Etta Grover-Silva
email: etta.grover-silva@mines-paristech.fr

Robin Girard
email: robin.girard@mines-paristech.fr

George Kariniotakis
email: georges.kariniotakis@mines-paristech.fr

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

Keywords: Optimal power flow, storage, smart grids, renewable energy, distribution grid planning Nomenclature Parameters η

Optimal sizing and placement of distribution grid connected battery systems through an SOCP optimal power flow algorithm

C nom st,j,d Final nominal capacity of battery systems for each node j for each day d F inj Function describing the cost of power injected into the feeder at the substation F inv Function describing the cost of investment of the battery nominal capacity and power F O&M Function describing the cost of operation and maintenance of the battery devices F p Function describing the cost of losses within the system including PV curtailment F st,p Function describing the cost of battery losses due to charging/discharging efficiency

N nom st,j,d
The number of hours of nominal autonomy of the battery system at node j P nom st,j,d Final nominal power of battery systems for each node j for each day d P 0,t Active power flow at the substation P jk,t Active power of branch jk P pv,j,t Photovoltaic injection at node j at time t after eventual curtailment P st,j,t Power injected by battery devices connected at node j Q jk,t Reactive power of branch jk Q pv,j,t PV reactive power injection at node j after eventual curtailment S jk,t Apparent power of branch jk S pv,j,t PV apparent power flow at node j soc st,j,t State of charge of the storage unit as a cumulation of energy at node j and time step t V j,t Voltage at node j

Introduction

The increasing environmental concerns, is one of the main drivers behind the large scale development of distributed energy resources (DER) in electric distribution grids. This development involves connection of decentralized generators to the electric grid primarily photovoltaic (PV) and wind turbines and also micro-hydroelectric generators bring about new challenges for the distribution grid operators. Decentralized renewable energy generators can introduce bi-directional flow within the network, while their production is uncertain and variable due to its inherent dependence on weather conditions. Other specific challenges of the distribution grid include higher uncertainty due to reduced aggregation effects of DER generators, voltage profile deviation and increased power flow in electric lines. These challenges are generally localized therefore creating local voltage perturbations that may not be visible by the distribution operator.

Solutions to these challenges include infrastructure upgrades such as electric line reinforcement or automation and integration of smart grid functionalities such as on-line tap changers (OLTC), DER generation curtailment, storage devices, demand side management (DSM) [START_REF] Tuballa | A review of the development of smart grid technologies[END_REF]. Specific technologies related to flexibilities include privately owned grid connected batteries such as electric vehicles [START_REF] Amini | Simultaneous allocation of electric vehicles parking lots and distributed renewable resources in smart power distribution networks[END_REF] or larger grid operator owned storage used to improve overall economic exploitation of the feeder. Demand side management optimization in smart grids and efficient smart grid technologies have been thouroughly explored for a variety of use cases [START_REF] Ogunjuyigbe | User satisfactioninduced demand side load management in residential buildings with user budget constraint[END_REF] [4] [START_REF] Esen | Technoeconomic appraisal of a ground source heat pump system for a heating season in eastern turkey[END_REF] [6] [START_REF] Esen | Experimental evaluation of using various renewable energy sources for heating a greenhouse[END_REF]. Infrastructure upgrades are easily quantified. However, new control and flexibility functionality is difficult to quantify economically and integrate into the planning phase of distribution grids. The cost benefit analysis of varying smart grid technologies and management strategies will become more important as DER penetration increases in future distribution grid systems.

Grid storage elements are presented in the literature as a cost effective solution to deal with the above challenges in distribution grids with high DER penetration. A techno-economic analysis of energy storage elements as a solution to intermittency of DER is presented in [START_REF] Luo | Overview of current development in electrical energy storage technologies and the application potential in power system operation[END_REF]. That paper details the cost-effectiveness of different grid storage applications including regulation of transmission and distribution power quality, voltage regulation and control, energy management, smoothing of intermittent renewable energy production, energy back-up, peak shaving, etc. For each specific application, taking into account the operational strategy of the storage device is important when sizing and placing the unit.

The optimal sizing and placement of storage devices in distribution grids has been addressed through various mathematical modeling methods presented in the literature. The problem of calculating the optimal placement and size of storage devices of an electric grid is highly dimensional and non-convex. The resolution of this highly dimensional non-convex problem has been successful with multiple mathematical techniques including analytical techniques, classical techniques, artificial intelligence techniques and other miscellaneous techniques [START_REF] Prakash | Optimal sizing and siting techniques for distributed generation in distribution systems: A review[END_REF]. In a different review of energy storage allocation, four main categories are defined to solve this highly dimensional non-convex problem: analytical methods, mathematical programming, exhaustive search and heuristics [START_REF] Zidar | Review of energy storage allocation in power distribution networks: applications, methods and future research[END_REF]. The different existing methods are of different complexity, some being simple, i.e. based on an energy balance of the examined system to size the storage. However, for grid connected systems the placement involves analysis of the impact of storage devices to the grid. For this reason, techniques based on mathematical programming such as power flow and optimal power flow (OPF) are more appropriate. These methods can be used to simulate distribution system functionality with generators and storage devices while taking into account grid constraints as seen in [START_REF] Boroojeni | A theoretical bilevel control scheme for power networks with large-scale penetration of distributed renewable resources[END_REF]. OPF algorithms are capable of taking into account decision variables and therefore capable of analyzing active management of distribution grids. An example of an OPF that analyzes hosting capacity of an active distribution grid is found in [START_REF] Etherden | Increasing the hosting capacity of distribution networks by curtailment of renewable energy resources[END_REF], where curtailment strategies and dynamic line rating are explored to increase renewable energy penetration.

OPF algorithms are efficient at analyzing active distribution networks for operation and planning. The two primary problem resolution techniques for solving this highly dimensional non-convex problem include heuristic techniques or linear convex relaxations of the power flow equations. Heuristic algorithms have been used to solve the optimal placement and sizing of storage devices. For example, a two-step process with a master and a sub-problem is proposed in [START_REF] Nick | Optimal location and sizing of distributed storage systems in active distribution networks[END_REF]. This method firstly uses a heuristic algorithm to solve optimal placement and sizing of batteries. Secondly, a daily AC OPF multi-objective function takes into account optimal voltage control, minimization of network losses and total energy costs. Another paper presents a comprehensive sizing and siting algorithm using particle swarm optimization [START_REF] Qing | Optimal siting amp; sizing of battery energy storage system in active distribution network[END_REF]. A different type of heuristic method was used to simultaneously size and place storage units using an artificial bee colony algorithm with an objective function that forces each storage node to be as autonomous as possible [START_REF] El-Zonkoly | Optimal placement and schedule of multiple grid connected hybrid energy systems[END_REF]. Another heuristic method used to analyze grid connected storage for a multi-objective problem addressing both distribution grid and transmission grid objectives is found in [START_REF] Motalleb | Optimal placement and sizing of the storage supporting transmission and distribution networks[END_REF]. However, heuristic algorithms often require a larger calculation burden and are not guaranteed to converge to a global optimal solution as noted in [START_REF] Paudyal | Three-phase distribution opf in smart grids: Optimality versus computational burden[END_REF]. A mixed integer linear programming approach for complete DER portfolio sizing and placement is presented in [START_REF] Mashayekh | A mixed integer linear programming approach for optimal der portfolio, sizing, and placement in multi-energy microgrids[END_REF]. The mixed integer strategy uses linearized power flow equations and loss estimations. Mixed integer linear approximations are proven to be effective at solving the non-convex placement and sizing problem however the calculation time is high and scalability to large network sizes have not been addressed.

