The large-scale electric vehicles connected to the microgrid have brought various challenges to the safe and economic operation of the microgrid. In this paper, a hierarchical microgrid dispatching strategy considering the user-side demand is proposed. According to the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. At the source-load level, priority should be given to the use of the renewable energy output. On the basis of considering the user demand, energy storage, electric vehicles, and dispatchable loads should be utilized to maximize the consumption of the renewable energy and minimize the user’s electricity cost. The source-grid-load level can smooth the tie-line power fluctuation through dispatching of the power grid and diesel generators. Furthermore, the study presents a modified MOEA/D algorithm to solve the hierarchical scheduling problem. In the proposed algorithm, a modified Tchebycheff decomposition method is introduced to obtain uniformly distributed solutions. In addition, initialization and replacement strategies are introduced to enhance the convergence and diversity. A wind-photovoltaic-diesel-storage hybrid power system is considered to verify the performance of the proposed dispatching strategy and the modified algorithm. The obtained results are compared with other dispatching approaches, and the comparisons confirm the effectiveness and scientificity of the proposed strategy and algorithm.

With the shortage of global resources and the aggravation of ecological pollution, countries all over the world begin to take the microgrid as an important supplement for the operation of the main network [

However, reducing the pollution of the environment and improving the economic benefits of the microgrid are two goals that influence and restrict each other. There is no absolute optimal solution to make both optimal at the same time. Therefore, in order to find the optimal solution to the scheduling problem, scholars from various countries have proposed various schemes. Hernandez-Aramburo et al. [

Farzin et al. and Singh and Khan [

EVs have the potential for low-carbon development and sustainable development. EVs with renewables to participate in the scheduling can improve the utilization of energy. Therefore, it is necessary to study how to arrange them to participate in grid dispatching. Based on the randomness of the wind energy and electric vehicles, the probability density function of wind speed and the energy storage and travel characteristic model of EVs are established in [

In addition, in order to fully consider the interests of the user side, some scholars have begun to study the dispatchable load in the microgrid system. The model established in [

At the same time, there are many studies on hierarchical scheduling of microgrids and electric vehicles, and many scholars begin to consider the generation-side and the user-side demand comprehensively [

Based on the above background and research results, this paper proposes a microgrid hierarchical dispatching strategy containing wind turbines (WTs), photovoltaic arrays (PVs), diesel generators (DGs), schedulable loads, and EVs. In the microgrid system, the hierarchical strategy gives full consideration to the influence of the user demands on the economic operation. It uses the modified MOEA/D algorithm to solve the problem. In the proposed strategy, according to the operation characteristics of each dispatch unit, the system is divided into the source-load level and the source-gird-load level. At the source-load level, the dispatchable load and the vehicle-to-grid (V2G) model of electric vehicles are established to maximize the consumption of the renewable energy. Thus, it can achieve peak load cutting of the system load curve and maximize the user’s interests. In dispatching of the source-gird-load level, the output of the DGs and the power of connection lines of the main grid are taken as the decision variables. The objectives of the level are to reduce the overall operation cost and power fluctuation between the microgrid and the main grid. On the premise of meeting the grid energy constraint, the overall optimization of the system side and the user side is realized.

The main result of this paper has the following three points: first, it adopts a hierarchical scheduling strategy. Based on the operation characteristics of each dispatch unit, the strategy divides the microgrid system into two levels: source-load level and source-grid-load level. Furthermore, it proves the scientific nature and effectiveness of the dispatching strategy in the simulation. Second, this paper improves the MOEA/D algorithm for solving the multiobjective problem. A modified Tchebycheff decomposition method is introduced as the decomposition approach in order to obtain uniformly distributed Pareto solutions. And this paper proposes a strategy of initializing the primary population based on the constraint violation value. In addition, a replacement strategy based on the maximum fitness value improvement is also integrated. In the end, it proves that hierarchical scheduling is more capable of coordinating the interests of all stakeholders than conventional scheduling strategies. And through the final comparison experiment, it can be indicated that the participation of schedulable load can effectively improve user satisfaction and reduce load variance.

