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Energy Procedia

Energy Procedia 00 (2011)12000–000 Energy Procedia (2011) 951 – 957

www.elsevier.com/locate/procedia

ICSGCE 2011: 27–30 September 2011, Chengdu, China

Optimized Fuzzy Control Algorithm in Integration of Energy Storage in Distribution Grids A. Darvishi *, A. Alimardani, B. Abdi Damavand Branch, Islamic Azad University, Damavand, Iran

Abstract This paper presents the incorporation of energy storage system (ESS) in smart distribution systems so that the total payment of the system is minimized along with compliance of the system constraint including voltage limit in the system's steady state. Fuzzy control and particle swarm optimization (PSO) algorithm are employed to acquire the charge/discharge mode and the amount of energy in storage system in each hour of a day using online pricing. The storage system is allocated in such a way that system constraint including voltage not violated in system’s steady state condition. So that a distribution load flow in backward/forward load sweep method is used to restrict the voltages in limit. Finally the profile of charge/discharge storage system considering bus voltage deviation limit is illustrated. © 2011 Published by Elsevier Ltd. Open access under CC BY-NC-ND license.

Selection and/or peer-review under responsibility of University of Electronic Science and Technology of China (UESTC) Keywords: Energy storage system; Smart distribution grids; Fuzzy control; Differential evolution

1. Introduction The electric grid delivers electricity from generation points to consumers. Electricity delivery network consists of two primary systems. First, the transmission system which delivers electricity from power plants to distribution substations. Second the distribution system which delivers electricity from distribution substations to consumers [1]. Traditionally, the electrical ″grid″ only refers to the interconnected transmission system. On the other hand, the term ″Smart Grid″ is used to refer to the entire electrical system, including generation, transmission [2-7], and distribution as well as into the home or building. It is well known that the distribution system is the largest and most complex part of the entire electrical system [8]. Thus, many research papers have focused on smart grids at the distribution level.

* Corresponding author. E-mail address: [email protected]

1876-6102 © 2011 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of University of Electronic Science and Technology of China (UESTC). Open access under CC BY-NC-ND license. doi:10.1016/j.egypro.2011.10.125

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A Smart Grid delivers electricity from suppliers to consumers using digital technology to save energy, reduce cost and increase reliability and transparency [9]. Smart Grids increase the connectivity, automation and coordination between suppliers, consumers and networks that perform either long distance transmission or local distribution tasks [8, 9]. Recent developments and advances in energy storage and power electronics technologies are making the application of energy storage technologies a potentially viable solution for modern power applications, allowing the system to be operated in a more flexible, controllable manner [10, 11]. Some papers have already studied energy storage within one main application in order to define energy storage system (ESS) optimal specifications and control strategy: Tam and Kumar used diurnal load leveling to minimize transmission losses on the AEP 14-bus system [12]. Lo and Anderson used dynamic programming algorithms to maximize fuel-cost savings and optimize battery size [13]. Maly and Kwan, using dynamic programming as well, optimized ESS charge scheduling to maximize benefits due to the energy pricing differences between peak-load and light-load periods [14]. Almost all of the references present ESS as a solution to maximize benefits due to only one application. There are some works related to energy management system (EMS) for distribution system. Gupta et al. [15] developed steady-state models for a hybrid energy system for determining its optimal operation. The hybrid system is composed of micro hydro, biogas, biomass, photovoltaic panels, a battery bank and a fossil fuel generator. An on-line power energy management for a hybrid fuel cell/battery distributed generation system is presented in [16]. The on-line arquitecture consists in three layers; first one captures the possible operations modes, the second is based in fuzzy controller for power splitting between battery and fuel cell and the last one regulates each sub-systems. Lu and Francois [17] present an energy management system based on the day-ahead power scheduling, for a micro grid (photovoltaic panels, gas turbine) considering the power prediction and load forecasting. Westermann and John in 2007 [18] describe a combination of wide-area measurement and ripple control for demand-side management. The proposed control system moderated the impact of increased renewable sources on adjacent transmission grids. Hamidi and Robinson in 2008 [19] proposed a responsive demand for a system with a significant intermittent generation. In this system, the demand can shift for reducing the demand peak and also shed for mitigating the power fluctuations, however this action requires evaluating the value of lost load for consumers. The aforementioned characteristics and research proposals are fully applicable to a smart hybrid grid concept. The scope of this paper is restricted to the incorporation of electricity storage in distribution system so that the total payment of the system is minimized. Fuzzy control and particle swarm optimization (PSO) algorithm are employed to acquire the charge/discharge mode and the amount of energy in storage system in each hour of a day. The optimization is along with bus voltage constraint. So that a distribution load flow in backward/forward load sweep method is used to restrict the voltages in limit. 2. The Distribution Network Planning Including Energy Storage The main advantages of using storage system is the more efficient usage of the grid (either for serving loads or connecting distributed generation) besides the possibility of delayed operation in response to load growth or elimination of one or more generators. In the new smart grid with the development of demand, beside management and employment of information services, especially in distribution system, storage system can be more effective than each another time. So this paper focused on storage system, it is assumed that this storage system can access to online prices and reaction to those for every hour of a day in such a way that the storage system can charge with cheap electricity in the off-peak and low cost periods and using this electricity during peak and high price periods. This has the advantage of minimum

