A multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles

Abstract

A multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles, comprising: reading EV parameters and grid electricity prices; solving the optimal charge and discharge power of the EV through CPLEX software and passing it to the NEMG layer; reading NEMG system parameters, setting particle swarm parameters, and initializing the particle swarm; NEMG superimposes the optimal charge and discharge power of the EV with the basic daily load, PV, and WT output to obtain the net load of NEMG; judging the optimization conditions, updating the new particle positions, calculating and updating the function values ​​corresponding to the particles, and obtaining the Pareto optimal solution; judging whether the convergence conditions are met, and if so, selecting the corresponding final compromise solution. The multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles of the present invention can better consider the interests of various stakeholders of different stakeholders in terms of hierarchical scheduling, achieve a win-win situation in terms of operating costs, environmental benefits, etc., and can also more reasonably schedule the resources within the system to maximize the social benefits of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of microgrid optimization, and in particular to a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles. Background Art

[0002] The unified management of electric vehicle (EV) power batteries can effectively alleviate the negative impact of large-scale EV access to the grid, especially the access to new energy microgrids (NEMG), which can not only consume renewable energy locally and achieve true low carbon, but also achieve multiple benefits such as peak load shaving and valley filling and providing auxiliary services through V2G technology. Microgrids are flexible in operation and can integrate multiple resources together. EV access to NEMG is one of the main development trends in the future.

[0003] At present, there have been a lot of studies on the scheduling problem after EV is connected to NEMG, but the scheduling model established in the relevant research does not take into account the interests of EV, and EV and NEMG belong to two different interest entities. It is necessary to consider and guarantee the interests of EV when optimizing the scheduling of NEMG containing EV. The scheduling of NEMG involves indicators such as economy and environmental protection, and has the characteristics of high dimension, nonlinearity, multiple constraints and multiple objectives. In order to benefit the interests of multiple parties, the multi-objective optimization method is applied to solve the multi-objective optimization problem, which can provide decision makers with useful information on the relationship between various indicators in NEMG scheduling, and finally obtain a compromise solution. Summary of the invention

[0004] Aiming at the problem that the optimal social welfare of the system cannot be achieved and the fair and reasonable distribution of profits cannot be guaranteed in the economic operation of electric vehicles EV connected to the new energy microgrid NEMG, the present invention provides a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles, and a multi-objective hierarchical scheduling of NEMG taking into account the orderly charging and discharging behavior of EVs and the comprehensive satisfaction of car owners. Compared with a non-hierarchical architecture, a hierarchical architecture of a microgrid containing EVs can better achieve mutual benefit and win-win results for EVs and NEMGs, and maximize system benefits.

[0005] The technical solution adopted by the present invention is:

[0006] A multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles comprises the following steps:

[0007] Step 1: Read the parameters of the electric vehicle EV and the power grid price;

[0008] Step 2: The EV layer calls the CPLEX software algorithm to obtain the objective function maxF1;

[0009] Step 3: Based on the owner satisfaction and with the goal of maximizing it, the CPLEX software is called to obtain the optimal charging and discharging power of the EV layer and transferred to the NEMG layer;

[0010] Step 4: The NEMG layer uses the PSO-TCS algorithm to obtain the objective function minF 11 and minF 22 ;

[0011] Step 5: Read the NEMG system parameters, set the particle swarm parameters, and initialize the particle swarm. Set k = 1 and start the iteration;

[0012] Step 6: The NEMG system superimposes the optimal charging and discharging power of EV with the basic daily load, PV, and WT output to obtain the net load of NEMG;

[0013] Step 7: Determine the optimization conditions, optimize the NEMG layer, and update the particle positions and corresponding function values;

[0014] Step 8: Use the classic crowding distance sorting strategy to obtain the optimal Pareto solution for this iteration;

[0015] Step 9: Determine the total number of generations Gen and Gen-max. If Gen is greater than or equal to Gen-max, the iteration stops; otherwise, k=k+1, and return to step 7 for the next iteration;

[0016] Step 10: Use the MPSO-TCS algorithm to solve the Pareto solution and Pareto frontier;

[0017] Step 11: Use the fuzzy membership function to select the corresponding compromise solution from the Pareto frontier as the final solution and output the result.

[0018] Through the above steps, multi-objective hierarchical optimization scheduling of new energy microgrids containing electric vehicles can be achieved.

[0019] In step 1, the parameters of the electric vehicle EV are read. The parameters of the electric vehicle EV include: the time when the EV is connected to the power grid, the time when it leaves the power grid, the initial state of charge, the expected state of charge and other basic information, as well as the parameters shown in Table 2 below:

[0020] Table 2 Parameters of electric vehicles EV

[0021]

[0022] E is the rated capacity of the EV battery; E change is the maximum charge and discharge capacity; C change The cost of replacing the battery; SOC max , SOC min P is the upper and lower limits of EV charge state; charmax , P dismax are the maximum power of EV charging and discharging respectively; η char, η dis They are EV charging and discharging efficiency respectively.

[0023] In step 2, the EV layer objective function is:

[0024]

[0025] Where: θ i , δ i are the cost satisfaction and travel satisfaction of EV owners respectively; α1 and α2 are θ i , δ i The weight coefficient of . N represents the total number of EV vehicles.

