The study on the control strategy of micro grid considering the economy of energy storage operation

Transcription

1 The study on the control stratey of micro rid considerin the economy of enery storae operation Zhiwei Ma, Yiqun Liu, Xin Wan, Bei Li, and Min Zen Citation: AIP Conference Proceedins 1864, (2017); View online: View Table of Contents: Published by the American Institute of Physics Articles you may be interested in Transient noise suppression alorithm in speech system AIP Conference Proceedins 1864, (2017); /

2 The study on the control stratey of micro rid considerin the economy of enery storae operation Zhiwei Ma 1, a), Yiqun Liu 1, b), Xin Wan 1, c), Bei Li 2, d) 1, e) and Min Zen 1 North China Electric Power University, Beijin , China 2 China Electric Power Research Institute, Beijin , China a) @163.com b) Correspondin author: @qq.com c) @qq.com d) @qq.com e) zenminbj@vip.sina.com Abstract. To optimize the runnin of micro rid to uarantee the supply and demand balance of electricity, and to promote the utilization of renewable enery. The control stratey of micro rid enery storae system is studied. Firstly, the mixed inteer linear prorammin model is established based on the recedin horizon control. Secondly, the modified cuckoo search alorithm is proposed to calculate the model. Finally, a case study is carried out to study the sinal characteristic of micro rid and batteries under the optimal control stratey, and the converence of the modified cuckoo search alorithm is compared with others to verify the validity of the proposed model and method. The results show that, different micro rid runnin tarets can affect the control stratey of enery storae system, which further affect the sinal characteristics of the micro rid. Meanwhile, the converent speed, computin time and the economy of the modified cuckoo search alorithm are improved compared with the traditional cuckoo search alorithm and differential evolution alorithm. INTRODUCTION In recent years, micro rid has been developed rapidly as a multi complementary smart enery interated utilization of local area network. The rid-connected micro rid is connected to the external power rid, which can realize the flexible switchin between two operatin modes of rid connected and isolated network. When the power eneration meet the demand of micro rid, micro rid control system will send the excess electricity into the network. Otherwise, the power supply will be achieved by main network, to form bidirectional balance, so as to meet user's requirements for power quality, power supply reliability and security. There has been a lot of rid-connected micro rid project in China, such as Luxidao rid-connected micro rid [1]. Micro rid contains a variety of renewable enery eneration, while the renewable enery output period is not consistent with the user's power consumption period, so it is difficult to meet the balance of supply and demand [2-3]. But enery storae can eliminate the peak valley difference between day and niht effectively, so as to promote renewable enery consumption [4]. Based on this, this paper studies the operation stratey of battery enery storae system with rid connected micro rid. At present, the domestic and forein scholars have studied the control stratey of battery enery storae system [5-12]. However, there are two problems in the previous studies, one is the static modelin; the other is the traditional calculation method, which needs to be improved in the converence time and the calculation results of the two. Therefore, rollin horizon dynamic control stratey is used to construct model, and adopt modified cuckoo search alorithm when calculatin, and compare it with traditional cuckoo search alorithm and differential evolution alorithm, to verify the validity of the model and method. Green Enery and Sustainable Development I AIP Conf. Proc. 1864, ; doi: / Published by AIP Publishin /$