The rest of this paper is organized as follows. Section

The microgrid system with controllable loads, EVs, WTs, PVs, DGs, and ES is shown in Figure

WT-PV-ES-DG-EV hybrid power system with the dispatchable load.

The purpose of source-load level dispatching is to reduce the peak-valley difference of the total loads. The strategy of the level takes into account the demand response of the user side and guides users to change the way of electricity consumption from the perspective of the electricity market such as electricity price. The consumption of renewable energy can be realized through source-load level dispatching, which makes the load curve closer to the renewable output curve. More importantly, it can well mobilize the enthusiasm of users to participate in grid dispatching, thus improving the reliability and stability of the grid. The dispatching objects of the source-load stage are EVs, ES, and controllable loads.

EVs have dual characteristics of load and onboard energy storage. Reasonable charging and discharging of electric vehicles can effectively achieve peak-load shifting. Since electric vehicles not only need to satisfy users’ travel needs but also need to participate in grid dispatching, this paper divides electric vehicles into two categories: one is subjected to users’ driving habits, and the other is entirely subjected to the microgrid management, which is fully dispatchable [

On the user side, the load can be divided into dispatchable load and undispatchable load [

The multiobjective optimization algorithm obtains the Pareto optimal solution set with minimum user cost and minimum system net load variance. Using the method of fuzzy membership degree [

The net load from the first level is absorbed at the source-grid-load level by the diesel generators and connection lines of the main grid, while the surplus renewable energy is sold to the main grid to gain benefits. The dispatching object of this level is the output of a large power grid and diesel generators. The second dispatching stage’s main purpose is to reduce the overall operation cost of the microgrid, as well as the power fluctuation of the connection lines of the grid. The objective function is to minimize the operation cost and the power fluctuation between the microgrid and the main grid. The ramp-rate constraint is adopted for the DGs. And the output upper and lower limits and power balance constraints of the generators should be satisfied at the same time. The net load of the microgrid obtained in the first step is substituted at this level, and the MOEA/D algorithm is used again to obtain the optimal Pareto frontier distribution of the system. After calculation, the compromise optimal solution is selected to discuss the results.

According to the scheduling policy’s description, scheduling of the microgrid system is divided into two levels: source-load level and source-grid-load level. This section will carry out mathematical modeling to the EVs and the dispatchable load and establish the corresponding objective function at each level of the system. In order to ensure safe and reliable operation of the system, the last part of this section will systematically introduce the constraints of microgrid scheduling.

The probability density functions (PDFs) of the EV’s departure and return journey were obtained by fitting the statistical data in [

Driving distance

The daily energy consumption of the electric vehicle can be obtained from the daily driving distance, so as to obtain the charged state when the battery of the electric vehicle enters the network:

The actual discharge duration

The discharge time

The charging load

Since the charging time of each EV is independent, the corresponding parameters of each EV can be obtained by random tests. According to the obtained charging and discharging initial time and the maximum discharge power by the Monte Carlo method, the charging time

Large-scale EV which are dispatched directly in the microgrid system will produce “dimension disaster.” The area of electric cars is gathered by agents in this paper, equivalent to large aggregate “virtual electric vehicles.” Then, through the system scheduling mechanism, all the aggregates are dispatched to alleviate the pressure on the system side [

In order to meet the travel characteristics and energy storage characteristics of electric vehicles, this paper makes the following assumptions about dispatchable EVs:

The SOC of electric vehicles is 100% when they leave home every morning

Electric vehicles can drive to and from work within an hour starting at 7 o’clock and 17 o’clock and can flexibly participate in grid dispatching during the rest of the time

The minimum SOC limit and rated charge and discharge power of the vehicle battery in the dispatching cycle are set at 20% of the rated value. And the charging and discharge efficiency is set at 0.85. The total driving distance of each EV in one dispatching period is 50 km. Taking into account the V2G behavior of electric vehicles and considering the energy consumption to meet users’ driving needs, the remaining electric quantity

In order to ensure battery’s life and operation safety, its remaining capacity

The charging and discharging power of an electric vehicle have a certain safety constraint and cannot exceed its rated charging and discharging power as follows:

The most important function of electric vehicles is to meet the vehicle owners’ travel needs. Since too many charge and discharge cycles will damage the battery life, this paper sets that only one charge and discharge cycle is completed in each scheduling cycle. The vehicle owners’ travel needs are constrained as follows:

Dispatching of the transferable load is an important measure in optimal dispatching of the microgrid. It can also realize peak-load shifting optimization and the absorption of renewable energy, as well as the optimal economic operation of the microgrid. In this paper, the load of the microgrid system is composed of the undispatchable load and transferable load. The load model is as follows [

Transferable loads can be transferred from one time period to another, but the total power of transferable loads remains the same in one scheduling period

In addition, transferable load shall be transferred within the load power range:

In this paper, we study the economic scheduling problem of the WT-PV-DG-ES microgrid system, which takes into account V2G and the dispatchable load. Economic scheduling, as a complex multiobjective optimization problem, is worth considering not only the operation cost but also the environmental protection, user satisfaction, and load peak-valley difference. On the one hand, dispatching of the source-load level is to improve the satisfaction of the user’s electricity expenditure; on the other hand, it is to realize the absorption of renewable energy and make the load curve fit the renewable energy output as much as possible [

In the first stage of dispatching, the user cost mainly refers to the user electricity charge

In order to alleviate the impact of the wind turbine and photovoltaic grid connection on the power grid and reduce the fluctuation of the load, the objective function was established by taking the minimum load variance

At the second-level dispatching, the scheduling objects are the power grid and DGs. It aims to reduce the comprehensive operation cost on the system side and improve the safety and reliability of the microgrid system, which is reflected by the power fluctuation of connection lines of the main grid. Therefore, the objective function is the minimum integrated operation cost

As the problem of air pollution becomes more and more serious, more and more countries and regions begin to consider environmental protection. However, conventional thermal power units will produce greenhouse gases and air pollutants in the process of power generation, such as

The operating cost

In order to ensure normal and safe operation of the system, the following general constraints are required [

The power balance constraint is expressed in the form of equality constraint, which represents the unit output’s satisfaction with the load in each dispatching period:

Interpretation of the equation:

The ramp-rate constraints are the unit lift (drop) power capacity within the unit scheduling period:

Constraint of the state of charge is given by

Energy storage output limits:

The configuration of spinning reserve capacity has played a positive role in the actual operation of the power system. Due to the uncertainty and intermittency of the renewable output, it is necessary to have a certain reserve capacity to ensure the safety and reliability of the grid connection [

As described in the previous section, the economic scheduling problem of the microgrid is a multiobjective optimization problem. The optimization problem needs to consider complex unit operation and system constraints, some of which are nonlinear and time-coupled equality and inequality constraints. This section will introduce the general framework of the multiobjective optimization problem. In order to solve this problem, this paper adopts the MOEA/D algorithm and improves some shortcomings of the algorithm. The last part gives detailed steps for the modified MOEA/D algorithm.

In general, a multiobjective optimization problem consists of two or more mutually restrictive objectives to be optimized and a series of equality and inequality constraints to be satisfied. Multiobjective optimization problems are usually expressed in the following forms [

In this paper, the

As described in [

The MOEA/D algorithm is designed to optimize

According to Section

However, when confronted with a complex multiobjective optimization problem, the obtained Pareto front is still not uniform [

Based on the modified Tchebycheff decomposition, the particle is in the direction vector corresponding to its subproblem, so the particle distribution is also uniform [

Obviously, in the microgrid hierarchical dispatching model, there are various high-dimensional nonlinear and time-coupled constraints, which make the feasible solution domain relatively narrow and topologically complex [

Since the process of optimization starts from the initialization of particles, the selection of primary particles is crucial. If the primary particles can be uniformly distributed in the whole space and most of them meet the constraints, the offspring can maintain better diversity in the subsequent process. On the contrary, if all constraints are hard constraints at the beginning of algorithm optimization, such as they are forced to return to the constraint range or constraint boundary when particles do not meet the constraints, it will greatly reduce the diversity of optimization results [

Therefore, this paper proposes a strategy of initializing the primary population based on the constraint violation value as outlined in Algorithm

