Research article

Optimal sizing and operation of energy storage systems considering long term assessment

  • Received: 20 November 2017 Accepted: 02 January 2018 Published: 08 January 2018
  • This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.

    Citation: Gerardo Guerra, Juan A. Martinez-Velasco. Optimal sizing and operation of energy storage systems considering long term assessment[J]. AIMS Energy, 2018, 6(1): 70-96. doi: 10.3934/energy.2018.1.70

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  • This paper proposes a procedure for estimating the optimal sizing of Photovoltaic Generators and Energy Storage units when they are operated from the utility’s perspective. The goal is to explore the potential improvement on the overall operating conditions of the distribution system to which the Generators and Storage units will be connected. Optimization is conducted by means of a General Parallel Genetic Algorithm that seeks to maximize the technical benefits for the distribution system. The paper proposes an operation strategy for Energy Storage units based on the daily variation of load and generation; the operation strategy is optimized for an evaluation period of one year using hourly power curves. The construction of the yearly Storage operation curve results in a high-dimension optimization problem; as a result, different day-classification methods are applied in order to reduce the dimension of the optimization. Results show that the proposed approach is capable of producing significant improvements in system operating conditions and that the best performance is obtained when the day-classification is based on the similarity among daily power curves.


    Bainitic press hardening is a key production technology for advanced high strength steels, specifically in the automotive industry [1]. Structural parts like A- and B-pillars, roof rails or bumpers are produced based on this method. Currently, it is of specific interest to the industry to take this important technology to the next level in terms of process optimisation. This technique, using a combination of interrupted cooling and quasi-isothermal holding in the bainitic parameter regime, offers highly desirable strength-failure-strain combinations [2]. The possibility to control the process tool temperature offers a vast amount of possible advances in process technology, and work pieces with locally varying thermoelastic properties for a wide range of applications are one of the central goals in this context. A challenge of central importance to the bainitic press hardening process is the understanding of the bainitic transformation.

    The mechanism of the bainite transformation is the subject of ongoing debate for decades, see e.g., [3,4,5,6]. The main controversy focuses on the following two arguments: (ⅰ) the nature of the bainitic ferrite/retained austenite interface and its capacity to support the displacive mechanism of bainitic ferrite growth and (ⅱ) the role of carbon transport during the bainite transformation, i.e., whether or not bainitic ferrite is initially supersaturated with carbon and then carbon partitioning takes place from the bainitic ferrite. Within this publication, we will focus on the transport-governed regime, i.e., the transformation is limited by carbon redistribution. The main branches of the scientific discussion about the details of the transport-governed regime can be summarised as follows.

    Considerations about the carbon partitioning published in [7,8] suggest that the ferritic component of bainitic microstructures can form with maximal carbon supersaturation, while the escape of carbon from ferrite to austenite after transformation is the essential aspect of the carbon transport. Alternatively, the transport of carbon during diffusional transformation of austenite to ferrite under metastable equilibrium conditions at the interface is also considered (e.g., [5]). An intermediate scenario, where ferrite grows with a partial supersaturation of carbon, with the remaining carbon partitioning into austenite or forming carbides, is described e.g., in [9,10,11]. All of these hypothesises effectively consider the competition between carbon escape and interface migration at high supersaturations, thus the transformation might exhibit a higher velocity than is expected by equilibrium partitioning of carbon. For our investigations, we focus on a regime where the diffusional transport of carbon from the supersaturated austenitic phase at local equilibrium is the rate-limiting mechanism.

    The following parts of this publication are organised in four sections; a basic theoretical treatment of solid-solid transitions in the displacive and the diffuse-displacive case is given in Section 2. We also introduce the basics of the boundary integral method, which is used for the investigations on the terminal subunit growth regime.

    In Section 3.1, we discuss the influence of coherency stresses and capillarity stresses on the growth of banitic subunits of small radii in the initial growth regime of the diffusional-displacive transition.