3 CONTROL STRATEGY MODEL OF MICROGRID ENERGY STORAGE SYSTEM In actual operation, optimal control stratey of micro rid enery storae system must take some factors such as electricity transaction volume, transaction price and operatin cost into account, and the factors vary with the operation state, so it is necessary to introduce the optimization method and concept of dynamic prediction. In this study, based on the principle of recedin horizon control MILP optimization model of micro rid enery storae system control stratey is constructed. Recedin Horizon Optimization Recedin horizon optimization is a dynamic optimization method. It is very difficult to achieve the lobal optimization in the dynamic environment with uncertainty, so we need to repeat the local optimization to make the prediction result closer to the lobal optimization. In order to achieve the purpose mentioned above, the idea of rollin optimization is introduced into dynamic prediction. As a Optimal control stratey that can track chanes in the system, recedin horizon optimization method can continuously promote the optimization set on the time axis and use optimization alorithms to solve the problem, the local optimization is achieved by solvin sub problems, and the final lobal optimization is achieved by continuous rollin. The runnin time of the micro rid enery storae system is divided by the recedin horizon optimization, and the rollin simulation and optimization are carried out in the form of step size. Net demand power forecast Cost and demand baseline Battery usae situation MILP optimization Battery enery storae device FIGURE 1. Control module chart of the recedin horizon control method Model Buildin The control module of the future micro rid enery storae system is based on AEMS (Advanced Enery Manaement). Combined with the principle of rollin horizon control, the control module of the micro rid enery storae system in this paper is shown in Fiure 1. Assumin that for each time step, the alorithm enerates an estimate of the expected net demand power vector Dnet PRE and the uncertainty error Dnet PRE. Dnet PRE represents the difference between the predicted power demand and the renewable enery output, represented in vector form. Dnet PRE=Dnet indicates that the net demand power vector predicted by the alorithm is equal to the actual power demand vector, that is, the net demand is predicted perfectly, Dnet represents the actual difference between the demand power and the wind farm power. In order to simulate the optimal charin and discharin stratey of enery storae devices, a MILP problem will be formed at each time step. Therefore, this paper tries to solve the problem by usin the method of recedin horizon optimization. The rollin horizon optimization method for a micro rid system with enery storae is described in fiure3. C represents cost, Dbase means the time domain demand power baseline, BE final represents prediction of battery enery in the time domain end, BE0 represents actual battery enery in time domain startin point, BP c means charin power of battery enery storae system, BPd is dischare power of battery enery storae system

4 Objective function The optimal oal of settin the model is to minimize the operatin cost of micro rid enery storae system, so, in this paper, we establish the objective function of the chare / dischare control stratey model of micro rid enery storae system based on recedin horizon optimization as shown below. min V BP V BP V BP V BP [ BC ( BP BP ) C BR C GR C D C ( D D )] (1) T cb T cs T db T ds T c d T T b s b s BR GR hih hih flat max min In this formula, the total forecast time zone is T, N is the number of rollin time window in the whole model optimization process, T is the step size of the rollin time window, that is, the predicted step size, and the total lenth of the whole optimization process is N T. In the rane of each step, the optimization of the next time step size should be based on the previous optimization. The meanins of the 3 factors in the formula (1) are as follows. Factor 1 represents the net electricity cost of the system. The electric power trade is different from the charin and discharin behavior of the system. When the charin and discharin of the enery storae system is carried out, the micro rid can still do some purchase or sale of electricity. BP cb represents the power to be purchased from the power rid when the battery is chared, V b T means purchase price of micro rid from rid, BP cs represents the power supplied to the rid section when the battery is chared, V s T represents the electricity price that enery storae system sell to power rid, BP db means power consumption of enery storae system in battery dischare, BP ds represents power supply of enery storae system to power rid durin battery dischare. Factor 2 indicates the cost of the battery and the cost of sinal smoothin. BP c = BP cs + BP cb, BC represents the cost of battery operation, BR is the chane of power ratio of battery enery storae system in continuous time step, and C BR represents the cost of smoothin battery power distribution sinals. Factor 3 ives the cost of micro sinal formation. GR is the chane of the power of the micro rid in the continuous time step, and C GR is the cost of smoothin the micro rid. D=BP + D net represents power level for the whole system. D hih is the maximum power value of the power over the reference demand, C hih means the cost of cuttin this power, D min is micro rid maximum power demand, C flat means the cost of flattin micro rid power demand. Constraint condition In this section, the constraints on the operation of micro rid and battery enery storae devices are established. Chare / dischare state decision-makin constraints of battery enery storae devices The battery charin and discharin power rane is as follows. 0 BPc BPc, max BP c, max (2) 0 BPd BPd, max (1- ) BP d, max (3) In this formula, i=1means charin, i=0 means discharin. BP c,max, BP d,max means the maximum chare / dischare power of the enery storae system respectively. BP c,max, BP d,max represents current battery remainin chare / dischare limits. Decision-makin constraints to buy / sell electricity from the rid of microram are as follows. BP db, max (1- b) BP db BP db, max (4) BP ds, max (1- s) BP ds BP ds, max (5) BP cs, max (1- s) BP cs BP cs, max (6) BPcb,max (1- b) BPcb BPcb,max b, 0 s, b 1 (7) BPk, kdb, max=min (max (0, Pdk), BPd, max) (8) BP kcs, max=min (max (0,-Pdk), BP c, max) (9) k represents a specific time zone for system optimization, vector b and s represent the decision makin of the purchase and sale of micro rid enery storae system. The formula (8),(9) indicates that the maximum value of the