Convex relaxations of the power flow equations generally have a lower calculation burden. The relaxation of the power flow equation into a second order cone has already been theoretically explained and detailed mathematically in [START_REF] Gan | Convex relaxations and linear approximation for optimal power flow in multiphase radial networks[END_REF]. Papers addressing specifically optimal sizing and placement of storage devices using convex relaxations can be found in the literature. An impedance model was used to perform optimal placement and sizing in [START_REF] Nazaripouya | Optimal sizing and placement of battery energy storage in distribution system based on solar size for voltage regulation[END_REF]. A convex relaxation was used for optimal placement and sizing of batteries with a linearized DC power flow for transmission planning with a maximum investment cost [START_REF] Thrampoulidis | Optimal placement of distributed energy storage in power networks[END_REF]. This linearization is not accurate for the high R/X ratio found in the low voltage distribution sys-tems, which implies electrical losses that are non linear. The use of an AC OPF for optimal placement [START_REF] Ghofrani | A framework for optimal placement of energy storage units within a power system with high wind penetration[END_REF] or optimal sizing [START_REF] Sharma | Optimal sizing of battery energy storage systems in unbalanced distribution feeders[END_REF] are also found in the literature. In [START_REF] Li | Convex optimization for des planning and operation in radial distribution systems with high penetration of photovoltaic resources[END_REF] the authors explore a two-step process of sizing and placement of storage units through relaxed power flow equations. However, this sizing methodology calculates power and energy imbalances locally at PV nodes and sizes the battery systems to mitigate these imbalances. Therefore, this methodology sizes the battery systems to reduce PV injection when power quality issues become an issue. This sizing methodology does not compare the cost of storage elements to other cost-effective solutions such as curtailment. The algorithm also does not analyze the possible benefits of batteries participating in an electricity market. A second order cone program (SOCP) OPF algorithm is then used in the second step to site the sized battery systems.

While there are multiple sizing and placement algorithms (e.g. [START_REF] Nick | Optimal location and sizing of distributed storage systems in active distribution networks[END_REF], [START_REF] Qing | Optimal siting amp; sizing of battery energy storage system in active distribution network[END_REF], [START_REF] El-Zonkoly | Optimal placement and schedule of multiple grid connected hybrid energy systems[END_REF], [START_REF] Motalleb | Optimal placement and sizing of the storage supporting transmission and distribution networks[END_REF]), the objective function of each presented methodology varies significantly implying significant variation in the results of the optimization simulations. For example in [START_REF] Nick | Optimal location and sizing of distributed storage systems in active distribution networks[END_REF] the objective is composed of minimizing the energy injection, voltage deviation and network losses. In [START_REF] Qing | Optimal siting amp; sizing of battery energy storage system in active distribution network[END_REF] the objective is peak load shaving, improving voltage profile quality and providing active power adjustment capacity. In [START_REF] El-Zonkoly | Optimal placement and schedule of multiple grid connected hybrid energy systems[END_REF] the investment, replacement and operations costs of PV, diesel generators, and battery banks as well as slack bus power, cost of energy not served, losses and excess energy of HPVDS. In [START_REF] Motalleb | Optimal placement and sizing of the storage supporting transmission and distribution networks[END_REF] the cost of local generation, the cost of centralized generation, unit cost of storage and the unit cost of power conversion. Due to the fact that the exploitation of a battery system for a specific purpose can have significant effects on the optimal size and placement as stated in [START_REF] Luo | Overview of current development in electrical energy storage technologies and the application potential in power system operation[END_REF], the results of the sizing and placement of these studies are very difficult to compare.

The SOCP relaxation of the power flow equations is present in multiple articles in the literature for example [START_REF] Marley | Solving multiperiod opf problems using an ac-qp algorithm initialized with an socp relaxation[END_REF]. However, it has been proven to be inexact during periods of high penetration from decentralized production. An example of this could be high PV production and low loads during the summer season. In order to overcome the challenge of inaccurate convex relaxations [START_REF] Abdelouadoud | Optimal power flow of a distribution system based on increasingly tight cutting planes added to a second order cone relaxation[END_REF] presents an AC OPF algorithm that integrates linear cuts implemented in an iterative manner to ensure an exact and feasible relaxation of the power flow equations. In a followup work, this algorithm has then been developed into a multi-temporal one in order to more objectively evaluate the benefits of grid connected storage and other temporally dependent variables in [START_REF] Grover-Silva | Multi-temporal optimal power flow for assessing the renewable generation hosting capacity of an active distribution system[END_REF].

This paper proposes a novel methodology to simultaneously perform optimal sizing and placement of storage devices to the distribution grid from a techno-economical view by considering the investment cost of batteries weighted against the operational benefit. Operational control of active power of storage and PV inverters are modeled with a multi-temporal OPF. The objective function of the op- This methodology is capable of taking into account in detail the operational strategy of storage devices in order to make planning decisions about their sizing and placement. Therefore, it is effective for distribution grid planning applications with predefined operational strategies. The structure of this paper includes section II describing the optimal power flow algorithm, section III illustrating a case study of the proposed methodology and section IV stating significant conclusions. A flow chart of the papers organization is found in 1

Optimal power flow model

The proposed methodology relies on solving the optimization problem given by eqs. ( 1)- [START_REF] El-Zonkoly | Optimal placement and schedule of multiple grid connected hybrid energy systems[END_REF]. The objective function is the sum of the battery investment costs, operation and maintenance costs, the system losses and power imported at the feeder substation. The constraints of this model include the active power limits of PV systems defined by the maximum available power as a function of weather conditions eq. ( 2), apparent power limits of PV systems eqs. ( 3) and ( 4), power flow equations eqs. ( 5)- [START_REF] Esen | Experimental evaluation of using various renewable energy sources for heating a greenhouse[END_REF], relaxation of the current equation eqs. ( 8) and ( 9) to represent the power flow equations as a convex SOCP, voltage limits of each branch eq. ( 10), battery state of charge (SOC) constraint eq. ( 11) and daily nominal power and capacity value constraints eqs. ( 12)-( 14). Constraint eq. ( 15) limits the ratio of nominal power and nominal capacity to be appropriate for distribution grid storage elements managed on a daily basis. This constraint also allows for fast convergence of the algorithm.

min F inv + F O&M + F p + F st,p (1)
subject to:

0 ≤ P pv,j,t ≤ P pv,j,t (2)
S pv,j,t ≤ S pv,j,t

S pv,j,t = P 2 pv,j,t + Q 2 pv,j,t

P ij,t = P ld,j,t + J j=0 P jk,t + r ij ij,t + P pv,j,t + P st,j,t

Q ij,t = Q ld,j,t + J j=0 Q jk,t + x ij ij,t + Q pv,j,t (5)
υ j,t = υ i,t -2(r ij P ij,t + x ij Q ij,t ) + (r 2 ij + x 2 ij ) ij,t (6)
S ij,t ≥ P 2 ij,t + Q 2 ij,t (7)
S ij,t ≥ ij,t υ i,t (8)
V 2 ≤ υ j,t ≤ V 2 ( (9)
) 10
soc st,j,t = soc st,j,t-1 -t (P st,j,t + η st |P st,j,t |) (11)

P nom st,j,d ≥ |P st,j,t | (12)
C nom st,j,d ≥ soc st,j,t (13)
C nom st,j,d = N nom st,j,d P nom st,j,d (14) 0
.1 ≤ N nom st,j,d ≤ 8 (15)
The optimal sizing and placement of storage requires the resolution of a temporal and spatial problem. The temporal problem implies a coupling of multiple time steps to ensure coherence of the battery state of charge (SOC) between each consecutive time step. The spatial problem implies the consideration of all nodes as possible placement locations for storage devices. The multi-temporal OPF is already high dimensional. For a grid with 137 nodes, feasibility testing showed that up to 130 coupled time steps was returned results by the solver, any larger coupling returned a maximum size exceeded error. Therefore, a certain decoupling is necessary in order to complete an annual analysis. In this paper, a daily decoupling was implemented. Therefore, the number T of coupled timesteps is 24. A temporal decoupling is applied due to the size limitations of the optimization problem. The decoupling was chosen to be done on a daily basis due to the fact that battery systems are often managed on a daily basis. The coupled time steps of a one day period were then simulated for each day of the year in order to successfully complete an annual analysis. An additional constraint is added to avoid daily accumulation effects by forcing the state of charge (SOC) of the first and last time step of a day to be equal as stated in eq. [START_REF] Motalleb | Optimal placement and sizing of the storage supporting transmission and distribution networks[END_REF].

soc st,j,0 = soc st,j,T (16)
Constraint 14 is the product of two variables and is non-convex rendering the problem NP-hard. The variable N represents the number of hours of autonomy of the battery system, limited by feasible battery sizes of 0.1 to 8. This relationship between the nominal power and capacity of a battery system is an essential relationship for accurately representing the cost of battery systems. In order to keep this constraint, a linearization of is performed through an iterative process. The linearization is shown in equation 17.

N nom st,j,d P nom st,j,d = 1 2 N nom 0,j,d * P nom st,j,d + N nom st,j,d * P nom 0,j,d (17)
Where The algorithm effectively calculates the optimal size and placement of storage devices for each node for each day. This sizing and placement exercise therefore results in 365 optimal nominal capacity and power. The final optimal size must then be choosen from analyzing these 365 values. This is done by taking the 75th quantile of the set of optimal values to calculate a final annual optimal size.

Variations of the objective function

The objective function of the general form given by Eq. eq. ( 1) can be altered in order to size the battery systems for different purposes. Two objectives are considered in this Section. The first objective function is to size the battery systems to minimize losses in the system section. The second possible objective function considers the minimization of losses and the absolute value of the active power injection from the high voltage grid to the medium voltage grid at the substation.

Loss minimization

The first sizing exercise entails using the battery system only for loss minimization and allows a comparison between loss minimization eq. ( 21), losses associated with charging/discharging of the battery systems eq. ( 22) and the sum of battery investment costs eq. ( 19) and battery operations costs eq. ( 20). The objective function is therefore eq. ( 18). The optimal nominal power and capacity is then calculated based only on the economic viability of using batteries for loss reduction.

min

F inv + F O&M + F p + F st,p (18)
where:

F inv = J j=0 c inv st C nom st,j,d (19)
F O&M = J j=0 c om st tP nom st,j,d (20)
F p = T t=0 J j=0
c e,t r ij ij,t + c e,t t P pv,j,t -P pv,j,t (21)

F st,p = T t=0 J j=0 c e,t η st t|P st,j,t | (22)
subject to:eqs. ( 2)-( 15)

The losses considered are the line losses, PV curtailment and the losses of the battery system due to the battery charging efficiency. If the sum of the operational costs and the investment costs of the battery systems is higher than the economic gain from loss reduction, the algorithm eq. ( 18) will calculate zero nominal capacity and power for each node.

Minimization of absolute active power flow at substation

Battery systems can also be used to participate in variable pricing electricity markets. This implies using battery systems to buy and sell electricity from the grid based on the hourly price of electricity. This objective function as detailed in eq. ( 23) allows the calculation of economic gains through battery participation in variable pricing markets and encourages autonomy of the feeder by minimization of the total absolute active power flow imported at the substation |P 0,t |. This formulation does not include explicitly the losses associated with the battery charging efficiency because this energy is already counted in the variable |P 0,t |.

min j∈J F inv + F O&M + F p + F inj (23)
where:

F inj = T t=0 c e,t t|P 0,t | (24)
subject to:eqs. ( 2)- [START_REF] El-Zonkoly | Optimal placement and schedule of multiple grid connected hybrid energy systems[END_REF] These two objective function variations can be used to determine the size and placement of battery storage devices coupled with installed PV systems for specific enduse scenarios. This algorithm does not consider constraints to exclude very small battery systems. Therefore it is incapable of minimizing the number of nodes that battery systems are installed at. As a result the algorithm often sizes battery systems for every node. However, in some cases, it can be desirable by the DSO to consider a limited number of battery systems or a battery system size minimum. If a minimum battery size was introduced into the problem, it would introduce a binary constraint making the problem NP Hard. Therefore, to deal with these small infeasible systems sizes, an iterative approach can be taken. At a first stage, nodes with the larger battery sizes are identified with the initial sizing and placement algorithm. Then, final sizing can be performed with added constraints eqs. ( 25) and ( 26) as a second iteration.

C nom st,j,d = 0 f or J -J st (25)

P nom st,j,d = 0 f or J -J st (26)
The criteria for choosing final node placement can be determined by choosing a maximum number of battery systems or an acceptable maximum and minimum size of battery systems. For sizing based on a maximum number of systems, J st in eq. ( 26) represents the number of nodes n with the n largest values for the nominal capacity and power. For sizing based on an acceptable maximum and minimum size, J st represents the nodes with feasible nominal power and capacity values.

Results

Case study

The example grid used for this study is a medium voltage distribution grid published here [START_REF] Baran | Optimal capacitor placement on radial distribution systems[END_REF]. This grid is composed of 69 nodes with a nominal voltage of 12.66 kV and is assumed to be located in Nice, France. A map of the grid topology can be found in Fig 2.