    In Section 3.2, we present calculations for the specific scenario of bainitic subunit growth frustration, which results form the decreasing temperature during the continuous cooling of the bainitic transformation.

    In Section 4, we discuss the context of other related growth regimes also relevant to elastically influenced solid-solid transformations.

    The work in this article is based on theoretical work and aims at elementary aspects of growth behaviour in bainitic subunits. As representative geometry, we present Figure 1.

    Figure 1.  The geometry of the growing bainitic subunit, approximated as lenticular ferritic inclusion in the retained austenite, with radius R and height h.

    The bainitic transformation is an example for diffusional or mixed diplacive-diffusional solid-solid transitions strongly influenced by elastic effects. An overview of elastical-diffusional transformations can be found e.g., in [12,13,14], in our studies reported in [15,16,17,18,19,20,21,22], we focused on transformations exhibiting coherent interfaces. Though the coupling of displacive and diffusional transitions has been investigated recently also emphasising engineering aspects, see [23], our focus in the course of this publication is on the possible dominance of elastic effects in the initial and terminal regime of subunit growth. When we refer to the terminal growth regime of a bainitic subunit, we consider the freezing-out carbon diffusion upon the continuous decrease of temperature during the progressing bainitic transformation.

    Within this manuscript, we focus on two distinct scenarios. First, the initial growth regime of the subunit is considered. The corresponding model is a sharp interface model, which aims at the prediction of the geometric properties of the growing pattern. Second, we investigate the terminal regime of subunit growth, where we apply the boundary integral method. As the main part of the work is associated to the terminal growth regime, we present here the theoretical basics of the boundary integral method in the context of the physical model we assume for the terminal growth regime. As the boundary integral method is especially useful in the study of steady-state moving fronts, we focus on these steady-state scenarios, here, as systematically integrated in the non-isothermal context of the bainitic transformation. The growth predictions obtained from such steady-state scenarios can be interpreted as valid during the persistence time scale which is basically limited by the variation of the governing parameters of the process.

    To introduce the boundary integral method, we focus on the diffusional aspect of the transformation. We represent the diffusion equation for a field u in a frame of reference which is co-moving along the y-direction symbolically via a linear differential operator L:

    L[u]=2u+2lDyu=0, (1)

    and use the Green's function G of this operator [15] to express the symbolic solution for the diffusion field via Lagrange's identity:

    GL[u]uL[G]=ˉJ(u,G) (2)

    where L is the adjoint operator of L and ˉJ(u,G) is a function of the diffusion field and the Green's function with derivations of order not higher than one in the case of the diffusion equation. Apparently, when we integrate the Lagrange identity and have no volume inhomogeneities, the integral contributions reduces to boundary terms. In our case, the inhomogeneity is the mass conservation of carbon at the moving interface, as we discuss in the subsequent description of the physical model. Finally, the whole description reduces to a fully interfacial problem as the obtained symbolic solution expresses the value of the diffusion field on each point at the interface when the local equilibrium condition is described, see also the following description of the physical model.

    For the terminal growth regime, the basis of our considerations is a coupled model of diffusional carbon transport and static elastic misfit stresses within the two adjoining phases, i.e., the parent austenite and the growing ferrite phase, together with the physically necessary boundary conditions at the progressing phase boundary. The diffusional transport is governed by a diffusion equation in the bulk, complemented by the mass conservation condition and the Gibbs-Thompson condition of local phase equilibrium. In total, we obtain the following three equations for the dimensionless carbon concentration field u=(cc0)/Δc, with c being the non-equilibrium carbon concentration field, c0 is a reference concentration and Δc=ceqγceqα denoting the difference in the equilibrium carbon concentrations of the γ-austenite and the α-ferrite phase at the respectively imposed constant temperature

    D2u=u/t,υn=DnΔu|int,u|int=Δdκ+TeqδFel/LmΔc. (3)