5 BP db and BP cs of the micro rid enery storae system in the optimized time domain depends on the net power demand of the load to the enery storae system P dk. Enery and power variation constraints of battery enery storae systems Battery enery level constraint is as follows: min k k k c loss 1 d 0 max i i i i, 1, (10) BE T BP BP T BP BE BE k N c d T i 1 i 1 i 1 In this formula, BE 0 indicates the enery level of battery enery storae system of time domain initial, BE min and BE max mean minimum / maximum limit respectively. c and d are chare / dischare efficiency. The battery enery level constraint in the time domain is as follows: c TT BPc- d-1 TT BPd-BPloss TT1=BEfinal-BE0 (11) In this formula, BE final is expected battery enery level at the end of time, the value should ensure that the battery enery is not exhausted. Battery sinal smoothin and power chane constraint is as follows. - BP T BPkc+BPkd-BPk-1c-BPk-1d BRk BP T (12) BP means maximum power conversion rate of battery enery storae system. Micro rid system sinal constraint The A-EMS controller can adjust the power distribution curve of the micro rid and the common point of the power network. In order to reulate and weaken the peak power over the demand baseline, usin the followin inequality constraints. In this formula, D base means reference power demand within the system. Micro rid sinal flattlin constraint is as follows. Dnet Dhih-Dbase (13) D min (BP cs +BP cb)-(bp db+bp ds) +Dnet D max (14) In this formula, D min, D max indicate the minimum and maximum values of the operatin power level of micro rid system respectively. Micro rid power sinal smoothin constraint is as follows. GR k (BP kcs+bp kcb)-(bp kdb+bp kds) + (Dnet, k-dnet, k-1)- (BPk-1cs+BPk-1cb)+ (BPk-1db+BPk-1ds) GRk, (15) MCS ALGORITHM Throuh the control stratey model of micro rid enery storae system that constructed by means of recedin horizon optimization, the rollin simulation and prediction of system runnin state can be achieved. In order to solve the model to et the optimal operation stratey, the MCS alorithm is introduced in this model. Alorithm Overview The main idea of CS alorithm is: enerate candidate nests by Lévy fliht path, and use elite reserve stratey to update the current nest position, so as to make the location of nest close to the lobal optimum. In CS alorithm, the formula to update the optimal nest is as follows:

6 Firstly, update by Lévy fliht. Adopt Lévy fliht search mechanism in the intellient alorithm. Which an expand the search rane, increase the diversity of the population, to jump out of the local optimal solution [13]. The update formula is as follows. Xi+1=xi+ L ( ) (16) L ( ) ~u=, 1< 3 (17) In this formula, x i +1 an individual i in the +1 eneration, is the step size control vector for random search, means point to point multiplication, L( ) is Lévy random search step. Secondly, update by fixed probability P. After each iteration, the probability P of the cuckoo e bein found by the host and random number are compared. If P<, the x i +1 is randomly chaned to enerate a new individual, on the contrary, do not chane. The way of new individual eneration is as follows. Xi +1=xi+ (xj-xk), i=1,2,,n (18) In this formula, x j and x k are two random solutions of the eneration, the location of the bird's nest is still x i +1. In the CS alorithm, there are two important parameters, P and, which are normally set to a fixed value, and remain unchaned durin iteration. In the process of iterative optimization, if P is larer, and is smaller, the converence speed of the alorithm will be improved, but the precision of the lobal optimal solution will be decreased. On the contrary, it will lead to a substantial increase in the number of iterations. Therefore, on one hand, there is larer randomness usin CS alorithm to enerate the initial solution, so in order to obtain a hih quality initial population, the population size should be improved. On the other hand, the CS alorithm depends on the location of the host nest in the Lévy fliht update population, and because of the lack of communication between the roups, the individual can t share their knowlede and experience to ive full play to the advantaes of roup collaboration. So it is necessary to improve the alorithm to improve its performance. Improved CS Alorithm Based on the above problems, this paper improves the CS alorithm in two aspects. First, introduce the enetic alorithm to increase the diversity of the population; second, referrin to the DE alorithm, we can embed the mutation, crossover and selection operations to improve the competition and cooperation between the roups, thus increasin the accuracy of the optimization results. Increasin population diversity In CS alorithm, the method to enerate initial individuals is as follows. Xi =xmin+ (xmax xmin), i [1,SIZE] (19) In this formula, x i is the initial ith population individual, x MAX and x MIN are upper and lower limits of population respectively, is a uniform random number between [0,1], SIZE means population size. With the continuous expansion of population size, the scope of optimization is also expandin, which will have an adverse impact on the optimization of the alorithm. Therefore, in this paper, the best individuals of each eneration are chaned to improve the quality of the population. The variation mechanism is as follows. xbest'=xbest+ { 1 cos [ (Giter-1)/2(GMAX-1)} (20) In this formula, x best' is the location of the bird nest, [0, 1] is 1 D vector, obey standard normal distribution, D is the dimension of the optimization problem. G MAX is maximum number of evolutionary alorithms, G iter is current eneration number. In order to ensure that the variation is in a favorable direction, the fitness values of x best' and x best are compared to select individuals with hiher fitness values to inherit to the next eneration to achieve effective mutation operations