EP: external archive, and EP = Ø

_{i}: summing all the constraint violations of the

^{1},…,^{N}}

Step 1:

Step 2: producing

Step 3: calculating the overall constraint violations _{i},

Step 4: sorting the solutions according to the overall constraint violations in the increasing order and selecting the first

Step 5: ^{N} > ^{1},…,^{N}}, and return to Step 2

Step 6: output EP = {^{1},…,^{N}}

Population replacement strategy is a key part of MOEAs, which has been studied by many people in recent years [

This paper adopts a replacement strategy based on the maximum fitness value improvement as outlined in Algorithm

_{ }

_{ }

_{ }

_{r}: maximum number of the replaced parent solutions by one offspring solutions

_{ }

^{i} = ^{i}) = F(

Randomly select an index

Delete

After obtaining the Pareto optimal solution set, it is necessary to find the best compromise solution in the final nondominant solution set by decision. In this paper, we use the fuzzy decision function to get a relatively satisfactory final solution in the Pareto front. In order to obtain the most precise judgment for the decision maker, the satisfaction degree of each objective function of the

For each nondominant solution, the normalized membership function can be expressed as

The microgrid optimization dispatching problem involves solving control variables and calculating state variables. The control variables of the problem are generator active power outputs. And the state variables are composed of renewable power output and load demand. All the control variables constitute an individual which represents one solution to the microgrid dispatching problem. When using the modified MOEA/D algorithm to solve the multiobjective optimization problem, the detailed steps are shown in Figure

The general main framework of the modified MOEA/D.

According to the relevant historical data and models, the curve of the renewable energy output and the daily load curve were simulated by the Monte Carlo method [

Load curve and renewable energy output curve.

In this paper, day-ahead economic dispatching is adopted for the microgrid system, the total dispatching cycle is 24 hours a day, the unit time interval is 1 h, and the electricity price of the main grid adopts TOU price [

Electric TOU price.

The operating parameters of EVs, ES, DGs, and the power grid are shown in Table

Operating parameters.

Operating parameter | Type | ||||
---|---|---|---|---|---|

EV | ES | DG1 | DG2 | Grid | |

P_min (kw) | 4 | 200 | 600 | 800 | 1500 |

P_max (kw) | −4 | −200 | 0 | 0 | −1000 |

C_min (kw) | — | — | 100 | 150 | — |

C_max (kw) | — | — | −100 | −150 | — |

Discharge efficiency | 0.9 | 0.9 | — | — | — |

Charge efficiency | 0.9 | 0.9 | — | — | — |

S_min (kw) | 0.3 | 0.25 | — | — | — |

S_max (kw) | 0.9 | 0.95 | — | — | — |

Operation and maintenance coefficient (yuan/kw) | — | 0.104 | 0.236 | 0.236 | — |

Pollutant emission factor and disposing cost.

Emission type | ||||
---|---|---|---|---|

Cost (yuan/kg) | 0.21 | 14.824 | 62.964 | |

Emission | DG | 649 | 0.206 | 9.89 |

Coefficient (g/kw·h) | Grid | 889 | 1.8 | 1.6 |

The Monte Carlo algorithm is used to simulate the travel of 400 EVs. The results and the daily load curve of the system are shown in Figure

Daily load curve and output of EVs following users’ driving habits.

As can be seen from the figure, the morning peak load begins to appear in the system at 8:00 a.m., and almost all the EVs finish charging and leave the microgrid system before the morning peak. During the period from 9:00 a.m. to 16:00 p.m., the EVs neither charge nor discharge, that is, they are off the grid. After 17:00 p.m., the EVs return to discharge and participate in system dispatching. And the travel curve is more in line with the user’s travel habits. On the contrary, the charging time of EVs is basically from 23:00 p.m. to 8:00 a.m., when the total load is in a low state, and the electricity price is low. Obviously, charging in this period can save charging cost for users. Electric vehicles return to the system after 17:00 p.m. to support peak load demand by discharging. It can alleviate the power shortage in the evening peak and obtain the discharge subsidy. WT, PV, ES, transferable loads, and EVs participate in level dispatching. The simulated traffic data of 400 electric vehicles and the microgrid load are substituted into the mathematical model established above. The objective functions are user cost and load variance in level dispatching. The Pareto front is obtained by using the modified MOEA/D algorithm, as shown in Figure

Pareto front of the source-load level.