    By D we denote the carbon diffusion constant, Teq is the transition temperature, L is the effective latent heat [24,25]. The capillary length d is defined here as d=γTeq/LmΔc, where γ is the surface energy, κ is the interface curvature and the dimensionless undercooling is given by Δ=(ceqc0)/(cγcα). The slope of the coexistence line is denoted by m=dT/dc. In accordance with our previous studies on displacive transformations, we choose the class of invariant plain strain transitions here, as it allows for extended coherent interfaces. When we indicate the mother phase via α and the onsetting phase via β, the elastic contribution to the local equilibrium equation is defined as

    δFel=σ(0)ikϵ(β)ikE[(ϵ0ττ)2+(ϵ0ss)2+2ν(ϵ0ττ)(ϵ0ss)+2(1ν)(ϵ0sτ)2]2(1ν2). (4)

    Here the indices ik refer to the components of the strain and eigenstrain tensors, with ss, sτ and ττ denoting the two tangential components in natural coordinates. This is obtained by introduction of the appropriate free energy and assuming phase and mechanical equilibrium at the coherent interface, see [15] for details. We require that the eigenstrain tensor ϵ(0)ik allows to satisfy coherent interfaces, the eigenstrain stress tensor σ(0)ik is the stress tensor corresponding to Hooke's law for the eigenstrains ϵ(0)ik. Therefore, the first contribution on the r.h.s of Eq 4 involves the eigenstresses and the strains in the austenitic phase. Following this brief introduction of our theoretical basis, we continue with the discussion of the initial growth regime.

    The physical picture emerging from these equations is clear. The propagation of the interface releases carbon proportional to the normal component of the growth velocity υn, while the local transition point itself depends on the interfacial stresses arising from the coherent lattice transformation and the curvature. The transport of the carbon from the interface limits the propagation velocity. We note that this model represents the coupling of the elastostatic and the diffusional problem via the local equilibrium condition for the carbon concentration at the transformation interface alone.

    The transformation of austenite to bainite involves displacive deformations of the matrix, diffusion controlled growth of bainitic sheaves and the precipitation of carbides inside the ferrite (lower bainite) or inside the austenite (upper bainite). A successful prediction of the transformation kinetics requires consideration of the complex interaction of these processes: elastic/plastic deformation, carbon partitioning and carbide precipitation. The local driving force for the transformation depends sensitively on the carbon concentration, the local misfit-stresses and the temperature. During the transformation, the carbon in the supersaturated bainitic-ferrite can either be released in the surrounding austenite phase or be captured by the embedded carbides, which are competitive for the solute atoms.

    When we focus on the very initial regime of the solid solid transformation, involving a nucleus of nanometer size, the size dependence of the elastic parameters in this stage can lead to substantial elastic softening of the growing phase. Furthermore, on this scale, the bulk crystal symmetry structure can be suppressed due to the dominance of interfacial effects on the overall energy of the nucleus. Therefore, we approximate the growing inclusion by concerning only the hydrostatic, isotropic elastic response to stresses from the surrounding bulk phase. The assumption of isotropic elasticity exhibits the lowest expected error on the real elastic behaviour of the nucleus prior any information on the early nm growth regime. Thus, we consider this initial stage in analogy to the argumentation given in [12], which provides an approximative and analytically accessible description of the growth problem.

    We assume that the new phase, which contains N particles and occupies a volume W, is homogeneous with chemical potential μ and a pressure P (we assume that the external pressure is zero). Because of the conservation of mass, we have the following expression for the volume of the melt:

    W=Nvα=Nvγ+unds (5)

    where vα and vγ are the atomic volumes of the ferritic and austenitic phases, and the last term describes the change in the volume of the nucleus upon deformation (un is the normal component of the displacement vector at the interface). The subunit is assumed to be a very oblate lentil of radius R and height h R. In a first approximation, we can ignore the height of the lentil in solving the elastic problem. In this case the pressure P, exerted on the nucleus by the retained austenite, is given on a circular plane cut within the radius R. This problem is equivalent to a crack problem and its solution provides the normal component of the displacement vector at the interface, see [26]. In this scenario, the equilibrium Gibbs-Thomson condition at the interface reads as [27,28]