7 x x, x x BEST best best best' x x, x x BEST best' best best' (21) Differential Evolution Operator As a kind of evolutionary alorithm based on population differentiation, DE alorithm enhance the deree of competition and cooperation between roups throuh mutation, crossover and selection to form an effective information sharin mechanism, which is an effective lobal optimization alorithm. Accordin to the information sharin mechanism of differential alorithm, this paper constructs the difference operator into the multi object CS alorithm. Mutation. Consider all the nests as a population, x i is an individual that needs to be chaned. Randomly select two individuals in the current population x rand1, x rand2, and enerate variant individuals throuh differential stratey y i. yi=xi+m (xrand1-xrand2) (22) Crossover. Crossover operation is to reconstruct the parent x i and variant y i to enerate candidate z i. z i i, z x, other. i y i r CR or d?, (23) In this formula, r is a uniform random number in [0, 1], CR is crossover probability, d is a randomly selected dimension. Selection. Compare the relationship between the candidate z i and the parent x i to inherit the dominant individuals to next eneration. This operation is used to achieve the preservation of elite individuals. x z, z dominatex, 1 i i i i x x, x dominatez, 1 i i i i x random z, x 1 i i i, (24) In this formula, random i, i z x means selectin z i and x i with equal probability randomly. SIMULATION RESULT In this section, a micro rid enery storae system is selected as an example. Under the optimal control stratey, the characteristics of the micro rid sinal and the battery sinal under different tarets are studied, and the converence of the alorithm is compared with other alorithms to verify the validity of the model and alorithm. This example is carried out in a commercial and residential environment, and system power eneration is from wind turbines. Typical daily system load requirements are shown in fiure 2. System wind power eneration of typical day is shown in fiure3. The micro rid system uses TOU price, as shown in Fiure

8 FIGURE 2. Load demand of the micro rid FIGURE 3. Wind power output of the micro rid Price/yuan kwh FIGURE 4. Time-of-use electricity price of the micro rid The basic parameters of the battery enery storae system are shown in table 1. TABLE 1. Parameter list of the battery enery storae system of micro rid Variable BE min BE max c d BP c,max BP d,max BP loss Vb=Vs numerical value 0 kwh 15 kwh kw 3 kw 0kW 0 Based on the above data, the power sinal characteristics of micro rid and battery enery storae system under different tarets are studied, and 3 tarets are set up. Peak clippin of micro rid system. Reduce the peak of the micro rid demand throuh the enery storae system under the premise of cuttin down, to maintain the level of 10kW and below. At this point, the cost of clippin peak C hih=1, other costs are 0. Sinal flattin of micro rid. On the basis of the peak clippin of micro rid, the lower limit of the demand for micro rid is improved by usin the enery storae system, which is maintained at 2kW and above. Adjusted accordin to the upper and lower limits of the system's net demand. At this point, the cost of micro sinal flattin C flat=1, other costs are 0. Micro rid sinal smoothin. Smooth the micro rid sinal on the basis of the sinal flattin oal of the micro rid system. At this point, micro rid sinal smoothin cost is C GR= T, other costs are 0. Time rane is 24h, and time interval size can be randomly set. To facilitate the calculation, this paper set it to T= [ ] T. In order to verify the optimal control model of the micro rid enery storae system, the MCS alorithm is used to solve the problem, and the results are compared with the CS alorithm and the DE alorithm. In this paper, we set the population size SIZE=50, maximum number of iterations G MAX=200, discovery probability P=0.25, crossover probability CR=0.85, scalin factor M=0.4. The characteristics of the micro sinal under different tarets are shown in fiure 5- fiure 8. The dotted line represents the upper and lower runnin time constraints of the entire reion of micro rid power system operation, thick lines show the actual operation of power system. Fiure 5 shows the power distribution of the initial net demand of micro rid. Fiure 6 shows the sinal characteristic of the micro rid under the object of peak-shavin. Fiure 7 shows sinal characteristic of the micro rid under the object of flattin. Fiure 8 shows the sinal characteristic of the micro rid under the object of smoothin