Generation and load profiles

Electric load profiles were simulated using a load simulator as described in [START_REF] Barbier | A novel approach for electric load curve holistic modelling and simulation[END_REF] for each low voltage substation load profile. Residential and commercial load profiles are simulated with statistically accurate representations of surface area, electric heating and number of individuals that align with the INSEE household inventory database of France. The location of each load node was chosen randomly due to the fact that no grid load data was available. The medium voltage feeder is assumed to be a 10 MVA transformer serving 21 low voltage substations. Load profiles aligning with meteorological data in Nice, France indicated a peak load of 4.7 MW during the summer and 5.9 MW during the winter with an average load of respectively 2.1 MW and 2.6 MW. Solar radiation data was simulated for Nice, France for the year 2012 by analyzing the global irradiation collected by HelioClim 3 [START_REF] Blanc | The Helio-Clim Project: Surface Solar Irradiance Data for Climate Applications[END_REF]. The PV system production was calculated based on a statistical distribution of direct and diffuse irradiation [START_REF] Ruiz-Arias | Proposal of a regressive model for the hourly diffuse solar radiation under all sky conditions[END_REF]. This data is then integrated into a projection model to calculate the percentage of direct and diffuse irradiation exposed to the panels [START_REF] Muneer | Solar radiation and daylight models for the energy efficient design of buildings[END_REF]. A system performance coefficient is then calculated based on the atmospheric conditions extrapolated from a performance data base of PV systems in the south of France.

An amount of 10 PV systems were randomly assigned to 10 nodes. The size of these systems was also chosen randomly to be between 125 -1250 kW. Characteristics of the electric load profile nodes can be found in Fig. 3 and PV size information can be found in Figure 4.

Economic analysis

In order to analyze the economic viability of battery systems, market price variation and battery system costs The capital cost of batteries for nominal capacity and power is determined by analyzing the study [START_REF] Abbas | Doe/epri electricity storage handbook in collaboration with nreca[END_REF]. The nine case studies on high performance lead acid battery for transmission and distribution applications were analyzed to calculate the battery investment cost per MWh. Battery costs ranged from 500 ke/MWh to 2.5 Me/MWh. To integrate these costs into the daily analysis, the investment costs per MWh are divided by the lifetime of the system. For all economic analysis, the battery life is assumed to be 10 years as assumed also in [START_REF] Jim Eyer | Energy storage for the electricity grid: Benefits and market potential assessment guide[END_REF]. Therefore, the "daily" investment costs ranged from 137.6 e/MWh-day to 678.6 e/MWh-day. Due to the large range of battery investment costs, a sensitivity analysis was completed to determine the price of batteries that is economically viable. In [START_REF] Jim Eyer | Energy storage for the electricity grid: Benefits and market potential assessment guide[END_REF], operations and maintenance prices of the battery systems are given to be between 2-6 cents per kWh. For this study this cost is selected to be 2 cents/kWh.

Results

The two objective functions presented in eq. ( 18) and eq. ( 23) were analyzed to determine economically viable battery placement and sizing. In all considered scenarios, equation eq. [START_REF] Mashayekh | A mixed integer linear programming approach for optimal der portfolio, sizing, and placement in multi-energy microgrids[END_REF] showed that battery investments were not economically viable for only loss minimization with the considered PV penetration and load profiles. A sensitivity analysis of the battery costs was performed with objective function eq. ( 23) to analyze the economic viability of storage systems used for loss minimization in addition to market participation. Results of the nominal power and capacity specifications calculated for each node using objective function eq. ( 23) can be found in Fig. 5.

This sensitivity analysis compares the sum of total nominal power and capacity for a feeder in relation to different investment costs. These total nominal capacity and power values are a sum of the individual nominal capacity and power values for each node. The price of batteries calculated by this study to be economically viable are lower than the battery costs found in [START_REF] Abbas | Doe/epri electricity storage handbook in collaboration with nreca[END_REF]. For example, a battery with a capacity of 2 MWh and a nominal power rating [START_REF] Abbas | Doe/epri electricity storage handbook in collaboration with nreca[END_REF]. In the sensitivity analysis, if the investment costs of the battery are 85 e/MWh-day, the total ideal nominal capacity and power for the feeder is 2 MWh and 1 MW. This per day investment cost can be translated into an initial investment cost by taking into account a life time of 10 years. The calculated initial investment cost of this system is therefore 310.25 ke. This implies that for this battery to be economically viable, capital costs must be 7.2 times cheaper than the battery costs published in [START_REF] Abbas | Doe/epri electricity storage handbook in collaboration with nreca[END_REF].

For the specific example of a battery investment price of 85 e/MWh-day, a sensitivity analysis was performed comparing the PV penetration and associated battery size. This comparison is found in Fig. 6 This sensitivity analysis shows a high correlation between optimally sized battery systems and PV system size. Therefore grid connected battery systems become exponentially more economical with high penetration of DER. A comparison of centralized and decentralized optimally placed capacity is shown in Fig. 7. The centralized storage nodes are considered to be any node on the main branch of the grid topology tree. The list of centralized nodes for this network are therefore 0,1,2,3,4,5 and 6. All other nodes are considered to be decentralized placement nodes for storage devices.

As seen from this analysis, decentralized battery systems are prioritized for lower battery investment costs while centralized systems size is mostly stable over all investment costs. Decentralized battery systems are more prioritized when battery costs are low and overall total capacity and power installed is higher. When total capacity and power installed is smaller, the ratio of decentralized to centralized systems is also much smaller.

The partitioning of total nominal battery capacity and power as a function of battery investment cost is found in Fig. 8 to demonstrate the repartitioning of battery capacity and power.

As seen in 8, certain decentralized nodes including for example node 25, 34, 65 and 66 are prioritized for storage placement. These three nodes give three different examples of when storage is advantages. Node 25 is a priority due to the high nominal PV power installed at node 24 and also the fact that this system is at the end of an electric feeder. For the case of node 34, high nominal PV power at node 35 combined with high loading at node 34 makes this node a priority. Node 65 and 66 are two nodes relatively close to each other. Node 66 has a high load and node 65 has a large PV system. The algorithm assigned capacity to both nodes, however in all cases a larger capacity is assigned to node 65 than 66 prioritizing a placement closer to the PV system installation rather than the highly loaded node.

A closer look is taken into the case study with a battery cost of 85 e/MWh-day. The individual calculated sizes of battery systems for each node can be found in Fig. 9.

In Fig. 9 a nominal power and capacity value of storage devices is assigned to every node. This is due to the fact that many small systems allow to reduce losses and regulate the balanced of generation and consumption at each node. The size of the battery systems exponentially decreases when the nodes are ranked by size. This shows that certain nodes are high priority but having a storage at all nodes is ideal.

The objective function does not penalize small battery systems, therefore the infeasible small battery systems must be eliminated through an iterative qualitative analysis. The final optimal placement and sizing of battery systems can be calculated by limiting the possible nodes where battery systems can be placed. This selection is performed based on the initial sizing and placement analysis. For the case defined by 85 e/MWh-day, the total number of systems installed was fixed to be 10. Therefore, a final analysis is performed by using eq. ( 26) and setting fixed the set J st as seen in eq. ( 27). The final resulting ideal sizing is found in Fig. 10. 34, 48, 35, 36, 32, 47, 25, 24, 65 (27) After fixing the maximum number of battery systems to be 10, a similar total nominal power and capacity is sized by the algorithm. Due to limited number of nodes for the repartition, the size of each system is therefore significantly larger than when all nodes are considered. Annual analysis results are then calculated by fixing the upper bounds of the nominal power and capacity of each battery system based on the values graphed in Fig. 10 and calculated by fig. 10. The feeder operation with and without battery systems is shown in Table 1.