    μ(P)=vα[f0γ+P+ˉσ2rr+ˉσ2ϕϕ2νˉσrrˉσϕϕ2E+γκ] (6)

    Here f0γ is the free energy density of the undeformed (initial) crystal and we have introduced cylindrical coordinates, ˉσik is a normalised stress tensor, ν and E are the Poisson and Young coefficients, γ is the interfacial energy and κ is the curvature of the interface (considered positive for a convex solid). To focus on the role of the elastic effects of the transformation, Eq 6 approximates the iron chemical potential by reducing contributions depending on the carbon content to an effective lattice expansion which is taken into account via the atomic volumes vα, vβ.

    Following the argumentation in [29] and [12], the radius Rc of the critical nucleus is related to the pressure P by the Griffith formula, leading to a relation Δ(R)=Δ(11Rc/R)=uInt for the interfacial equilibrium condition. This relation together with mass conservation provides the characteristic length scale ρ and the height h of the inclusion

    h=ρR (7)
    ρ=128γ(1ν2)v2γ9πE(vαvγ)2 (8)

    Apparently, these expressions remain valid only when the elastic softening is pronounced and the assumption that neglecting the nonydrostatic aspect of the elastic behaviour in the inclusions is acceptable. However, within the approximative validity of these assumptions, we can consider the onset to be elastically dominated. The crucial point is the absence of any thermal quantities in ρ. This indicates that the pattern is dominated by elastic effects in this early growth regime. After the shape undergoes a Mullins-Sekerka instability, which takes place as derived in [30], it grows via the coupled diffusional-displacive mechanism, qualitatively comparable to the dendritic growth regime as we described it in [19]. In the next section, we introduce the regime of frustrated growth of bainitic subunits, which is represented by a limiting case of the elasto-chemical diffusion-limited growth, i.e., diffusion-limited growth mediated by an elastic selection.

    Within this section, we focus on the elasto-chemical diffusion-limited growth of a subunit in the terminal regime of carbon transport frustration, i.e., where an effective reduction of the thermodynamic driving force due to the size of the grown subunit can be assumed. The frustration of the carbon transport within the retained austenite is related to the continuous decrease of the overall temperature during the progression of the bainitic transformation. Upon a linear temperature decrease the carbon diffusion coefficient undergoes an exponential decay, as it obeys an Arrhenius-type temperature dependence. The combination of a reduced driving force and an exponentially decreasing diffusion coefficient suggest to study the zero velocity or zero diffusion limit.

    To take full advantage of our assumptions, the boundary integral method, as introduced in the methods section, see Eq 3 and in [19], is most suitable. The resulting formulation of the problem combines the governing equations of the elastostatic and transport problem into a closed representation for the unknown boundary, y(x), and the tip radius of curvature R:

    ΔdκR+TeqcpδFelL2=pπ+dxexp[p(y(x)y(x))]K0(pη(x,x)). (9)

    To simplify the representation, we introduce a splitting of the elastic contribution to the local equilibrium into a constant, hysteresis contribution Δel and the remaining, spatially varying contribution, for details we refer to [19]. Important here is to note that the Peclet number of the problem depends on the net driving force which takes the elastic hysteresis into account, i.e., ˜Δ=ΔΔel=pπexp(p)erfc(p). This includes that a strong elastic hysteresis leads to a substantially reduced driving force for the transformation, and we use approximations for the dependence of the effective driving force on the Peclet number and the Green's function of the problem as described in [19] to obtain the following representation:

    σκ+ΔelpΦ[σϵiku(α)ikEϵ2B(x)2(1υ2)]=1π+dxlog[(xx)2+(y(x)y(x))2(xx)2+(x22+x22)2]1/2. (10)

