, the establishment of the new energy consumption index is as follows [15]:

$$L=1-\left|{\displaystyle \frac{S\cdot P-P_{\mathrm{s}}}{P_{\mathrm{s}}}}\right|$$

(6)

where L is the new energy consumption index; S is the startup status of the new energy unit, 1 indicates startup and 0 indicates shutdown; P is the actual output power of the new energy unit; and P_s is the target power or predicted power of the new energy unit.

3.1. Objective Function

On the basis of considering the conventional control objectives of highway power supply systems, the health status information of multi-energy conversion equipment and the new energy consumption level are integrated to establish a multi-objective optimization model for power dispatching of highway power supply systems. The objective function is as follows:

$$J=\max \left\{a{\displaystyle \sum _{j=1}^C{\displaystyle \sum _{i=1}^M{H}_i^j}+b{\displaystyle \sum _{j=1}^C{\displaystyle \sum _{k=1}^K{S}_k^j{L}_k^j}}}\right\}$$

(7)

where C is the number of optimized cycles; M is the number of multi-energy conversion equipment; and K is the number of new energy unit installed.

3.2. Constraints

1.

The power constraints of each new energy unit are as follows:

$$P_i^{j,\min }\le P_i^j\le P_i^{j,\max }$$

(8)

where, $P_i^{j,\min }$ is the lower limit of the output power of the new energy unit in the jth control period, which is generally determined by the minimum operating power of the new energy unit; and $P_i^{j,\max }$ is the upper limit of the output power of the new energy unit in the jth control period, which can be equivalent to its predicted power.

2.

In order to cope with the power demand of the highway power supply system when the external grid power supply is lost, the SOC constraint of the energy storage system at any time is as follows:

$$SO{C}_{\min }\le SOC\le SO{C}_{\max }$$

(9)

During the entire dispatching cycle, the energy storage system may be in a charging state or a discharging state. The energy storage system SOC calculation formula is as follows:

$$\left\{\begin{array}{l}SO{C}_t=SO{C}_{t-1}+\eta P_{\mathrm{c}}\cdot t\\ SO{C}_t=SO{C}_{t-1}-\eta P_{\mathrm{d}}\cdot t\end{array}\right.$$

(10)

where, SOC_t-1 is the SOC of the energy storage system after the end of the previous dispatching cycle; P_c and P_d are the charging and discharging power of the energy storage battery, respectively; and η is the charging and discharging efficiency of the energy storage power station.

3.

The module temperature is mainly affected by the module power. Simulation analysis shows that the module temperature is linearly related to the power. Therefore, the relationship between the module temperature and power can be approximately expressed as follows:

$$T=T_0+k(P-P_0)$$

(11)

where, k is the proportional coefficient, which can be obtained through experiments; T₀ is the initial temperature of the module; and P₀ is the initial power.

4.

The module output voltage is also related to the power of the module. When the power increases, the module output voltage decreases. When the power decreases, the module output voltage increases. The relationship between voltage and power is approximately expressed as follows:

$$V=V_0+k(P_0-P)$$

(12)

where V₀ is the voltage corresponding to the module power P₀.

5.

The power constraints of the DC–DC module and the DC–AC module. Considering the loss of the DC–DC module, the power of the DC–AC module is as follows:

$$P_{\mathrm{AC}}=P_{\mathrm{DG}}-P_{\mathrm{DL}}-P_{\mathrm{Dl}}$$

(13)

where P_AC is the DC–AC module power from DC side to AC side; P_DG is the total generation power of new energy; P_DL is the total load power of DC side; and P_Dl is the total power loss of DC–DC modules.

4. Case Analysis

In some highway power supply systems, new energy power generation includes wind power and PV power. The energy storage system is mainly used to supply power to the system in an emergency state, and to smooth the fluctuation of new energy power and improve the new energy consumption capacity. The DC load is the hydrogen production load. It starts when the generation of new energy is high, and corresponding production tasks are formulated every day. The PV power installed capacity is 900 kW, which is connected with multi-energy conversion equipment through four DC–DC modules. The wind power installed capacity is 400 kW, which is connected with multi-energy conversion equipment through two DC–DC modules. The level 1 load (non-interruptible load) in the power supply system is 50 kW. The energy storage system must be able to provide continuous power supply for 12 h. The energy storage system is configured to be 1000 kWh. Ignoring the charge and discharge loss of the energy storage system and the change of the module voltage, the minimum SOC constraint of the energy storage system is 60%~90%, the power consumption of hydrogen production is 200 kWh, the initial temperature of the DC–DC module is 90 °C, and the corresponding power is 150 kW. The proportional coefficient k is 0.1333. Only the effect of temperature on the health of multi-energy conversion equipment is considered, assuming that the normal temperature range of each module is [85, 105]. Figure 3 shows the load curve, PV power, and wind power curves of the highway power supply system.

In Figure 3, from 10:00 to 15:00, the PV power output is at a high level, and combined with the wind power output, it can meet the load demand of the highway power supply system. At other times, electricity needs to be imported from the external power grid. Figure 4 compares the PV power and wind power outputs before and after power dispatching and shows the external power grid input power, the energy storage system charging and discharging power, and the hydrogen production power after power dispatching.

It can be seen in Figure 4 that when the PV power and wind power powers are high, in order to reduce the temperatures of the DC–DC modules, the dispatching strategy appropriately reduces the output power of PV power and wind power. From 11:00 to 15:00, during the PV power at high power period, in order to make full use of new energy, the energy storage system charges and the hydrogen production produces hydrogen. From 17:00 to 24:00, the PV power is low, the PV power is not controlled, and in order to reduce the amount of electricity imported from the external power grid, the energy storage system discharges until the SOC reaches the minimum limit of 60%.

Figure 5 compares the temperatures of the PV power and wind power DC–DC modules before and after power dispatching. It can be seen that after the implementation of power dispatching, the temperature of the PV power module is significantly reduced during the peak period and the temperature of the wind power module reduces during the entire dispatching cycle. The maximum temperature of the photovoltaic module drops from 105 °C to 102 °C, and the maximum temperature of the wind power module drops from 103 °C to 88 °C.

Assume that new energy is fully consumed before power dispatching. In order to further verify the effectiveness of the power dispatching strategy in this paper, the health index of multi-energy conversion equipment, the new energy consumption index, and the objective function value of the power dispatching optimization strategy before and after power dispatching are calculated, as shown in Figure 6.

It can be seen that after adopting the power dispatching strategy, the health index of multi-energy conversion equipment has been significantly improved, while the consumption level of new energy is still at a high level. The abandoned PV and wind energy is 530 kWh, and the objective function value is improved throughout the entire power dispatching period.

5. Conclusions

The main novelties of this paper are establishing the health index of multi-energy conversion equipment based on the temperature and output voltage of each module and proposing a power dispatching optimization model based on the health index of multi-energy conversion equipment and the new energy consumption index. The conclusions and suggestions are as follows:

(1)

In multi-energy conversion equipment, the module temperature and power have a certain relationship, and temperature is an important parameter affecting the multi-energy conversion equipment health status;

(2)

The health index based on temperature and output voltage can describe the operating status of multi-energy conversion equipment to a certain extent;

(3)

The power dispatching strategy based on equipment health index and new energy consumption index in this paper can reduce the module temperature during the peak period of new energy power while taking into account the consumption of new energy and can delay the aging process of multi-energy conversion equipment and improve the reliability of the power supply system. In addition, since wind power lasts for a long time and has a large heat accumulation, wind power dispatching should be given priority.