J st = 4,
The operation of the above feeder is affected by the presence of optimally placed and sized battery systems that allow for load shifting through pricing signals. The sum of imports and exports at the substation is therefore decreased. The first line in Table 1 shows that the optimized battery systems succeed to decrease the cost of imported energy. The level of exported energy shown in line two remains low due to the objective function that minimizes the absolute energy flow at the feeder. Line four shows a reduction in PV curtailment due to added battery systems therefore increasing PV benefits as shown in line three.

Algorithmic performance

The algorithmic performance of the simulation of a single time step and the full annual analysis has been observed as shown in Table 2.

The iterative approach to calculate P nom st,j,d and N nom st,j,d increases the daily analysis calculation time linearly depending on the number of iterations needed for convergence. The calculation time showed in 2 are therefore averages of all analysis performed.

Discussion

This algorithm is capable of calculating the placement and sizing of storage devices in a distribution grid. The size is sensitive to the investment cost of the batteries as shown by the sensitivity analysis. In the context of a project, the capital investment of the project may be lower than the ideal size of storage capacity and power. If an investment constraint exists, an investment constraint can be integrated into the optimization problem as seen in eq. [START_REF] Grover-Silva | Multi-temporal optimal power flow for assessing the renewable generation hosting capacity of an active distribution system[END_REF].

c inv st J j=0 C nom st,j,d ≤ I max (28)
This algorithm does not consider the planning of active demand as an alternative solution to electrochemical storage. While the algorithmic structure is very similar to modeling electrochemical storage elements, the calculation of the cost of demand side management is difficult. The costs are non-linear, client-dependent and data sets quantifying these costs are rare. Other possible solutions that were not explored include infrastructure upgrades.

A challenge identified in this paper was the capability to integrate non linear cost functions with respect to the nominal power and capacity of batteries. Another intersting improvement to the cost function could include the integration of variable battery technologies and the associated variable cost parameters. Future work can be done to integrate these non-linear characteristics into the SOCP convex relaxation algorithm.

Conclusion

This paper has successfully demonstrated an adaptation of a SOCP convex relaxation of the power flow equations for optimal sizing and placement of battery systems in a medium voltage distribution feeder. The proposed algorithm that simultaneously sizes and places battery systems can be effectively used to analyze the economic viability of operational case studies in comparison to investment and operational costs. The specific contributions of this paper include:

• A high performance algorithm for solving simultaneously the sizing and placement problem through decomposition into daily analysis and assessment of a typical year of operation of the system under study and taking into account the impact of the electrical network. This is a major contribution compared to the state of the art where these two problems of placement and sizing are often considered in a decoupled way

• A methodology to integrate operational case studies of battery management strategies into the planning phase of active distribution grids

• A qualitative study of battery investment costs and their operational benfits to make investment decisions about grid connected storage

• A demonstration of the increasing benefit of grid connected storage in the presence of high DER penetration This type of innovative algorithm gives insights into the advantages of grid connected storage devices in distribution systems and the integration of operational strategies into the planning phase.

Figure 1 :

 1 Figure 1: Paper organization

Figure 2 :

 2 Figure 2: Grid topology including low voltage substation (C) and PV system placement (PV)

Figure 3 :

 3 Figure 3: Load characteristics for all loaded nodes

Figure 4 :

 4 Figure 4: PV nominal power ratings for each PV node

Figure 5 :

 5 Figure 5: Total aggregated nominal capacity and power optimal system size as a function of battery costs

Figure 6 :

 6 Figure 6: Sensitivity analysis of PV penetration in relation to optimal aggregated nominal power and capacity battery size for an investment cost of 85 e/MWh-day

Figure 7 :Figure 8 :

 78 Figure 7: Comparison of centralized and decentralized nominal power (P) and capacity (C) optimal system size as a function of battery costs

Figure 9 :

 9 Figure 9: Calculated size of battery sizes for battery prices of 85 e/MWh-day

Figure 10 :

 10 Figure 10: Final size of selected nodes with a battery cost of 85 e/MWh-day

  N nom 0,j,d and P nom 0,j,d are initial values assigned. With these initial values P nom st,j,d and N nom st,j,d are calculated with the algorithm. If the difference between the initial estimation and the final calculated values is larger than a 0.001 for either P nom st,j,d or N nom st,j,d a new iteration is performed assigning N nom 0,j,d and P nom 0,j,d to the values of P nom

	or N nom st,j,d .	st,j,d

Table 1 :

 1 Operation of electrical feeder with and without battery integration. Annual analysis for the case of 85 e/MWh-day

	Characteristic	With battery Without battery
	Total Cost of En-	467	489
	ergy Imports (thou-
	sand e)
	Total Price of En-	1.64	2.13
	ergy Exports (thou-
	sand e)
	PV Benefits (thou-	334	314
	sand e)
	PV	Curtailement	1.05	1.39
	(MWh)
	Nominal PV Power	5.53	5.53
	Installed (MW)
	Nominal Batt Power	0.74	0.0
	Installed (MW)
	Nominal Batt Ca-	1.55	0.0
	pacity	Installed
	(MWh)

Table 2 :

 2 Calculation time for daily and annual analysis

	Algorithm	Simulated	Calculation
		time steps	time (s)
	multi-temporal	24	2.7
	SOCP OPF
	multi-temporal	24x365	2700
	SOCP OPF

Acknowledgment

The authors would like to thank Mr. Thibaut Barbier for his support with the simulated load curves. Authors would like to thank Dr. Miguel Heleno from Lawrence Berkeley National Lab for useful discussions on possible comparative studies and existing sizing and placement tools. This work is performed in the frame of the Horizon 2020 project Sensible (Grant Agreement 645963) funded in part by the European Commission under the Horizon 2020 Framework Program as well as a PhD scholarship grant supported by the French Energy agency ADEME (Agence de l'Environnement et de la Matrise de l'Energie).


Bertrand Caron
email: bertrand.caron@bnf.fr

Jordan De

La Houssaye

Thomas Ledoux
email: thomas.ledoux@bnf.fr

Stéphane Reecht
email: stephane.reecht@bnf.fr

Life and Death of an Information Package: Implementing the Lifecycle in a Multi-Purpose Preservation System

Keywords: Lessons learned, package lifecycle, information lifecycle, package update, package deletion, preservation policies, data enrichment, package Version, package Edition, OAIS interpretation, PREMIS Events

This paper aims to explain how the National Library of France (BnF) faced the question of the information lifecycle, during the implementation of its digital preservation system, in the light of the experience it acquired managing and using this system, from a theoretical approach following the OAIS reference model to an implementation. It was understood very early that from a preservation point of view the management of subsequent Versions and Editions of an Information Package was a particularly sensitive problem akin to risks management. However it was thought at the time that the system should be able to manage the lifecycle of an AIP (Archival Information Package) in a universal way that would be valid whatever the package considered and the context. These ideas quickly proved to be impractical and didn't resist the test of reality. Some improvements were provided, while taking into account the rest of the digital ecosystem of the BnF.

INTRODUCTION

Among the challenges an institution has to face when it puts into practice digital preservation, the management of updates of data isn't the easiest to face. It is necessary to conciliate precautions to avoid the loss of information, requirements for enabling enrichment of data, and financial sustainability of storage. The lifecycle of information has to be considered through the prism of risk management, therefore no turnkey solution is available for those who have to deal with this issue.

The BnF (National Library of France) is assigned with the mission of preserving over the long-term a part of the French cultural heritage, including in its digital forms. It has been building since 2007 its own digital preservation system. The issue of the packages lifecycle had to be taken into consideration.

In this paper we reflect on the history of digital preservation at the BnF in order to account for successive refinements that were made to our original assumptions regarding the lifecycle. In the first part, we detail the way the OAIS reference model was interpreted in order to make implementation choices in the SPAR system. In the second part we detail chronologically how the system originally managed any updates of AIPs, and how new use cases the BnF faced forced us to adapt our preservation system. In the third part we reflect on the way these improvements changed the lifecycle of AIPs in SPAR as well as the broader preservation environment at the BnF.

THINKING ABOUT A LIFECYCLE: WHERE, WHY, HOW? 2.1 The Context: SPAR and its Ecosystem

The BnF preservation system is named SPAR (Scalable Preservation and Archiving Repository). It is being built in house since 2007 and is in production since 2010. Its scope is to manage all entities that can be automated through modules corresponding to the OAIS entities. The system includes the management of the storage infrastructure.

In SPAR, the sets of documents to be ingested are processed by tracks and channels (sub-tracks), according to their nature (e.g., digitized books, audiovisual files, web archives, administrative records), their legal framework, and the way the BnF plans to apply preservation strategies. At the present time, SPAR can ingest objects through six tracks: digitized documents and associated files, audiovisual objects, web legal deposit (ARC or WARC files), negotiated legal deposit (ebooks and other born-digital documents), administrative records, acquisitions and donations (born-digital documents out of the scope of the legal deposit), and third-party archiving (various kinds of files, from partners outside the institution).

Each track has a leader (named a 'track manager'), someone in the library, generally a librarian, who is more than a representative. This person is responsible for preserving the collections, and is in charge of negotiations with the IT department. The result of the negotiations is formalized, for each channel of the track, in Service Level Agreements (SLAs) that formally rule the interaction between the Producer, the Archive and the Consumer (see Figure 1). More precisely, they define the terms of ingest, preservation and dissemination (e.g., formats accepted, maximum size of packages, availability of service, number of copies etc.). Each SLA is transcribed in XML files that configure the system. As of March, 2017, more than 7 million Archival Information Packages (AIPs) are preserved in the system, containing about 300 million files, for a volume of 3 Petabytes.

Track

Original Principles of the Implementation of OAIS Lifecycle Concepts

The SPAR system is strongly inspired, from the beginning, by OAIS [START_REF]Reference Model for an Open Archival Information System (OAIS). Recommended Practice[END_REF], and its implementation follows closely the principles of this standard1 . Therefore, not only the responsibilities, the data model, the entities and their functions were supposed to follow the reference model (part 3 and part 4), but also the practices described in the standard (part 5, Preservation perspectives).

Among the practices that the system had to take into account, was the answer to the question: what to do when an update of a package is proposed for ingestion? Previously to SPAR, updates were managed on a case-by-case basis, and generally the new digitized or digital document replaced the older one, that was erased on the storage capacity or became inaccessible. Updates didn't happen very often, but we foresaw that over time it would turn into one of the usual cases: quality improvement on the Producer's initiative (especially in digitization contexts), format migration for preservation purpose... The preservation system had to implement a lifecycle in order to prevent the loss of data and to enable the management of digital document at a large scale.

For this purpose, the OAIS standard offers some valuable concepts. Section 5.1 (and more precisely 5.1.3) distinguishes four types of Digital Migration that can affect the Archival Information Package (AIP) for preservation purposes. Refreshment and Replication are operations that do not affect the Content Information, the Packaging Information or the Preservation Description Information (PDI) of the package; they are directly handled by the Storage layer [START_REF] Ledoux | SPAR: From Design to Operations[END_REF] and are beyond the scope of this paper. Repackaging implies changes to the Packaging Information, and Transformation implies changes to the Content Information and/or to the PDI; both cases enter in the field of the lifecycle of the Information Package2 . The standard then specifies what the Archive is supposed to do (5.1.3.5): only in case of a Transformation, the Archive has to create a new AIP Version, i.e. an AIP that "is a candidate to replace the source AIP" (1.7.2). Concerning what to do with the replaced AIP, there is no constraining rule: "The first version of the AIP is referred to as the original AIP and may be retained for verification of information preservation. " (5.1.3.4).

In case of an upgrade or an improvement of the AIP, at the Producer's initiative, the OAIS standard specifies that "This is not a Digital Migration in that the intent is not to preserve information, but to increase or improve it." Thus, a new AIP Edition has to be created, but once more the standard leaves the choice to the Archive to retain or not the previous AIP (5.1.3.5). At the BnF, it has been understood that these choices were a matter of risk management regarding data loss.

Because implementation choices also had to be done, the interpretation for an AIP Edition was: increasing or improving information means adding Data Objects (for example: supplement image mode digitization with OCR files) or adding/modifying metadata (such as Descriptive Information, PDI. . . ). In every case, there is no necessity to preserve the previous AIP, because there are no risks of losing data, and because the SPAR system includes a lot of functionalities to avoid the ingestion of packages with metadata too poor for preservation. The understanding for an AIP Version was: modifying Data Objects can be thought of as a Transformation, and therefore leads to a new Version of the AIP. In this case, because Data Objects were changed, it has been considered by default that deleting the previous AIP Version was a risk: as the OAIS says that "the new AIP is viewed as a replacement for the source AIP" (5.1.3.5), it would be too big a responsibility for the Archive to remove the source AIP. Therefore a general preservation rule was defined: the first, the latest and the penultimate Version of a package has to be preserved ("0, N-1 & N Rule", see Figure 2), while a new Edition always removes the latest one.

As OAIS requires (sub-section 4.2.1.4.2), this part of the history of the package is recorded as Provenance Information. Moreover, every instance of the AIP is addressable at the level of the Reference Information, through its identifier: the ARK [START_REF] Kunze | The ARK Identifier Scheme[END_REF] identifier is systematically suffixed with two qualifiers, one to mark the Version and the other to mark the Edition. Initially, it appears in the form of version0.release03 . When an update occurs, the number of version or release, following the case, is increased for the new package; for example, if the first update produces a new version, the qualifiers for the new package are version1.release0. From a preservation point of view, this enables to apply automatically the "0, N-1 & N rule". From an Access point of view, this enables to establish a simple and global policy: if the Consumer asks for an AIP in absolute (i.e. only the ARK identifier without qualifiers), the system retrieves the last Version and Edition; if he asks for a particular Version or Edition (i.e. specifying qualifiers), the system retrieves the requested Version or Edition if it still exists.