[0026] (1) Satisfaction with the cost of the i-th EV:

[0027]

[0028] Where: are the maximum and minimum expenses that EV owners can afford. i It can be expressed as follows:

[0029] C i =C i,price +C i,bat

[0030] C i,price , C i,bat They are respectively represented by charging and discharging costs and potential battery loss costs.

[0031]

[0032]

[0033] In the formula, are charge and discharge state variables, 1 indicates charge and discharge, and 0 indicates no charge or discharge; C change Cost of replacing the battery; E change is the maximum charge and discharge capacity. T and t represent the total time period and a certain time period respectively; P i,t represents the charging or discharging power of the i-th EV at time t. They respectively represent the charging and discharging electricity prices for each time period released by the NEMG control center.

[0034] (2) Travel satisfaction of the i-th EV:

[0035]

[0036] Where: are the EV power when travel satisfaction is maximum and minimum, respectively. When the owner charges in a disorderly manner according to travel convenience, his travel satisfaction is maximum, that is, δ i =1, when the owner charges and discharges the car without considering the convenience of travel in order to minimize his own expenses, his travel satisfaction is the lowest, that is, δ i =0.

[0037] The corresponding constraints are:

[0038] ①. EV charging and discharging power constraints:

[0039]

[0040] Where: P is the maximum power of EV charging and discharging respectively. i,t represents the charging or discharging power of the i-th EV in period t.

[0041] ②. EV charging and discharging state variable constraints:

[0042]

[0043] The table shows the charging and discharging state variables of the i-th EV during period t.

[0044] ③. EV state of charge constraints:

[0045] SOC min ≤SOC i,t ≤SOC max ;

[0046]

[0047] Where: SOC max , SOC min The upper and lower limits of the EV state of charge; SOC i,t is the state of charge of the i-th EV at time period t. E is the rated capacity of the EV battery; η char , η dis They are EV charging and discharging efficiency. SOC i,t+1 , SOC i,t They represent the charge state of vehicle i at time period t+1 and time period t respectively.

[0048] In step 3, based on the goal of maximizing the comprehensive satisfaction of the car owner, the CPLEX software is called to obtain the optimal charging and discharging power of the EV layer, and then transferred to the NEMG layer;

[0049] Overall satisfaction of car owners:

[0050]

[0051] θ i , δ i They are respectively represented as the cost satisfaction and travel satisfaction of the i-th EV; α1 and α2 are θ i ,δ i The weight coefficient of .

[0052] The optimal charging and discharging power of the EV layer refers to the EV charging and discharging plan arranged by the owner according to the driving habits and cost expenditure. In order to take into account both cost expenditure and travel convenience, the cost satisfaction of each EV owner θ is considered. i and travel satisfaction δ i , with maximizing the comprehensive satisfaction of all car owners as the objective function, it optimizes the scheduling of EV rational charging and discharging, and the power of electricity, effectively playing the effect of peak shaving and valley filling.

[0053] In step 4, the objective function of the NEMG layer is:

[0054] (1) NEMG has the lowest comprehensive cost:

[0055] min F 21 =C op +C en ;

[0056] Where: C op , C en are the operating cost and environmental cost of NEMG respectively.

[0057] NEMG operating costs C op It can be expressed as follows:

[0058]

[0059] Where: J is the total number of controllable distributed power sources, j=1, 2 represents DE, MT respectively; It is the output of distributed power generation; are the fuel cost and operation and maintenance cost of the jth type of distributed power generation, and is the output of the distributed power generation function.

[0060] OM j It can be expressed as follows:

[0061]

[0062] K om,j is the operation and maintenance factor.

[0063] Energy interaction cost between NEMG and main network C grid It can be expressed as:

[0064]

[0065] In the formula, Indicates the status of buying and selling electricity. They are the buying and selling electricity prices for each time period collected by the NEMG control center.

[0066] SB charging and discharging cost C SB_price It can be expressed as:

[0067]

[0068] P SB,t is the charging and discharging power of the battery in period t; They respectively represent the charging and discharging status of the battery in time period t.

[0069] SB charge and discharge conversion loss cost C SB_bat It can be expressed as:

[0070] C SB_bat =nC loss ;

[0071] n is the number of SB charge and discharge conversions; C loss It is the conversion cost of each charge and discharge of SB.

[0072] SB operation and maintenance cost C SB_om It can be expressed as:

[0073]

[0074] K om,SB is the operation and maintenance coefficient of SB.

[0075] Environmental cost of NEMG en It can be expressed as follows:

[0076]

[0077] Where: K is the total number of pollutant types; C k is the unit cost of treating the kth type of pollutant; u j,k 、u grid,k are the unit emissions of the kth type of pollutants generated by the operation of the jth type of distributed power source and the main grid tie line. T represents the total time period; J represents the total category of distributed power source; represents the power generation of the jth distributed generation at time t; P grid,t It is the interaction power between NEMG and the main grid. A positive value indicates that NEMG purchases electricity from the main grid, and a negative value indicates that NEMG sells electricity to the main grid.

[0078] (2) The power fluctuation of NEMG interaction with the main network is minimal:

[0079]

[0080]

[0081] Where P grid,av is the average interaction power in the scheduling period.

[0082] The corresponding constraints are:

[0083] ①. Power balance constraints:

[0084]

[0085]

[0086] Where: P net_load,t is the net load in time period t; P load,t is the basic load; P WT,t , P PV,t They respectively represent the power generation of wind turbines and photovoltaic modules in period t.