The fuzzy decision method is used to select (13,732 kw, 25,437 yuan) as the final compromise solution for further analysis. As shown in Figure

Load curves after dispatching.

The curve of netLoad represents the net load of the user side in Figure

Power output of each unit at the source-load level.

In this stage, the modified MOEA/D algorithm is still used to solve the problem. The objective functions are the minimization of the total operating cost of the system and the power fluctuation of the tie lines. The Pareto front obtained by MOEA/D has been illustrated in Figure

Pareto front of the source-grid-load level.

The power outputs of the connection lines and two DGs are shown in Figure

Power output of each unit at the source-grid-load level.

In order to prove the effectiveness of the improved algorithm, this part uses the initial MOEA/D algorithm to solve the hierarchical scheduling problem. We take source-load level scheduling as an example and compare the Pareto solution sets that the two algorithms obtained as follows.

As can be seen in Figure

Algorithmic comparison.

Comparison of convergence. (a) The curve of the value of the user-side cost. (b) The curve of the value of load variances.

It can be seen from the figure that although both algorithms eventually converge to the approximate position in the same objective functions, the improved MOEA/D algorithm converges faster than the original algorithm. More importantly, the improved MOEA/D algorithm can eventually reach a smaller value of objective functions. It indicates that the modified MOEA/D algorithm is more effective in this problem.

According to Qiu et al. [

The curve of netLoad and renewables in the strategy without considering the dispatchable load.

In order to better illustrate the satisfaction of the user side after joining the demand-side response, this section divides user satisfaction into two aspects of comfort and economy:

Comparison of simulation results of dispatching strategies.

Dispatching strategy | Load variance | User-side cost | |||
---|---|---|---|---|---|

Considering dispatchable load | 13732 | 25437 | 0.89 | 1.16 | 1.04 |

Nonconsidering dispatchable load | 19969 | 27957 | 1 | 1 | 1 |

From Table

It can be indicated that the proposed strategy can reduce the impact on users’ comfort as much as possible and maximize the economic benefits of users. Therefore, the strategy with dispatchable loads can maximize users’ satisfaction, and the transferable loads can greatly optimize the scheduling results in the dispatching process.

In the microgrid system mentioned above, the dispatching strategy without considering hierarchy is used to solve the optimization problem. For a comprehensive comparison, the nonhierarchical scheduling strategy is divided into nonhierarchical scheduling 1 and nonhierarchical scheduling 2. The objective functions of nonhierarchical scheduling 1 are the lowest user cost and operating cost of the microgrid, while the objective functions of nonhierarchical scheduling 2 are the minimum load variance and the minimum power fluctuation of the connection lines. These two scheduling problems are still multiobjective optimization problems, so the modified MOEA/D algorithm is adjustable to solve them. The operation results of the three scheduling policies are compared, as shown in Table

Comparison of operation results of three dispatching models.

Dispatching strategy | Load variance | User-side cost | Operating cost of the microgrid | Tie-line power fluctuation |
---|---|---|---|---|

Hierarchical strategy | 13732 | 25437 | 1675 | 5073 |

Nonhierarchical strategy 1 | 30622 | 27980 | 2345 | 9131 |

Nonhierarchical strategy 2 | 16616 | 43752 | 3786 | 6696 |

As can be seen from Table

In this paper, a microgrid hierarchical dispatching strategy is proposed. The source-load level strategy considers user-side fees, and load variance, the operation cost of the microgrid, and the power fluctuation of the connection lines are minimized at the source-grid-load level of the system. The modified MOEA/D algorithm is used to solve the optimal dispatching problem. Through analysis and comparison with the result of the final scheduling, the following conclusions can be drawn:

The participation of schedulable load and EVs in microgrid dispatching has a significant effect on peak load clipping and valley filling of the load curve. Moreover, it also improves the utilization rate of renewable generation and the microgrid’s income to a certain extent.