    Here we define Φ=2(1ν2)/(E(ϵ2yy+ϵ2zz+2νϵyyϵzz)), function B(x) includes the local contributions to the elastic free energy difference. The stability parameter of classical dendritic growth is σ=d0/pR. We note that the velocity and the radius of the asymptotically matching Ivantsov parabola are defined as

    υdD=2π2σ˜Δ4, (11)
    Rd=πσ˜Δ2. (12)

    As mentioned, the specific scenario we apply the boundary integral formulation for is a scenario of very slow diffusion, so that the interface propagates slowly. Then, the diffusion is sufficiently low to have the elastic effects dominate the transition. In the case of very small Δel/p, one shall exploit the possibilities of analytical methods, e.g., apply the complex matching method which yielded the solution of the classical dendritic problem, but for the limit of p/Δel 1, which represents the scenario of transport and frustrated transformation in the subunits, analytical approaches in the aforementioned spirit are not at hand, suggesting specific numerical consideration here.

    Therefore, we approximate this frustrated transformation regime via the limit of zero velocity and diffusion. Formally, we can reach the regime of a possibly negligible release of latent heat at the interface when υ0. The Peclet number is given for a fixed ˜Δ1 as

    υR2D=p˜Δ2, (13)

    which shows that for a given radius of the subunit, i.e., if R(˜Δ)1, the limiting case of a decreasing diffusion coefficient D0 is equivalent to υ0. As the exponential dependence of the carbon diffusion coefficient on the temperature provides a pragmatically reasonable approximation to the strong decay of carbon diffusion, we will consider if a scenario with R(˜Δ)1 can be predicted.

    We introduce a representation of the problem which suits this approach, namely

    dΔelRκ+Φ[σϵiku(α)ikEϵ2B(x,x)2(1ν2)]=1πpΔel+dxlog[(xx)2+(y(x)y(x))2(xx)2+(x22+x22)2]1/2. (14)
    Φ=2(1ν2)Eϵ2(ˉϵ2yy+ˉϵ2zzˉϵyyˉϵ2zz)

    and the corresponding solvability parameter reads as

    dΔelR=σel=1R2γ(1ν2)Eϵ2(ˉϵ2yy+ˉϵ2zzˉϵyyˉϵ2zz) (15)

    We note that we have introduced a solvability parameter which describes the growth regime without involving any diffusional transport parameters. This reflects the possible elastic selection on the formal level. We implemented a symmetric diffusion model instead of the one-sided model which is typically used for highly asymmetric carbon transport. Therefore, we note that the dependence of the stability parameter on the control parameter in the symmetric and one-sided model is qualitatively identical. The symmetric model implementation is numerically easier to handle in the regime of p/Δel0. The resulting calculations for pure shear can cover the regime up to p/Δel=0.05, but even at this already small value, an extrapolation to p/Δel0 remains inconclusive. As the plot in Figure 2 shows, we can at least assume that the hidden behaviour of the selection does not obey a linear law. Exemplarily, we discuss the result of a potentially present finite σel and the result for the case σel(p/Δel)1/2.

    Figure 2.  The close up of the regime up to p/Δel=0.5. The solid line was obtained in the asymptotic regime and matches the results obtained for Δel/p vs σ before, represented by the dashed line. The results for the asymptotic regime Δel/p remain inconclusive concerning the presence of a selection for zero velocity.

    A finite value of σel at zero p/Δel would lead to an elastic selection as

    R=dΔelσel1 (16)
    υ=2ΔelσelπDd˜Δ2˜Δ2, (17)

    where we note that the net driving force relative to the elastic hysteresis in our model is assumed to be reduced by the frustration of the transformation. A possible square-root dependence of the solvability parameter on p/Δel would lead to scaling laws for the transformation velocity and the radius as

    R1˜Δ, (18)
    υ~Δ3. (19)

    Despite of the inconclusive numerical results for the zero velocity limit concerning a finite σel, the results show that an elastically dominated terminal growth regime of the subunits is possible.