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A.A.Darvishi – 957 Darvishietetal.al./ Energy / EnergyProcedia Procedia1200(2011) (2011)951 000–000

payment of customer in addition to loss minimization and peak load shaving; the objective function which is considered in this study is the total payment of customer. This payment can be expressed as bellows: n

m

Payment = ∑ ∑ Pi j

(1)

i =1 j =1

Pi j = ( Pd ,ij + Ps ,ij ) Pprice,i

where n is the number of hours, m is the total number of buses, Pd,ij is the demand in every bus and Ps,ij is the power charge or discharge of storage system and Ppric,j is the forecasted cost of energy in every hour. Ps,ij is achieved with corporation of PSO and fuzzy control which is mentioned in next section. This optimization is along with some constraints including bus voltage. For this aim, distribution load flow in backward/forward load sweep method is mentioned in section IV to check if any bus voltage is in limit or not. 3. PSO Algorithm and Fuzzy Control for the Optimal Allocation of Storage Units 3.1. Fuzzy control Power management strategy in control structure is crucial for balancing between price and performance of storage systems. The term ‘‘power management’’ refers to the design of the higher-level control algorithm that determines the proper power level to be generated, while satisfying the power demand from the load and maintaining adequate energy in the energy storage device. Hence an on-line control strategy for instantaneous power management based on fuzzy logic has been proposed. A fuzzy logic controller is designed to distribute the power among the battery (storage system) and distribution network, to satisfy the load power requirement with respect to dynamic restrictions of these systems such as bus voltages, power demand, battery power, and battery state of charge. A fuzzy logic controller (FLC) is used to decide on operating point of the battery storage system. FLC relates the controller output to the inputs using a list of if–then statements called rules. The if-part of the rules refers to adjectives that describe regions (fuzzy sets) of the input variables. A particular input value belongs to these regions to a certain degree, so it is represented by the degree of membership. To obtain the output of the controller, the degrees of membership of the if-parts of all rules are evaluated, and the then-parts of all rules are averaged and weighted by these degrees of membership. The core of the rule set of the fuzzy controller is illustrated as follows. Rule 1: if price is low, then Pstorage is positive (Battery charge mode) Rule 2: if price is medium, then Pstorage is zero. Rule 3: if price is high, then Pstorage is negative (Battery discharge mode). The membership function for one bus is shown in Fig. 1. The amounts of variables which are shown in the figure are acquired by PSO algorithm which is mentioned in the following.