[0087] ②Distributed power output constraints:

[0088]

[0089] Where: They are The upper and lower limits of .

[0090] ③. Distributed power supply climbing constraints:

[0091]

[0092] Where: r j is the maximum ramp rate of the jth distributed generation. It represents the power generation of the jth distributed generation in the period t-1.

[0093] ④. SB charge and discharge power constraints:

[0094]

[0095] Where: are the maximum values ​​of SB charge and discharge respectively.

[0096] ⑤. Constraints on NEMG trading state variables:

[0097]

[0098] They represent the state variables of NEMG buying and selling electricity to the main network in period t.

[0099] ⑥. SB charge and discharge state variable constraints:

[0100]

[0101] They represent the charging and discharging state variables of the battery in period t respectively.

[0102] 7. SB state of charge constraint:

[0103] SOC SB,min ≤SOC SB,t ≤SOC SB,max

[0104]

[0105] Where: SOC SB,max , SOC SB,min SOC is the upper and lower limits of SB state of charge; SB,t , SOC SB,t+1 are the charge states of SB in period t and period t+1 respectively; E SB is the rated capacity of SB; η SB,char , η SB,dis are SB charge and discharge efficiency respectively; u is SB self-discharge rate.

[0106] NEMG system: The grid-connected NEMG system includes renewable energy generation such as wind turbines (WT) and photovoltaic modules (PV), distributed power sources such as diesel engines (DE) and micro turbines (MT), batteries (SB), basic loads (Load), EVs connected to the grid through smart charging piles, and necessary communication infrastructure.

[0107] The NEMG layer is a layer of architecture of the NEMG system multi-objective hierarchical scheduling optimization mathematical model that takes into account the orderly charging and discharging behavior of EVs and the comprehensive satisfaction of car owners. EVs and NEMGs are different stakeholders. The NEMG layer is based on the optimized EV charging and discharging plan, takes the minimum system comprehensive cost and the minimum power fluctuation in interaction with the main grid as the objective function, and uses the MPSO-TCS algorithm to solve the high-dimensional, nonlinear multi-objective optimization model to optimize and adjust the output of the controllable distributed power sources inside the NEMG system.

[0108] In step 6, the NEMG system superimposes the optimal charging and discharging power of the EV with the basic daily load, PV, and WT output to obtain the net load of the NEMG:

[0109]

[0110] Where: P net_load,t is the net load in time period t; P load,t is the basic load; P WT,t , P PV,t They respectively represent the power generation of wind turbines and photovoltaic modules in period t.

[0111] In step 7, the optimization formula of the NEMG layer includes the traditional formula and the absorption formula:

[0112] (1) Traditional method:

[0113]

[0114]

[0115] Where: l, r1, r2∈[0,1] are random numbers; subscripts i and d represent the dth dimension of the ith particle (feasible solution); superscripts k and k+1 represent the kth and k+1th generations of optimization iteration; v and x are the velocity and position of the particle, respectively; pbest is the individual optimal; c1 and c2 are learning factors; and r is the radius of the black hole.

[0116] They represent the d-th dimension speed of the i-th particle (feasible solution) of the k+1-th and k-th generations of optimization iterations respectively;

[0117] They represent the d-th dimension position of the i-th particle (feasible solution) in the k+1-th and k-th generations of optimization iterations respectively;

[0118] They represent the optimal individual of the d-th dimension of the i-th particle (feasible solution) of the k-th generation of optimization iteration and the optimal individual among all particles in the d-th dimension respectively; w is the inertia weight.

[0119] (2) Absorption formula:

[0120]

[0121]

[0122] Where: x max 、x min are the upper and lower bounds of the particle respectively; k and Gen are the current generation and the total generation respectively; r is the radius of the black hole, that is, the region where the approximate true solution is located; l′, r3, r4∈[0,1] are random numbers; a is the random number credibility factor, and a∈[0,1].

[0123] In step 8: a classic crowding distance sorting strategy is used to obtain a Pareto solution. The classic crowding distance sorting strategy is described in the literature [DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II [J]. IEEE Transaction on Evolutionary Computation, 2002, 6 (2): 182-197.].

[0124] The Pareto solution refers to the set of all solutions in the feasible domain obtained by using the MPSO-TCS algorithm with the goal of minimizing the comprehensive cost of NEMG and minimizing the interaction power between NEMG and the main grid.

[0125] In step 9: determine the total number of generations Gen and Gen-max. If Gen is greater than or equal to Gen-max, the iteration stops; otherwise, k=k+1, and return to step 7 for the next iteration.

[0126] In the step 10: the Pareto solution and the Pareto frontier are solved by the MPSO-TCS algorithm. Specifically, the TCS-based particle swarm algorithm is extended to MPSO, and the classic crowding distance sorting strategy is used to obtain the Pareto solution, that is, the MPSO-TCS algorithm is obtained.

[0127] The Pareto frontier is the set of target values ​​corresponding to the Pareto solution set.

[0128] In step 11: the fuzzy membership function is used to select the corresponding compromise solution from the Pareto frontier as the final solution, that is, P in the period 1 to T grid,t , P SB,t , P DE,t and P MT,t The fuzzy membership function is described in the literature: [XU Yan, ZHANG Rui, DONG Zhaoyang, et al. Optimal placement of static compensators for multi-objective voltage stability enhancement of power systems [J]. IET Generation Transmission & Distribution, 2015, 9 (15): 2144-2151.].