    We presented our recent work on the possibilities of elastically dominated regimes in diffusion-limited solid state transformations. Referencing the bainitic transformation, specifically the scenario of sheaf growth via subunit growth into the retained austenite, the very initial and the terminal growth regime are considered. For the early growth regime, we find that under strong assumptions for the elastic softening and effective isotropy of the onsetting phase, the problem can be considered analogous to a crack formation problem. This is in line with previously published investigations on melt inclusion growth published in [29]. When the Mullins-Sekerka instability sets the transition from this early, elastically governed growth regime to the coupled diffusive-displacive growth regime, described in [15,16,18,19], the transport of carbon in the retained austenite is assumed to be the limiting factor of subunit growth. Taking into account the decrease of the carbon diffusion coefficient with decreasing temperature suggests to consider the effect of a reduced diffusion coefficient. In combination with elastically frustrated growth, i.e., reduction of the effective driving force, the reduced diffusion coefficient suggests to study a zero velocity limit. The presented numerical results show the possibility of an elastically dominated terminal growth regime of subunits.

    N. T. would like to acknowledge financial support from Innovation Foundation For Postgraduate and Fundamental Research Funds of Central South University (Grant No. 1053320171074), Changsha, China. K. W. would like to acknowledge financial support from Chinese Scholarship Council (Grant No. 201706370221), China. X. Y., M. F. and C. H. acknowledge the support of the Deutsche Forschungsgemeinschaft under Project No. SPP 1713.

    All authors declare no conflict of interest in this paper.