By comparing the modified MOEA/D with the original algorithm, it can be found that the replacement strategy based on the maximum value and the initialization strategy based on the constraint violations can effectively improve the convergence speed of the algorithm.

The hierarchical scheduling strategy can fully consider the operation characteristics of the generation units at each level of scheduling. It can not only improve the overall satisfaction of users but also reduce the economic operation cost of the microgrid. Thus, the proposed strategy can realize the win-win situation of user satisfaction, good economy, and high system security.

Expectation of

Variance of

Expectation of

Standard deviation of

Power consumption of 100 kilometers

Total capacity of the EV battery

Upper (lower) limit of the SOC

Charging and discharging efficiency

Scheduling interval

Average power consumption per unit distance

Upper (lower) limits of battery power

Rated charging (discharging) power of an EV

Total load power of the system

Transferable load power

Discharge subsidy

Number of scheduling time periods

Uniform discharge subsidy of the microgrid system to the electric vehicle

Running maintenance coefficient

Lift (drop) ramp rate of generator

Upper (lower) limits of the SOC

Upper (lower) output limits of ES

Decision vector

Direction vector

Number of inequality constraints

Number of equality constraints

Objective functions

Weight vector

A constant close to zero

Population size

External archive

Offspring particle

Reference point

Number of objective functions

Number of nondominant solutions

Fuel coefficients of the diesel engine

Return trip time of the last trip of electric vehicles

Driving distance

SOC of the EV battery

Discharge power of the EV

Beginning (end) time of EV discharge

Duration of discharging

Charging load of the EV

Charging (discharging) time of the

Daily charging and discharging load of each EV at time

Remaining electric quantity of an electric vehicle in time

Energy consumed by the electric vehicle in the process of driving in time period

Driving distance

Upper (lower) limits of the power consumption of the transferable load in time

User electricity charge

Charging (discharging) power of the EV in time period

TOU price

Load variance

Output of the ES

Predicted renewable energy output power

Average load

Integrated operation cost of DGs and the power grid

Power fluctuation of the tie lines

Comprehensive operation cost of DGs

Comprehensive operation cost of the power grid

Operating and maintenance cost of DGs

Fuel cost of DGs

Environmental governance cost of DGs

Treatment of category

Emission of pollutants generated by DGs

Electricity transaction cost

Environmental governance cost

Emission of pollutants generated by the grid

Tie-line power of the main network

Output of the DGs

Power of the WT and PV predicted

Output state of ES

Spinning reserve capacity requirement of the system

Ideal value of the

Summing all the constraint violations of the

Value of the improvement of the offspring particle

Subproblem of the maximum fitness

Indices of the neighbor subproblems of the

Objective function of the

Maximum number of the replaced parent solutions by one offspring solution

Satisfaction degree of the

Degree of comfort satisfaction of users

Degree of economy satisfaction of users

Degree of comprehensive satisfaction of users

Sum of the absolute value of the electric quantity change in each period

Total load value of

Total load value of

Economic/environmental dispatching

Nondominant sorting genetic algorithm with elite strategy

Artificial shark optimization

Microgrid

Dynamic economic emission dispatch

Multiobjective evolutionary algorithm based on decomposition

Localized penalty-based boundary intersection

Hybrid renewable energy system

Electric vehicle

Distributed energy resources

Energy storages

Wind turbine

Photovoltaic arrays

Diesel generator

Vehicle-to-grid

State of charge

Time of use

Probability density function.

The data used to support the findings of this study are available from the corresponding author upon request.

The authors declare no conflicts of interest.

X.W., H.L., and X.D. conceived and designed the study; X.W. and H.L. performed the study; X.W., J.P., and Y.W. reviewed and edited the manuscript; and X.W. and H.L. wrote the paper. All authors read and agreed to the published version of the manuscript.

This work was supported in part by the National Natural Science Foundation of China (NSFC: 61563034), the International S&T Cooperation Program of China (ISTCP: 2014DFG72240), and Higher School Science and Technology Floor Plan in Jiangxi Province (KJLD14006).