[0129] The present invention provides a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles, and the beneficial effects are as follows:

[0130] 1) The dispatching method of the present invention connects electric vehicles to NEMG, which can consume renewable energy locally and achieve low carbon in a true sense; the unified management of EV power batteries not only facilitates the management of EVs, but also effectively alleviates the negative impact of large-scale EV access to the power grid; the proposed comprehensive satisfaction model makes EV charging and discharging more reasonable and can more effectively achieve the effect of "peak shaving and valley filling"; peak shaving and valley filling are achieved through V2G technology, which not only smoothes system energy fluctuations and reduces energy losses, but also provides other auxiliary services to achieve multiple benefits, ensuring the needs of EV users and the interests of participating in V2G.

[0131] 2) The multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles proposed in the present invention takes into account the orderly charging and discharging behavior of EVs and the comprehensive satisfaction of car owners for NEMG multi-objective hierarchical scheduling. Compared with the non-hierarchical architecture, hierarchical optimization can better achieve a win-win situation between EV and NEMG in terms of operating costs, environmental benefits, etc., and maximize system benefits.

[0132] 3) The present invention takes into account the orderly charging and discharging behavior of EVs and the comprehensive satisfaction of car owners to ensure the interests of EVs, adopts a multi-objective particle swarm optimization (MPSO-TCS) algorithm based on a three-black hole capture strategy to improve the authenticity of the solution, further optimizes and adjusts the operation scheduling of NEMGs containing EVs, achieves mutual benefit and win-win results between EVs and NEMGs, and maximizes the benefits of the system.

[0133] 4) The MPSO-TCS algorithm proposed in the present invention has good convergence for solving high-dimensional, nonlinear multi-objective optimization models, can approach the real Pareto frontier and has good distribution, making the solution more realistic, further optimizing the operation scheduling of the system, improving the system benefits, and maximizing the overall benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0134] Figure 1 This is the structural framework diagram of NEMG containing EV.

[0135] Figure 2(a) shows the convergence curves of the MPSO-TCS algorithm and the MPSO-RBH algorithm (ZDT3 function).

[0136] Figure 2(b) shows the convergence curves of the MPSO-TCS algorithm and the MPSO-RBH algorithm (ZDT4 function).

[0137] Figure 3 Flowchart for model hierarchical solution.

[0138] Figure 4 It is the fuel cost curve of each distributed power source.

[0139] Figure 5 It is the WT and PV output and daily load curve.

[0140] Figure 6 This is a time-of-use electricity price chart.

[0141] Figure 7 Figure 2 is the load curve diagram under different strategies.

[0142] Figure 8 Pareto front plot for multi-objective optimization.

[0143] Fig. 9 This is the output curve of each unit in NEMG.

[0144] Fig.10 This is the Pareto front diagram without stratification. DETAILED DESCRIPTION

[0145] Principle analysis:

[0146] Unified management of electric vehicle (EV) power batteries can effectively alleviate the negative impact of large-scale EV access to the grid. Connecting EV to the new energy microgrid NEMG can not only consume renewable energy locally and achieve true low carbon, but also achieve multiple benefits such as peak load shaving and valley filling and providing auxiliary services through V2G technology. Microgrids are flexible in operation and can integrate multiple resources together. The combination of EV and NEMG is one of the main development trends in the future.

[0147] First, the NEMG model containing EV is layered. Among them, the EV layer considers the orderly charging and discharging behavior and the comprehensive satisfaction of the car owner, combines CPLEX software to solve the mixed integer linear programming problem, and passes the optimized charging and discharging plan to the NEMG layer. Then in the NEMG layer, the particle swarm algorithm based on TCS is expanded to MPSO, and the classic crowding distance sorting strategy is used to obtain the Pareto solution, that is, the MPSO-TCS algorithm is obtained, and then the MPSO-TCS algorithm is used to iteratively solve the nonlinear, multi-dimensional, multi-constrained and multi-objective optimization problems of the NEMG layer.

[0148] The present invention will be further described in detail below in conjunction with embodiments and drawings, but the embodiments of the present invention are not limited thereto.

[0149] Figure 1The following is a structural framework diagram of NEMG with EV. The NEMG structure with EV is mainly divided into two layers: the first layer is the NEMG layer, which is mainly composed of renewable energy sources such as wind turbines and photovoltaics, distributed power sources such as diesel engines and gas turbines, loads, batteries, and loads, which can interact with the main grid and batteries. The second layer is the EV layer, which is mainly composed of electric vehicles EV and charging piles. EV is connected to the grid through charging piles.

[0150] The present invention integrates electric vehicles into new energy microgrids, manages all EV power batteries in a unified manner and participates in the optimization and scheduling of microgrid operations. The EV layer fully considers the benefits of EV participation in V2G, takes into account the orderly charging and discharging behavior of EVs and the comprehensive satisfaction of car owners, and aims to maximize the comprehensive satisfaction of car owners' expenses and travel. The NEMG layer adjusts the output of controllable distributed power sources within the microgrid based on the EV charging and discharging plan to achieve the goal of minimizing the overall system cost and interactive power fluctuations. CPLEX software and the multi-objective particle swarm optimization (MPSO-TCS) algorithm based on the three-black hole capture strategy are used to solve the optimization problems of the EV layer and the NEMG layer respectively, and the optimal charging and discharging power of the EV layer and the minimum cost and minimum power fluctuation of the NEMG layer are obtained. Then, from the Pareto frontier of the MPSO-TCS algorithm, the corresponding compromise solution is selected from the fuzzy membership function as the final solution, so that the final solution is optimized on the original basis to maximize the benefits of the system.