    [1] New York Independent System Operator (2014) A Review of Distributed Energy Resources.
    [2] Farret FA, Godoy SM (2006) Integration of Alternative Sources of Energy. John Wiley Press, 301–332.
    [3] Ackerman T, Andersson G, Söder L (2001) Distributed generation: a definition. Electr Power Syst Res 57: 195–204. doi: 10.1016/S0378-7796(01)00101-8
    [4] Willis HL, Scott WG (2000) Distributed power generation: planning and evaluation. Crc Press.
    [5] Sandia National Laboratories and NRECA (2015) DOE/EPRI Electricity Storage Handbook.
    [6] Luo F, Meng K, Dong ZY, et al. (2015) Coordinated operational planning for wind farm with battery energy storage system. IEEE T Sustain Energ 6: 253–262. doi: 10.1109/TSTE.2014.2367550
    [7] International Electrotechnical Commission (2011) Electrical Energy Storage.
    [8] Farzin H, Fotuhi-Firuzabad M, Moeini-Aghtaie M (2017) A stochastic multi-objective framework for optimal scheduling of energy storage systems in microgrids. IEEE T Smart Grid 8: 117–127. doi: 10.1109/TSG.2016.2598678
    [9] Lazaroiu GC, Dumbrava V, Balaban G, et al. (2016) Stochastic optimization of microgrids with renewable and storage energy systems. International Conference on Environment and Electrical Engineering. IEEE.
    [10] Silvestre MLD, Graditi G, Ippolito MG, et al. (2011) Robust multi-objective optimal dispatch of distributed energy resources in micro-grids. PowerTech, 2011 IEEE Trondheim. IEEE, 1–5.
    [11] Agamah SU, Ekonomou L (2016) Peak demand shaving and load-levelling using a combination of bin packing and subset sum algorithms for electrical energy storage system scheduling. Iet Sci Meas Technol 10: 477–484. doi: 10.1049/iet-smt.2015.0218
    [12] Levron Y, Shmilovitz D (2012) Power systems' optimal peak-shaving applying secondary storage. Electr Pow Syst Res 89: 80–84. doi: 10.1016/j.epsr.2012.02.007
    [13] Jayasekara N, Wolfs P, Masoum MAS (2014) An optimal management strategy for distributed storages in distribution networks with high penetrations of PV. Electr Pow Syst Res 116: 147–157. doi: 10.1016/j.epsr.2014.05.010
    [14] Ippolito MG, Silvestre MLD, Sanseverino ER, et al. (2014) Multi-objective optimized management of electrical energy storage systems in an islanded network with renewable energy sources under different design scenarios. Energy 64: 648–662. doi: 10.1016/j.energy.2013.11.065
    [15] Rahmani-Andebili M (2017) Stochastic, adaptive, and dynamic control of energy storage systems integrated with renewable energy sources for power loss minimization. Renew Energ 113: 1462–1471. doi: 10.1016/j.renene.2017.07.005
    [16] Meirinhos JL, Rua DE, Carvalho LM, et al. (2017) Multi-temporal Optimal Power Flow for voltage control in MV networks using Distributed Energy Resources. Electr Pow Syst Res 146: 25–32. doi: 10.1016/j.epsr.2017.01.016
    [17] Hejazi H, Mohsenian-Rad H (2016) Energy storage planning in active distribution grids: a chance-constrained optimization with non-parametric probability functions. IEEE T Smart Grid, 1–13.
    [18] Rahmani-Andebili M, Shen H (2017) Cooperative distributed energy scheduling for smart homes applying stochastic model predictive control. IEEE International Conference on Communications. IEEE, 1–6.
    [19] Kargarian A, Hug G (2016) Optimal sizing of energy storage systems: a combination of hourly and intra-hour time perspectives. Iet Gener Transm Dis 10: 594–600. doi: 10.1049/iet-gtd.2015.0031
    [20] Kerdphol T, Qudaih Y, Mitani Y (2016) Optimum battery energy storage system using PSO considering dynamic demand response for microgrids. Int J Elec Power 83: 58–66. doi: 10.1016/j.ijepes.2016.03.064
    [21] Carpinelli G, Mottola F, Proto D (2016) Probabilistic sizing of battery energy storage when time-of-use pricing is applied. Electr Pow Syst Res 141: 73–83. doi: 10.1016/j.epsr.2016.07.013
    [22] Brown PD, Peas Lopes JA, Matos MA (2008) Optimization of pumped storage capacity in an isolated power system with large renewable penetration. IEEE T Power Syst 3: 523–531.
    [23] Wen S, Lan H, Fu Q, et al. (2015) Economic allocation for energy storage system considering wind power distribution. IEEE T Power Syst 30: 644–652.
    [24] Korpaas M, Holen AT, Hildrum R (2003) Operation and sizing of energy storage for wind power plants in a market system. Int J Elec Power 25: 599–606. doi: 10.1016/S0142-0615(03)00016-4
    [25] Correia PF, Jesus JMFD, Lemos JM (2014) Sizing of a pumped storage power plant in S. Miguel, Azores, using stochastic optimization. Electr Pow Syst Res 112: 20–26.