Fig. 1. The membership functions for one bus

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3.2. PSO algorithm Particle Swarm Optimization (PSO) is a multi-agent search technique, which is inspired by the social behavior of a flock of birds searching for food [20]. Each bird in the flock is called a particle and the flock is referred to as a swarm. Each particle travels through multidimensional search space looking for the best position (global optimum), by adjusting its position according to its own experience as well as the experience of its adjacent particles [21]. In this notation, p and s denote as particle coordinates (position) and its corresponding flight speed (velocity) in a search space, respectively. The best previous position of a particle is recorded and represented as Pbest. The index of the best particle among all the particles in the group is represented as Gbest. The modified velocity and position of each particle can be calculated as shown in (2) and (3): sd +1 = k *(γ * vd + ac1 .rand () * ( pBest − pd ) + ac2 * rand () *( gBest − pd )) pd +1 = pd + sd +1

(2) (3)

where k is a function of ac1 and ac2 according to (4): k=

2 | 2 − ac − ac2 − 4ac |

(4)

where ac=ac1+ac2 and ac>0.4 appropriate selection of inertia weight, provides a balance between global and local explorations. In general, γ is set according to (5):

γ = γ max −

γ max − γ min itermax

× iter

(5)

In the above procedures, the particle velocity is limited by a maximum value, smax. In many experiences with PSO, smax was often set at 10%–20% of the dynamic range of the variable on each dimension [22]. In [23] and [24] a decaying inertia weight is proposed and tested. If the inertia weight is not reduced with time, it is suggested to select a value between 0.8 and 1.2, although there are several studies propose different values for these parameters [25]. The computation procedure of PSO technique described in the following steps. Step 1- Input the lower and upper boundaries of each particle of optimization problem and initialize the particles of the population randomly. The initial Pbest of individual i, is a set of initial positions of the individual i and the initial Gbest is determined as the position of an individual with minimum fitness. Step 2- Update the velocity vector using (2)-(4) and Modify the position considering constraints by (3) based on its updated velocity. The parameters in (3) and (4) are selected as follows: γ=0.7968, ac1=ac2=2 Step 3- If any element of an individual violates its constraints, then it should be replaced by the particle calculated in the previous iteration which satisfies constraints. Step 4- Update Pbest and Gbest. Step 5- End the iterations, if stopping criteria is satisfied, otherwise go to step 2. Clearly, if the maximum number of iterations is reached, the operation shall be also terminated.

A.A.Darvishi – 957 Darvishietetal.al./ Energy / EnergyProcedia Procedia1200(2011) (2011)951 000–000

3.3. Backward/Forward sweep method In general, distribution networks operate with a radial structure. There are two types of computational method proposed in the literature. First type is the direct search method based on circuit laws. The backward/forward sweep [26] and other methods based on node equivalent calculation [27]-[28] takes place in this type. The methods of other type need information on the derivatives of the network equations as well [29]. The traditional Newton-Raphson method and its improved methods [30] belong to this type. In this paper, backward/forward sweep method [30] is employed for load flow of distribution networks. 3.4. Simulation results In order to verify and prove the capability of the proposed approach, fuzzy control and PSO method is applied to IEEE 18-bus network shown in Fig. 2. The topology and data of this system can be found in [31]. The pattern of distribution load data for the summer season is applied to the all buses of the network. The forecasted and real energy price data is given in [32]. Applying the PSO and fuzzy control result in a daily cost of $471.563 instead $788.365 which is the daily cost without using storage system, which means 40.18% reduction in load payment. Fig.3 and Fig. 4 presents the membership functions of storage device in bus 7.

Fig. 2. IEEE 18-bus distribution network

Fig. 3. Membership functions of storage system power of in bus 7

Fig. 4. Membership functions of price for storage device in bus 7

4. Conclusion In this paper, the incorporation of electricity storage in distribution system was analyzed. Fuzzy control and particle swarm optimization (PSO) algorithm was incorporated to acquire the charge/discharge mode