[0151] The specific implementation plan is as follows:

[0152] 1: The EV layer takes maximizing the comprehensive satisfaction of all car owners as its objective function, which can be expressed as follows:

[0153]

[0154] Where: θ i , δ i are the cost satisfaction and travel satisfaction of EV owners respectively; α1 and α2 are θ i , δ i The weight coefficient of .

[0155] (1) Satisfaction with the cost of the i-th EV:

[0156]

[0157] Where: They are respectively the maximum and minimum expenses that EV owners can afford.

[0158] Expenses C. i It can be expressed as follows:

[0159] C i =C i,price +Ci,bat

[0160]

[0161]

[0162] In the formula, are charge and discharge state variables, 1 indicates charge and discharge, and 0 indicates no charge or discharge; C change Cost of replacing the battery; E change The maximum charge and discharge capacity.

[0163] (2) Travel satisfaction of the i-th EV:

[0164]

[0165] Where: are the EV power when travel satisfaction is maximum and minimum, respectively. When the owner charges in a disorderly manner according to travel convenience, his travel satisfaction is maximum, that is, δ i =1, when the owner charges and discharges the car without considering the convenience of travel in order to minimize his own expenses, his travel satisfaction is the lowest, that is, δ i =0.

[0166] 2: The corresponding constraints of the EV layer are:

[0167] 1) EV charging and discharging power constraints:

[0168]

[0169] Where: are the maximum power for EV charging and discharging respectively.

[0170] 2) EV charging and discharging state variable constraints:

[0171]

[0172] 3) EV state of charge constraints:

[0173] SOC min ≤SOC i,t ≤SOC max

[0174]

[0175] Where: SOC max , SOC min are the upper and lower limits of the EV state of charge; E is the rated capacity of the EV battery; η char , η dis They are EV charging and discharging efficiency respectively.

[0176] 3: The objective function of the NEMG layer is to minimize the comprehensive cost of NEMG and minimize the power fluctuation of interaction with the main network, which can be expressed as follows:

[0177] (1) NEMG has the lowest comprehensive cost:

[0178] f 21 =C op +C en

[0179] Where: C op , C en are the operating cost and environmental cost of NEMG respectively They are the electricity price of the microgrid purchasing electricity from the grid and the electricity price of the microgrid selling electricity to the grid at time t;

[0180] The operating cost of NEMG can be expressed as follows:

[0181]

[0182] Where: J is the total number of controllable distributed power sources, j=1, 2 represents DE, MT respectively; are the fuel cost and operation and maintenance cost of the jth type of distributed power generation, and is the output of the distributed power generation Function of

[0183] OM j It can be expressed as follows:

[0184]

[0185] K om,j is the operation and maintenance factor.

[0186] Energy interaction cost between NEMG and the main network C grid It can be expressed as:

[0187]

[0188] In the formula, I chargrid,t ,I disgrid,t Indicates the status of buying and selling electricity.

[0189] SB charging and discharging cost C SB_price It can be expressed as:

[0190]

[0191] SB charge and discharge conversion loss cost C SB_bat It can be expressed as:

[0192] C SB_bat =nC loss

[0193] SB operation and maintenance cost C SB_om The points can be expressed as:

[0194]

[0195] The environmental cost of NEMG can be expressed as follows:

[0196]

[0197] Where: K is the total number of pollutant types; C k is the unit cost of treating the kth type of pollutant; u j,k 、u grid,k They are the unit emissions of the kth type of pollutants generated by the operation of the jth type of distributed power source and the main grid interconnection line, respectively.

[0198] (2) The power fluctuation of NEMG interaction with the main network is minimal:

[0199]

[0200]

[0201] Where P grid,av is the average interaction power in the scheduling period.

[0202] 4. The corresponding constraints at the lower level are:

[0203] 1) Power balance constraints:

[0204]

[0205]

[0206] Where: P net_load,t is the net load in time period t.

[0207] 2) Distributed power output constraints:

[0208]

[0209] Where: They are The upper and lower limits of .

[0210] 3) Distributed power supply ramp constraints:

[0211]

[0212] Where: r j is the maximum ramp rate of the jth distributed generation.

[0213] 4) SB charge and discharge power constraints:

[0214]

[0215] Where: are the maximum values ​​of SB charge and discharge respectively.

[0216] 5) Constraints on NEMG trading state variables:

[0217]

[0218] 6) SB charge and discharge state variable constraints:

[0219]

[0220] 7) SB state of charge constraints:

[0221] SOC SB,min ≤SOC SB,t ≤SOC SB,max

[0222]

[0223] Where: SOC SB,max , SOC SB,min SOC is the upper and lower limits of SB state of charge; SB,t is the charge state of SB in time period t; E SB is the rated capacity of SB; η SB,char , η SB,dis are SB charge and discharge efficiency respectively; u is SB self-discharge rate.

[0224] 5: The optimization formula of the NEMG layer is:

[0225] The optimization methods are divided into traditional formula and absorption formula:

[0226] (1) Traditional method:

[0227]

[0228]

[0229] Where: l, r1, r2∈[0,1] are random numbers; subscripts i and d represent the dth dimension of the ith particle (feasible solution); superscripts k and k+1 represent the kth and k+1th generations of optimization iteration; v and x are the velocity and position of the particle, respectively; pbest is the individual optimal; c1 and c2 are learning factors; and r is the radius of the black hole.

[0230] (2) Absorption formula:

[0231]

[0232]

[0233] Where: x max 、x min are the upper and lower bounds of the particle respectively; k and Gen are the current generation and the total generation respectively; r3, r4∈[0,1] are random numbers.

[0234] In order to illustrate the economic benefits of adopting hierarchical optimization based on the interests of both EV and NEMG, the present invention adopts three strategies for comparison: Strategy 1: Coordinated optimization of EV according to the disordered charging of the owner's wishes; Strategy 2: Coordinated optimization of EV according to the orderly charging and discharging with the least cost expenditure. Strategy 3: Coordinated optimization of EV considering the orderly charging and discharging behavior of EV and the comprehensive satisfaction of the owner. The quantitative index values ​​of the three strategies are compared as shown in Table 1 below:

[0235] Table 1 Comparison of indicators under different strategies

[0236]

[0237] As shown in Table 1, unlike the random and disorderly charging of Strategy 1 and the pursuit of the lowest cost expenditure of Strategy 2, the strategy of the present invention has not only achieved the effect of significantly reducing the load peak-to-valley difference and load variance, but also effectively avoided the phenomenon of excessive cost expenditure. It can be seen that the strategy proposed by the present invention effectively balances travel satisfaction and cost satisfaction, achieves the maximum comprehensive satisfaction, and further illustrates the effectiveness of this model.

[0238] Figure 2(a) and Figure 2(b) are the convergence curves of the MPSO-TCS algorithm and the MPSO-RBH algorithm. It can be clearly seen that the MPSO-TCS algorithm is closer to the true Pareto frontier than the MPSO-RBH algorithm. Therefore, the MPSO-TCS algorithm has better convergence, and the solution is more realistic and well distributed.

[0239] Figure 3 It is a flowchart of the hierarchical solution of the model.

[0240] Figure 4 It is the fuel cost curve of each distributed power source. It can be seen that as the output power increases, the fuel price of distributed power sources such as diesel engines (DE) and micro gas turbines (MT) increases faster and the price becomes higher.

[0241] Figure 5 The WT and PV output and daily load curves are shown in Figure 1. During the period 17-22, the combined output of wind turbines and photovoltaics in the microgrid is less than the load demand, and it is necessary to purchase electricity from outside to meet user demand. The load demand can be met in other periods.

[0242] Figure 6 This is a time-of-use electricity price diagram. At any time, the price of electricity purchased by EV from the outside is higher than the price of electricity sold by itself.

[0243] Figure 7 is the load curve diagram under different strategies. Figure 7 It can be seen that under Strategy 1, EV charging is concentrated between 9:00 and 13:00 and 18:00 and 22:00. These two time periods are when car owners arrive at their workplaces and residences. During this period, car owners charge in an unordered manner according to their wishes, which leads to "peak upon peak" loads and increases the burden on NEMG. Under Strategy 2, driven by profit, EVs discharge during high electricity price periods of 9:00 to 13:00 and 18:00 to 22:00 to gain profit, and charge during low electricity price periods of 1:00 to 6:00 to reduce charging costs, but new load peaks and valleys appear due to the "herd effect". Under the strategy of the present invention, the dual influence of expense expenditure and travel convenience makes EVs charge and discharge rationally, effectively playing the role of "peak shaving and valley filling". The strategy of the present invention effectively balances load fluctuations, makes it easier for microgrids to optimize scheduling, and makes microgrids more stable and secure.

[0244] Figure 8 The Pareto frontier diagram for multi-objective optimization. The optimal charging and discharging power of EV obtained by optimizing the EV layer is transferred to the NEMG layer, and together with the basic load, photovoltaic output and wind turbine output in the NEMG layer, it constitutes a net load. Based on the net load, NEMG dispatches the internal controllable distributed power output, and the Pareto frontier is obtained by solving the MPSO-TCS algorithm. It can be seen that the comprehensive cost of NEMG corresponding to the non-dominated scheme is distributed between 3700 and 3900 yuan, and the interactive power fluctuation with the main grid is distributed between 0 and 500kW2. The fuzzy membership function is used to select (3757.2382 yuan, 69.5800kW2) in the Pareto frontier as the final compromise solution.

[0245] Fig. 9 It is the output curve of each unit in NEMG. Fig. 9The final compromise solution can be seen, corresponding to Pgrid,t, PSB,t, PDE,t and PMT,t in the 1-T period of the 1-24 period. It can be seen that: first, compared with the pollutant emissions of the main grid power generation, the power generation cost and pollution treatment cost of MT and DE are low. They are the power supply units of NEMG in each period. Only when there is a shortage, SB is called to discharge and purchase electricity from the main grid. When there is a surplus, SB is charged and the main grid is sold. Secondly, compared with DE, MT has a lower operating cost, so MT is fully output and DE output power is 150-200kW. Furthermore, in order to minimize the comprehensive cost, NEMG buys electricity from the main grid when the electricity price is low (such as 1:00-6:00) and sells electricity to the main grid during high electricity price periods (such as 9:00-13:00 and 18:00-22:00). Finally, the main role of SB is, on the one hand, to reduce the operating cost of NEMG by charging during periods of lower electricity prices (such as 1:00-8:00 and 22:00-24:00) and discharging during periods of higher electricity prices (11:00-21:00), and on the other hand, to reduce the interactive power fluctuations between NEMG and the main grid (such as 9:00-10:00) through orderly charging.