    [26] Arabali A, Ghofrani M, Etezadi-Amoli M, et al. (2013) Genetic-algorithm-based optimization approach for energy management. IEEE T Power Deliver 28: 162–170. doi: 10.1109/TPWRD.2012.2219598
    [27] Nick M, Cherkaoui R, Paolone M (2014) Optimal allocation of dispersed energy storage systems in active distribution networks for energy balance and grid support. IEEE T Power Syst 29: 2300–2310. doi: 10.1109/TPWRS.2014.2302020
    [28] Silvestre MLD, Graditi G, Sanseverino ER (2014) A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach. IEEE T Ind Inform 10: 152–162. doi: 10.1109/TII.2013.2272945
    [29] Yang P, Nehorai A (2014) Joint optimization of hybrid energy storage and generation capacity with renewable energy. IEEE T Smart Grid 5: 1566–1574. doi: 10.1109/TSG.2014.2313724
    [30] Abedi S, Alimardani A, Gharehpetian GB, et al. (2012) A comprehensive method for optimal power management and design of hybrid RES-based autonomous energy systems. Renew Sust Energ Rev 16: 1577–1587. doi: 10.1016/j.rser.2011.11.030
    [31] Meng N, Wang P, Wu H, et al. (2015) Optimal sizing of distributed generations in a connected DC micro-grid with hybrid energy storage system. Energy Conversion Congress and Exposition. IEEE, 3179–3183.
    [32] Erdinc O, Paterakis NG, Pappi IN, et al. (2015) A new perspective for sizing of distributed generation and energy storage for smart households under demand response. Appl Energ 143: 26–37. doi: 10.1016/j.apenergy.2015.01.025
    [33] Papaefthymiou SV, Papathanassiou SA (2014) Optimum sizing of wind-pumped-storage hybrid power stations in island systems. Renew Energ 64: 187–196. doi: 10.1016/j.renene.2013.10.047
    [34] Dugan RC (2016) Reference Guide. The Open Distribution System Simulator (OpenDSS). EPRI.
    [35] Dugan RC, McDermott TE (2011) An open source platform for collaborating on smart grid research. Power and Energy Society General Meeting. IEEE, 1–7.
    [36] Martinez-Velasco JA, Guerra G (2015) Analysis of large distribution networks with distributed energy resources. Ingeniare 23: 594–608.
    [37] Dugan RC, Taylor JA, Montenegro D (2017) Energy storage modeling for distribution planning. IEEE T Ind Appl 53: 954–962. doi: 10.1109/TIA.2016.2639455
    [38] Eisen MB, Spellman PT, Brown PO, et al. (1998) Cluster analysis and display of genome-wide expression patterns. P Natl Acad Sci USA 95: 14863–14868. doi: 10.1073/pnas.95.25.14863
    [39] Tsekouras GJ, Hatziargyriou ND, Dialynas EN (2007) Two-stage pattern recognition of load curves for classification of electricity customers. IEEE T Power Syst 22: 1120–1128. doi: 10.1109/TPWRS.2007.901287
    [40] Iglesias F, Kastner W (2013) Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6: 579–597. doi: 10.3390/en6020579
    [41] Chicco G, Napoli R, Postolache P, et al. (2003) Customer characterization for improving the tariff offer. IEEE T Power Syst 18: 381–387.
    [42] Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE T Acoustics Speech Signal Process 26: 43–49. doi: 10.1109/TASSP.1978.1163055
    [43] Calinski T, Harabasz J (1974) A dendrite method for cluster analysis. Commun Stat-Theor M 3: 1–27. doi: 10.1080/03610927408827109
    [44] Passino KM (2006) Biomimicry for optimization, control, and automation. IEEE T Automat Contr 51: 1406. doi: 10.1109/TAC.2006.878700
    [45] Michalewicz Z (1996) Genetic Algorithms + Data Structures = Evolution Programs. Springer, 347–348.
    [46] Yeh EC, Venkata SS, Sumic Z (1995) Improved distribution system planning using computational evolution. IEEE T Power Syst 11: 668–674.
    [47] Mendoza F, Bernal-Agustin JL, Domínguez-Navarro JA (2006) NSGA and SPEA applied to multiobjective design of power distribution systems. IEEE T Power Syst 21: 1938–1945. doi: 10.1109/TPWRS.2006.882469
    [48] Abido MA (2006) Multiobjective evolutionary algorithms for electric power dispatch problem. IEEE T Evolut Comput 10: 315–329. doi: 10.1109/TEVC.2005.857073
    [49] Buehren M (2007) MATLAB Library for Parallel Processing on Multiple Cores. Available from: http://www.mathworks.com.
    [50] Martinez JA, Guerra G (2014) Parallel Monte Carlo approach for distribution reliability assessment. Iet Gener Transm Dis 8: 1810–1819. doi: 10.1049/iet-gtd.2014.0075
    [51] Guerra G, Martinez JA (2016) Optimum allocation of distributed generation in multi-feeder systems using long term evaluation and assuming voltage-dependent loads. Sust Energ Grid Network 5: 13–26. doi: 10.1016/j.segan.2015.10.005
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