In order to document and preserve PDI, Representation Information and Packaging Information, the BnF initially chose the METS format. A single METS file, called "manifest", is used as a wrapper for descriptive, technical and provenance metadata. For provenance metadata, PREMIS was chosen to document operations that affected the Content Information before and after ingestion. Among these operations are particular events occurring in the package lifecycle: SIP creation, ingestion and package update. When a package update is processed, the whole set of events which affects the Information Content from its creation is preserved as the package audit trail. This general policy, already defined in 2008 [START_REF] Bermès | Digital preservation at the National Library of France: A technical and organizational overview[END_REF], can be viewed as a simplification regarding what the reference model offers, but it presents the advantages of enabling an automated implementation.

THE LIFECYCLE ACTUAL IMPLEMENTATION AND ITS EVOLUTION 3.1 Initial Implementation

During the initial development of the SPAR system, a decision tree was implemented during the ingestion phase, in order to assess automatically if a package proposed for an update had to lead to a new Version, a new Edition or a failure. A quite complex sequence diagram was specified to describe SPAR's decision making, which occurs in an internal process of the ingestion phase named "ACT_10" (see Figure 3). This sequence combines the SIP manifest analysis and comparisons between the AIP and the SIP manifest. Some steps were a transcription of a simple rule (for example, if there is no fileSec in the SIP METS manifest, then it is only a metadata modification, and the system has to create a new Edition); some implied complex comparisons, should the SIP contain Data Objects.

Regarding the "0, N-1 & N" rule, it has not been implemented, so that every Version is preserved in the system. As for the Edition, the system is able to really remove the previous one.

As the system is in production since 2010, with the first track "Digitized documents for preservation", the BnF faced several use cases. That led to reconsidering the whole package lifecycle, not only in the SPAR system but also in the broader environment of digital management in the library.

A First Exception to the Automated Lifecycle: Requesting Explicitly for a New Version

In the context of mass-digitization, it often happens that a defect is detected after ingestion, or that digitized documents are supplemented with OCR, which causes a reprocessing of the package. It appeared that the automated mechanism was not sufficient to address with absolute confidence the different use cases, because of their complexity and variety. One use case is when, at the same time, the update consists in modifying the existing Data Objects and adding new ones, for example when a book digitized in black and white is newly digitized in color and OCRed. The system was then supposed to create at the same time a new Edition and a new Version, which means that the automated implementation of the lifecycle was not complete. At this point, when this case occurred, a risk existed that the system would create a new Edition and delete It was therefore decided that the decision to create or not a new version had to be taken outside of the preservation system, earlier in the workflow. A specific request functionality for a new Version was implemented, coming explicitly from the outer "delivery chain for digitization", that knows the context of the delivery or the update better than the preservation system. This request is submitted to SPAR by means of a PREMIS updateRequest Event, which keeps track of the request in the manifest after the update is processed.

It is interesting to note that in the context of mass digitization at the BnF, every subsequent digitization leads to a new Version of the target AIP. From an OAIS perspective, this suggests that a digitization is a kind of migration, and indeed, it can be argued that digitizing a book for example is a media migration from physical to digital. Consequently, each subsequent digitization can be thought as a new migration, albeit from the same original source, and therefore has to lead to a new Version of the corresponding AIP.

Requesting a Deletion of the AIP

The second evolution came directly from the integration of Administrative records. Indeed, handling archival processes forces taking into account the elimination of the content of a package. Regulation rules or laws make such destruction of information mandatory. However, since SPAR gives a permanent identifier to each package, it couldn't just erase the record: the elimination is thought as an update. In SPAR, updating a package requires a re-ingestion, in order to apply the policy defined in the SLAs (as the OAIS specifies that "AIPs should never be deleted unless allowed as part of an approved policy; there should be no ad-hoc deletions" 4 ). Here, this update will replace the actual content with a certificate of elimination, called "tombstone". This file contains the reason for the elimination as well as the requester and the authorizer. Doing so enables an appropriate response in case someone wants to retrieve this package.

Similarly to an update request, this request is submitted to SPAR by means of a PREMIS deletionRequest Event. In fact, the act of elimination is viewed as an update of the package with a new content (the 'tombstone') that will replace the latest one while erasing all the previous Versions of the package. This is the first time the system accepts that the original Version (Version 0) can be deleted. Of course, in order to keep such an operation safe, the permission of doing it must be given in the ingest SLA as well as who can initiate such a task and in which conditions.

At the end of the operations, a unique version is kept, the latest, containing the tombstone but also, in its manifest, all the audit trail of the operations applied to the package. No one can retrieve any previous content but anyone can be informed as to why it has been discarded.

It is worth noting that the result of a deletionRequest is a new Version of the AIP, whose content is the so-called "tombstone". It could be argued that it should be a new Edition, and not a new Version, because its Content Information has been upgraded (albeit not to improve it but to destroy it). However it has been understood as a Transformation (and therefore a Migration) because the Content Information has been altered and the resulting AIP is meant to replace the source AIP.

Requesting a Deletion with Redirection of the AIP

The previous evolution was fine from an archival perspective but, seen from a preservation one, losing the information content is not acceptable. In the context of the digitization programs, the same analog item might have been digitized by different means (old black and white digitization vs. new high resolution color one) and with different identifiers, generating sorts of duplicates. When keeping both is seen as unneeded, choosing the "best" one cannot be an automated process but is directly related to the management of the collection: the weeding procedure. In order to make it possible, the system was enhanced by adding to the deletionRequest the obligation of specifying another package. During the process of selection, the collection manager generates a tombstone not only stating the reasons for his decision but also providing the alternate package holding the same informational content. During the ingestion of this tombstone, the existence of the substitution package is verified. Then, in case someone wants to access the discarded package, the system will automatically redirect its request to the associated one. Once again the linear lifecycle is here clearly eluded but following a clear and stated decision. This functionality came from the needs of digitization, but turned out to be useful for other tracks, such as Web legal deposit, to also address the case of a Producer's mistake that causes a duplicate 4 see [1, section 3.2.5, p. [START_REF] Bermès | Digital preservation at the National Library of France: A technical and organizational overview[END_REF][START_REF] Caron | Experiment, Document & Decide: a Collaborative Approach to Preservation Planning at the BnF[END_REF][START_REF] Kunze | The ARK Identifier Scheme[END_REF] and that is detected after the ingestion of both packages. If the duplication is intentional, then the Producer can use the same identifier for both packages in order to create a new Edition of the package. In fact, this case matches exactly what OAIS calls "improvement" of information.

Requesting a New Edition Explicitly

Following the updateRequest, a new functionality was then implemented to address the case of data enrichment, for example when OCR files are added to still images. Here, new data and metadata are delivered simultaneously, and the automatic detection of a new Edition turns out to be very difficult. The explicit decision to create a new Edition (in PREMIS terms: replacementRequest) is now possible on the track manager's initiative. For consistency reasons, this functionality was implemented on the model of the updateRequest (see 3.2).

Requesting a Channel Switch

Given that every channel is ruled by SLAs defining ingestion, preservation and dissemination policies (see 2.1), the need for changing those policies for a package implies changing its channel. A channelSwitchRequest has been developed, resulting in a new Edition of the package in the target channel. This functionality can be viewed as a mix of a replacementRequest (as it is an explicit request for a new Edition) and a deletionRequest (as it also requires authorization, explanation and documentation). The Channel Switch is far from being a simple operation performed by an administrator; on the contrary, it means a new ingestion into the system, so it has to be allowed at the same time by the SLAs of the source channel and of the target channel. Switching a package from one channel to another only means moving it and not modifying it, consequently this operation must not imply a doubling of the storage size. That's why it results in a new Edition of the package.

While working on this matter, it was deemed beneficial to expand the possibility to document such an operation. When the switch is defined, it is now mandatory to indicate either the reference of a BnF internal document or the identifier of a preservation plan also preserved in the system [START_REF] Caron | Experiment, Document & Decide: a Collaborative Approach to Preservation Planning at the BnF[END_REF], that explains the decision of switching the package or a set of packages from one channel to another. In addition, in an iterative approach, this constraint has been extended to the deletionRequest functionality.

Almost ten years after its first version, the sequence diagram of the "ACT_10" process has been substantially enriched (see Figure 4). Now, the first steps involve detecting one of the four PREMIS Events that explicitly lead to a new Version or a new Edition of the package. Only after these steps begins the analysis from 2007, that has been simplified because some of the steps were no longer useful after implementation of the new functionalities. Thus, the risk that the system creates an unwanted Edition, is significantly reduced.

ENRICHMENT AND ENHANCEMENT OF DIGITAL CONTENT: LIFE BEYOND INGESTION

After having produced digital contents for more than twenty years, the BnF had to take into account the fact that its digitization policy is no longer limited to the creation of new digital copies of analog material, but also aims at enriching or replacing older digitization and adding new derived products. As a consequence, the package lifecycle, at first designed to be linear, is becoming circular (see Figure 5): Information Packages subjected to enrichment or enhancement have to be disseminated, reprocessed or enriched, and then ingested again. Services and systems which until then had no conscience of their role in digital information preservation (e.g., QA delivery chains, dissemination services, etc.) had to endorse responsibility for actions that would affect the Content Information and its quality. These services were affected by this new principle and had to evolve accordingly in order to ensure that no loss could affect the packages fixity and quality.

Dissemination

The act of disseminating an IP with the intention of enhancing some of its components or enriching it with new representations consequently appeared to be a critical phase in the package lifecycle.

Particularly, the context (date, reason, agents involved) had to be taken into account to determine the systems behavior when the SIP expected to update the corresponding AIP will be submitted. A new PREMIS Event, named disseminationCompletion, has been

Package Delivery

As the preservation system is unaware of the production context, specified by digitization contracts for example, the creation of a SIP for update had to be made before ingestion by services that have the understanding of the reason why an AIP had to be updated.

Policy about Versions Retainment

As previously mentioned, no policy was globally defined concerning the choice whether to retain or not previous package Versions. Preservation experts considered that this choice had to be made by people particularly aware of digital collections management. In the case of heritage digitization for example, the following policy was adopted:

(1) whenever a reprocessing due to quality failure in the course of an ongoing contract generated a new Producer package, delivery chains will request the preservation system for a new Version; (2) whereas, in the case of a new Producer package intended for enhancement of the existing AIP, delivery chains will request a new Edition.

At the end of the digitization contract, a general decision has to be made to define which policy should be adopted for previous Versions, taking into account the causes that led to requesting a new Producer package.

Managing the Risk of Quality Loss

Though creating AIPs with several SIPs delivered progressively is a common problem that Archives have to face for several years 6 , the iterative process of Data Objects production, based on previously produced Data Objects (OCR files produced from image files, or accessible Daisy files produced from OCR files) became extremely tricky when both production processes had to be managed simultaneously.

Indeed, the risk of quality loss arose because Producer packages intended for updates could be delivered concurrently from different Producers, either to enrich or to enhance the original AIP. The decision was taken to reject a SIP for update intended for enhancement if the corresponding AIP has been disseminated for enrichment.

Moreover, in order to warn contract managers if some case of quality loss should arise, the delivery chains have to compare the last AIP Version or Edition and the Version or Edition of the DIP used to generate the Producer package. Whenever the last AIP Version/Edition is more recent than that of the DIP, QA would raise a warning flag and alert contract managers and track managers.

CONCLUSION

In the course of our daily operations, two trends emerged:

First, we tend to explicit more and more our intentions in the SIP: if a new Edition or a new Version is required, we state as much explicitly. It is the business of the track manager to decide if such or such an action to the data should lead to a new Version, a new Edition or an entirely new Package. This trend reached the point where we started to invoke directly the business intent into SPAR.

Second, we now acknowledge that the question of the management of subsequent Versions should be treated track by track, as part of the broader question of preservation policy. This question is once again in the hands of the track manager.

The enrichment of the AIP lifecycle leads to putting our track managers more and more at the center of our daily operations. We foresee the time when this question of the AIP lifecycle will be treated explicitly in the SLAs of our tracks, alongside the treatments associated with business related actions on the packages.

From an OAIS point of view, this trend is perfectly logical. We first needed to build a working system, and therefore we took some shortcuts in our OAIS implementation. 10 years in the making, both the system and the business matured. SPAR is now clearly understood as the technical part of our OAIS, completed by the BnF as an organization dedicated to preserving part of the French cultural heritage.

Figure 1 :

 1 Figure 1: Relationships between tracks, channels, policies and Service Level Agreements (SLAs)

  a Data Object: update or deletion 1/ Update of metadata only (PDI, RI, etc.): addition, update, deletion 2/ Addition of a Data Object ark:/12148/1234bcd.version4.release2

Figure 2 :

 2 Figure 2: Original "0, N-1 & N" rule

ACT_10. 1 :Figure 3 :

 13 Figure 3: Simplified view of the decision tree to process a SIP for update (2008 version)

Figure 4 :

 4 Figure 4: Simplified view of the decision tree to process a SIP for update (2017 version)

Figure 5 :

 5 Figure 5: Circular lifecycle management

Unless otherwise stated, all the references in this section are from the OAIS.

Repackaging has not been on the agenda for the BnF yet, given the youth of the system.

The string "release" has been preferred to "edition", because the word "edition" has another meaning in the context of a library and was deemed too ambiguous.

The Consumer of the DIP happens, in this case, to be the Producer of the SIP for update.

In particular, the CCSDS standard PAIS (Producer-Archive Interface Specification)[START_REF]Producer-Archive Interface Specification (PAIS)[END_REF] addresses this need.

ACKNOWLEDGMENTS

The authors would like to thank all the members of the SPAR team for their commitment and their invaluable contributions, John Thompson for his proofreading, and Claire Caillet from the CNES as well as Claire Sibille from the SIAF for the fruitful talks about OAIS.