[0246] Fig.10 is the Pareto frontier diagram without stratification. Fig.10 It can be seen that without stratification, the MPSO-TCS algorithm is used to simultaneously solve and calculate the EV charging and discharging power, Pgrid,t, PSB,t, PDE,t and PMT,t, and Pareto frontier of time periods 1 to 24. Then the fuzzy membership function is used to select (0.3889, 5090.9112 yuan, 96.7553kW2) as the final compromise solution, and the corresponding quantitative index values ​​of comprehensive satisfaction, NEMG comprehensive cost and interactive power fluctuation are 0.3889, 5090.9112 and 96.7553 respectively. Compared with the strategy of the present invention in Table 1, the comprehensive satisfaction decreased by 41.19%, the NEMG comprehensive cost increased by 48.80%, and the interactive power fluctuation increased by 39.06%. It further illustrates that under stratified optimization, the interests of EVs are guaranteed and the overall benefits of the system are large.

Claims

1. A multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles, characterized in that The following steps are involved: Step 1: Read the parameters of the electric vehicle EV and the power grid price; Step 2: The EV layer calls the CPLEX software algorithm to obtain the objective function maxF1; Step 3: Based on the owner satisfaction and with the goal of maximizing it, the CPLEX software is called to obtain the optimal charging and discharging power of the EV layer and transferred to the NEMG layer; Step 4: The NEMG layer uses the PSO-TCS algorithm to obtain the objective function minF 11 and minF 22 ; Step 5: Read the NEMG system parameters, set the particle swarm parameters, initialize the particle swarm; set k = 1, and start the iteration; Step 6: The NEMG system superimposes the optimal charging and discharging power of EV with the basic daily load, PV, and WT output to obtain the net load of NEMG; Step 7: Determine the optimization conditions, optimize the NEMG layer, and update the particle positions and corresponding function values; Step 8: Use the classic crowding distance sorting strategy to obtain the optimal Pareto solution for this iteration; Step 9: Determine the total number of generations Gen and Gen-max. If Gen is greater than or equal to Gen-max, the iteration stops; otherwise, k=k+1, and return to step 7 for the next iteration. Step 10: Use the MPSO-TCS algorithm to solve the Pareto solution and Pareto frontier; Step 11: Use the fuzzy membership function to select the corresponding compromise solution from the Pareto frontier as the final solution and output the result; Through the above steps, multi-objective hierarchical optimization scheduling of new energy microgrids containing electric vehicles is achieved; In step 2, the EV layer objective function is: Where: θ i , δ i are the cost satisfaction and travel satisfaction of EV owners respectively; α1 and α2 are θ i , δ i The weight coefficient of ; N represents the total number of EV vehicles; (1) Satisfaction with the cost of the i-th EV: Where: are the maximum and minimum expenses that EV owners can afford; the expense C i As shown below: C i =C i,price +C i,bat C i,price , C i,bat They are respectively expressed as charging and discharging costs and potential battery loss costs; In the formula, are charge and discharge state variables, 1 indicates charge and discharge, and 0 indicates no charge or discharge; C change Cost of replacing the battery; E change is the maximum charge and discharge capacity; T and t represent the total time period and a certain time period respectively; P i,t represents the charging or discharging power of the i-th EV at time t; They represent the charging and discharging electricity prices for each time period released by the NEMG control center; (2) Travel satisfaction of the i-th EV: Where: are the EV power when travel satisfaction is maximum and minimum, respectively. When the owner charges in a disorderly manner according to travel convenience, his travel satisfaction is maximum, that is, δ i =1, when the owner charges and discharges the car without considering the convenience of travel in order to minimize his own expenses, his travel satisfaction is the lowest, that is, δ i =0; The corresponding constraints are: ①. EV charging and discharging power constraints: Where: are the maximum power of EV charging and discharging respectively; P i,t represents the charging or discharging power of the i-th EV in period t; ②. EV charging and discharging state variable constraints: The tables represent the charge and discharge state variables of the i-th EV during period t; ③. EV state of charge constraints: SOC min ≤SOC i,t ≤SOC max ; Where: SOC max , SOC min The upper and lower limits of the EV state of charge; SOC i,t is the state of charge of the i-th EV at time period t; E is the rated capacity of the EV battery; η char , η dis They are EV charging and discharging efficiency; SOC i,t+1 , SOC i,t They represent the charge state of EV i at time period t+1 and time period t respectively; In step 4, the NEMG layer objective function is: (1) NEMG has the lowest comprehensive cost: min F 21 =C op +C en ; Where: C op , C en are the operating cost and environmental cost of NEMG respectively; NEMG operating costs C op It can be expressed as follows: Where: J is the total number of controllable distributed power sources, j=1, 2 represents DE, MT respectively; It is the output of distributed power generation; are the fuel cost and operation and maintenance cost of the jth type of distributed power generation, and is the output of the distributed power generation Function of OM j It is expressed as follows: K om,j is the operation and maintenance factor; Energy interaction cost between NEMG and main network C grid It is expressed as: In the formula, Indicates the status of buying and selling electricity; They are the buying and selling electricity prices for each time period according to the collected data by the NEMG control center; SB charging and discharging cost C SB_price It is expressed as: P SB,t is the charging and discharging power of the battery in period t; Respectively represent the charging and discharging status of the battery in time period t; SB charge and discharge conversion loss cost C SB_bat It is expressed as: C SB_bat =nC loss 4 n is the number of SB charge and discharge conversions; C loss is the conversion cost of each charge and discharge of SB; SB operation and maintenance cost C SB_om Can be expressed as: K om,SB is the operation and maintenance coefficient of SB; Environmental cost of NEMG en It can be expressed as follows: Where: K is the total number of pollutant types; C k is the unit cost of treating the kth type of pollutant; u j,k 、u grid,k are the unit emissions of the kth type of pollutants generated by the operation of the jth type of distributed power source and the main grid tie line; T represents the total time period; J represents the total category of distributed power source; represents the power generation of the jth distributed generation at time t; P grid,t is the power exchanged between NEMG and the main grid. A positive value indicates that NEMG purchases electricity from the main grid, whereas a negative value indicates that NEMG sells electricity to the main grid. (2) The power fluctuation of NEMG interaction with the main network is minimal: Where P grid,av is the average interaction power in the scheduling period; The corresponding constraints are: ①. Power balance constraints: Where: P net_load,t is the net load in time period t; P load,t is the basic load; P WT,t , P PV,t They represent the power generation of wind turbines and photovoltaic modules in period t respectively; ②Distributed power output constraints: Where: They are The upper and lower limits of ③. Distributed power supply climbing constraints: Where: r j is the maximum ramp rate of the jth distributed power source; represents the power generation of the jth type of distributed generation in the t-1 period; ④. SB charge and discharge power constraints: Where: are the maximum values ​​of SB charge and discharge respectively; ⑤. Constraints on NEMG trading state variables: They represent the state variables of NEMG buying and selling electricity to the main grid in period t respectively; ⑥. SB charge and discharge state variable constraints: They represent the charging and discharging state variables of the battery in period t respectively; 7. SB charge state constraints: SOC SB,min ≤SOC SB,t ≤SOC SB,max Where: SOC SB,max , SOC SB,min SOC is the upper and lower limits of SB state of charge; SB,t , SOC SB,t+1 are the charge states of SB in period t and period t+1 respectively; E SB is the rated capacity of SB; η SB,char , η SB,dis are SB charge and discharge efficiency respectively; u is SB self-discharge rate.

2. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 1, the parameters of the electric vehicle EV are read. The parameters of the electric vehicle EV include: the time when the EV is connected to the power grid, the time when it leaves the power grid, the initial state of charge, the expected state of charge, and the following parameters: Rated capacity of EV battery: E; Maximum charge and discharge capacity: E change ; Battery replacement cost: C change ; Upper and lower limits of EV state of charge: SOC max , SOC min ; Maximum power of EV charging and discharging: P charmax , P dismax ; EV charging and discharging efficiency: η char , η dis .

3. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 3, based on the goal of maximizing the comprehensive satisfaction of the car owner, the CPLEX software is called to obtain the maximum charge and discharge power of the EV layer, and transferred to the NEMG layer; Owner satisfaction: θ i , δ i They are respectively represented as the cost satisfaction and travel satisfaction of the i-th EV; α1 and α2 are θ i , δ i The weight coefficient of .

4. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 6, the NEMG system superimposes the optimal charging and discharging power of the EV with the basic daily load, PV, and WT output to obtain the net load of the NEMG: Where: P net_load,t is the net load in time period t; P load,t is the basic load; P WT,t , P PV,t They respectively represent the power generation of wind turbines and photovoltaic modules in period t.

5. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 7, the optimization formula of the NEMG layer includes the traditional formula and the absorption formula: (1) Traditional method: Where: l, r1, r2∈[0,1] are random numbers; subscripts i and d represent the dth dimension of the ith particle; superscripts k and k+1 represent the kth and k+1th generations of optimization iteration; v and x are the velocity and position of the particle, respectively; pbest is the individual optimal; c1 and c2 are learning factors; r is the radius of the black hole; They represent the d-th dimension speed of the i-th particle in the k+1-th and k-th generations of optimization iterations respectively; They represent the d-th dimension position of the i-th particle in the k+1-th and k-th generations of optimization iterations respectively; They represent the optimal individual of the d-th dimension of the i-th particle of the k-th generation of optimization iteration and the optimal individual of all particles in the d-th dimension respectively; w is the inertia weight; (2) Absorption formula: Where: x max 、x min are the upper and lower bounds of the particle respectively; k and Gen are the current generation and the total generation respectively; r is the radius of the black hole, that is, the region where the approximate true solution is located; l′, r3, r4∈[0,1] are random numbers; a is the random number credibility factor, and a∈[0,1].

6. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 8: a classic crowding distance sorting strategy is adopted to obtain a Pareto solution, where the Pareto solution refers to a set of all solutions in the feasible domain obtained by using the MPSO-TCS algorithm with the goal of minimizing the comprehensive cost of NEMG and minimizing the interaction power between NEMG and the main grid.

7. According to claim 1, a multi-objective hierarchical optimization scheduling method for a new energy microgrid containing electric vehicles is characterized by: In step 11: the fuzzy membership function is used to select the corresponding compromise solution from the Pareto frontier as the final solution, that is, P in the period 1 to T grid,t , P SB,t , P DE,t and P MT,t .

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