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and the amount of energy in storage system in each hour of a day. The optimization minimized payment along with compliance of the system constraints including voltage limit in the system's steady state. A distribution load flow in backward/forward load sweep method was used to restrict the voltages in limit. Finally the profile of charge/discharge storage system was illustrated. References [1] Office of electricity delivery and energy reliability, ″ Smart grid, ″ Internet: http://www.oe.energy.gov/ smartgrid.htm, Accessed: August 2009 [2] Paul Marken, John Marczewski, Robert D'Aquila, Paul Hassink, Jim Roedel , ″ VFT – A Smart Transmission Technology That Is Compatible With the Existing and Future Grid, ″ IEEE Power Systems Conference and Exposition, 2009 [3] P. E. Marken, ″Variable frequency transformer – a simple and reliable interconnection technology, ″ EPRI HVDC Conference, September 2007 [4] J. Gagnon, D. Galibois, D. McNabb, D. Nadeau, P. Paquette, E. Larsen, D. McLaren, D. Piwko, C. Wegner, H. Mongeau, ″A 100 MW variable frequency transformer (VFT) on the Hydro-Québec Network,″ CIGRE, France, 2006 [5] D. Nadeau, ″A 100-MW variable frequency transformer (VFT) on the Hydro- Québec TransÉnergie Network – the behavior during disturbance,″ IEEE PE General Meeting, Page(s): 26-28, 2007 [6] Vu, Begouic, Novosel, ″ Grids get smart protection and control, ″IEEE Computer Applications in Power, Vol. 10, No. 4 , Page(s): 40-44, 1997 [7] H.F. Wang, ″Multi-agent co-ordination for the secondary voltage control in power system contingencies″, In Proc. IEE Generation, Transmission and Distribution, Vol. 148, Page(s): 61-66, Jan 2001 [8] Saint B, ″ Rural distribution system planning using Smart Grid Technologies, ″ IEEE Rural Electric Power Conference, REPC '09, Page(s):B3 - B3-8, April 2009 [9] Pipattanasomporn, Feroze, Rahman, ″ Multi-agent systems in a distributed smart grid: Design and implementation, ″ IEEE/PES Power Systems Conference and Exposition, PES '09, Page(s):1 – 8, March 2009 [10] P. Ribeiro, B. Johnson, M. Crow, A. Arsoy, and Y. Liu, “Energy storage systems for advanced power applications,” Proc. IEEE, vol. 89, no. 12, pp. 1744–1756, Dec. 2000. [11] F. A. Chacra, P. Bastard, G. Fleury, and R. Clavreul, “Optimizatio multiobjectifs du stockage d’énergie dans un poste source HTB-HTA,” in Electron. Proc. EF2003 Conf.—SUPELEC, Gif sur Yvette, France, Dec. 2003. [12] K. Tam and P. Kumar, “Impact of superconductive-magnetic energy storage on electric power transmission,” IEEE Trans. Energy Convers., vol. 5, no. 3, pp. 501–509, Sep. 1990. [13] C. Lo and M. Anderson, “Economic dispatch and optimal sizing of battery energy storage systems in utility load-levelling operations,” IEEE Trans. Energy Convers., vol. 14, no. 3, pp. 824–829, Sep. 1999. [14] D. Maly and K. Kwan, “Optimal battery energy storage system (BESS) charge scheduling with dynamic programming,” Proc. Inst. Elect. Eng. Sci. Meas. Technol., vol. 142, no. 6, pp. 453–458, Nov. 1995. [15] Gupta, A., Saini, R., Sharma, M. (2009). Steady-state ModellinHybrid Energy System. Third International Conference on Electrical Engineering, ICEE '09. [16] Hajizadeh, A., Aliakbar Golkar M. (2007). Intelligent power management strategy of hybrid distributed generation system Electrical Power and Energy Systems, Vol. 29, pp. 783–795. [17] Lu, D., Francois, B. (2009) Strategic Framework of an Energy Mana gement of a Microgrid with a Photovoltaic-Based Active Generator. ELECTROMOTION 2009 – EPE Chapter ‘Electric Drives’ Joint Symposium, 1-3 July 2009, Lille, France. [18] Westermann, D., John, A., (2007). Demand Matching Wind Power Generation With Wide-Area Measurement and Demand-Side Management, IEEE Transactions on Energy Conversion, Vol. 22, N°1, pp. 145-149 [19] Hamidi, V., Robinson, F. (2008). Responsive Demand in Networks with High Penetration of Wind Power. 2008 IEEE Power and Energy Society General Meeting - Conversion and [20] E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based algorithms”, Advanced Engineering Informatics, vol. 19, no. 1, pp. 43-53, Jan. 2005. [21] J. Kennedy, and R. Eberhart, “Particle swarm optimization,” in Proc. IEEE Int. Conf. Neural Networks, Nov. 1995, vol. 4, pp. 1942–1948.

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