9 power/kw /kw 12 9 /kw (h) FIGURE 5. Power distribution of the initial net demand (h) FIGURE 6. Sinal characteristic of micro ird of micro rid under the object of peak-shavin power/kw /kw 12 9 /kw /h FIGURE 7. Sinal characteristic of the micro rid under the object of flattin /h FIGURE 8. Sinal characteristic of the micro rid under the object of smoothin Based on the flattin and smoothin of the sinal of micro rid, use CS alorithm and DE alorithm to improve the converence and computation time of MCS alorithm. The operatin cost of the micro rid system is shown in fiure COST/yuan MCS DE CS Iterations FIGURE 9. Comparison of the iteration process of MCS alorithm, CS alorithm and DE alorithm Therefore, compared with the CS alorithm and DE alorithm, MCS alorithm has faster converence rate and lower cost, which proves scientificness and effectiveness of optimization model of control stratey for micro rid enery storae system based on MCS alorithm

10 SUMMARY In this paper, we construct the dynamic control model of rid connected micro rid enery storae system based on recedin horizon prediction, and propose the MCS alorithm to calculate the example. The results show that different micro rid operatin tarets will affect the control stratey of the enery storae system, and then affect the sinal characteristics of the micro rid. Meanwhile, compared with the traditional CS alorithm and DE alorithm, the MCS alorithm has a sinificant improvement in converence speed, computation time and economy. But in this paper, the model of renewable enery output has not been established, and the research of electric vehicle charin and discharin is not included, which can be further improved. ACKNOWLEDGMENTS This paper is supported by Science and Technoloy Project of SGCC (The comprehensive evaluation on the advanced and lare-capacity enery storae applied to the power rid). REFERENCES 1. Luxidao micro rid demonstration project successfully put into trial operation [J]. East China electric power, 2014, 42 (1): Zen M, Duan J, Wan L, et al, Orderly rid connection of renewable enery eneration in china: manaement mode, existin problems and solutions [J]. Renewable and Sustainable Enery Reviews, 2015, 41 (1): Lu Y. Review and prospect of clean, renewable enery utilization [J], Science & Technoloy Review, 2014, 32: Wan S, Lai X, Chen S. An analysis of prospects for application of lare-scale enery storae technoloy in power systems [J]. Automation of Electric Power Systems, 2013:37 (1): Zhan B, Chen Y, Dai X. Controller s parameters optimization of the distributed enery storae system in micro rid [J]. Journal of Huazhon University of Science and Technoloy (Nature Science Edition), 2014, 42(12): Miao F, Chen Y, Xu Z. Control study on enery storae unit in photovoltaic micro rid system [J], China Journal of Power Sources, 2014, 38 (7): Bi R, Wu J, Din M, A cooperative control stratey for standalone micro rid consistin of multi-storae and diesel enerators [J], Automation of Electric Power Systems, 2014, 38 (17):73-79, Kumars R, Kumars M, Candela J, et al. intellient voltae control in a DC micro-rid containin PV eneration and enery storae[c]//proceedins of the IEEE Power Enineerin Society Transmission and Distribution Conference, 2014, Vasilevskiy J, Peças Lopes JA, Matos MA. Interated micro-eneration, load and enery storae control functionality under the multi micro-rid concept [J]. Electric Power Systems Research, 2013, 95: Navix E, Ebrahem F. Distributed chare/dischare control of enery storaes in a renewable-enery-based DC micro-rid [J]. IET Renewable Power Generation, 2014, 8(1): Tan W, Lv Z, Hu L. Control stratey of isolated operation for DC micro-rid system [J]. Journal of Guanxi University: Nat Sci Ed, 2014, 39(5): Ma X, Lv Z, Lu Z. Research on dynamic optimization of enery for stand-alone micro rid with wind/pv/battery enery resources [J]. Journal of Guanxi University: Nat S ci Ed, 2013, 38 (2): Rani KNA, Wee FH, Malek MFA. Modified cuckoo search alorithm in weihted sum optimization for linear antenna array synthesis[c]//2012 IEEE Symposium on Wireless Technoloy and Applications (ISWTA), Bandun, Indonesia, 2012: