Yujun He

Marc Petit

Demand Response Scheduling to support Distribution Networks operation using Rolling Multi-period Optimization

Keywords: Demand response, distributed generation, distribution network, thermostatically controlled loads, rolling optimization

This paper presents an approach of demand response (DR) scheduling with thermostatically controlled loads (TCL) in a distribution grid with high penetration of distributed generations (DG). In this approach, household TCL are employed as flexibility resources to support the distribution network for mitigating voltage or congestion constraints. A two-stage rolling optimization based control scheme is proposed to determine the optimal operating status of flexible loads using the forecast of generation and demand in the distribution system. The proposed methodology is conducted in a distribution test feeder with realistic scenarios. The simulation results have shown the usefulness and efficiency of the proposed method in improving the network operation and increasing the hosting capacity of DG.

Introduction

The growth of renewable energy sources (RES) and new demand such as battery electric vehicles (BEV), will introduce massive impacts on distribution network operation [START_REF] Walling | Summary of Distributed Resources Impact on Power Delivery Systems[END_REF], such as voltage violation and congestion. In medium voltage (MV) networks the conventional voltage control approach using on-load tap changer (OTLC), based on the assumption of unidirectional power flow, is not designed for this change [START_REF] Hallberg | Active distribution system management: a key tool for the smooth integration of distributed generation[END_REF]. Additionally, in areas with large amount of RES and weak demand, some transformers at the connection between de transmission and distribution grids may be subjected to higher peak load during injection than during withdrawal from the transmission grid. Now distribution networks have to become (more) active to comply with the connection of large amount of DG. Demand response (DR) program is an attractive way to address this issue, as it can respond quickly with respect to the variation of DG [START_REF] Sullivan | Using Residential AC Load Control in Grid Operations: PG&E's Ancillary Service Pilot[END_REF]. Typically, most applications of DR are dedicated to the participation in the balancing markets or frequency regulation reserves because valorization markets still exist [START_REF] Codani | Financial shortfall for electricvehicles: Economic impacts of transmission system operators marketdesigns[END_REF]. Nevertheless, many references integrate the use of flexibility in distribution grid operation [e.g. [START_REF] Christakou | GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response[END_REF][START_REF] Zakariazadeh | A new approach for real time voltage control using demand response in an automated distribution system[END_REF][START_REF] Balasubramaniam | A MILP formulation for utility scale optimal demand side response[END_REF][START_REF] Karfopoulos | Distributed Coordination of Electric Vehicles Providing V2G Services[END_REF]. Demand response taking advantage of the thermal buffering capability of thermostatically controlled load (TCL) within residential buildings can be a flexibility methodology because buildings with high thermal inertia can absorb a remarkable amount of thermal energy for a shifted use. By dispatching the flexibility, the distribution system operator (DSO) can maximize the use of the current capacity of distribution system infrastructures and postpone investment for grid reinforcement. The optimal load dispatch issue in a distribution system can be divided into two sub-problems: how to optimize this part of load flow (e.g. flexible loads) in the distribution system to generate DR scheduling signal, and how to dispatch flexible loads in response to the DR scheduling signal without disturbing critically the comfort of end-users.

Many references intend to address the optimal power flow (OPF) problem which consists in managing diverse distributed energy resources (DER) in distribution systems. In [START_REF] Christakou | GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response[END_REF], the load control signal is based on a real time OPF at a snapshot. The multi-period OPF accounting for the time series coupling of some DER is proposed in [START_REF] Gill | Dynamic Optimal Power Flow for Active Distribution Networks[END_REF]. Due to the non-convex and nonlinear nature of the modeling of distribution system, the complexity of OPF increases remarkably as a function of the system scale. The solutions include linear approximations of the distribution system model [START_REF] Christakou | GECN: Primary Voltage Control for Active Distribution Networks via Real-Time Demand-Response[END_REF] or a proposal for distributed algorithms to solve OPF separately [START_REF] Zakariazadeh | A new approach for real time voltage control using demand response in an automated distribution system[END_REF]. Furthermore, an exact convex formulation of OPF can be found in [START_REF] Li | Exact convex relaxation of OPF for radial networks using branch flow model[END_REF][START_REF] Nick | An Exact Convex Formulation of the Optimal Power Flow in Radial Distribution Networks Including Transverse Components[END_REF][START_REF] Farivar | Branch Flow Model: Relaxations and Convexification -Part I[END_REF]. In this approach, a semi-definite condition is adopted to relax the non-convex constraint in the model and formulate OPF as second order cone programming (SOCP). However, those references only study SOCP with a static view, so it is not able to tackle with the flexibility dispatch issue with time domain coupling. The SOCP based OPF in time-series can be found in [START_REF] De Oca | Optimal Demand Response in distribution networks with several energy retail companies[END_REF]- [START_REF] Shi | Optimal Residential Demand Response in Distribution Networks[END_REF]. In [START_REF] De Oca | Optimal Demand Response in distribution networks with several energy retail companies[END_REF], the authors address a DR scheme using the SOCP based OPF in the day-ahead retail market. In [START_REF] Shi | Optimal Residential Demand Response in Distribution Networks[END_REF], the SOCP based OPF is combined with a household energy management system and solved in a distributed way in order to keep the privacy of users. The stochastic method in this context has to be developed to consider uncertainties associated with generation and demand.

From a bottom-up view, the flexibility dispatch is usually formulated as a decision making problem by operating the individual load status to minimize the overall costs of loads [START_REF] Remani | Load scheduling problems under demand response schemes: A survey[END_REF]. A detailed modeling of TCL in participating in demand response scheme can be found in [START_REF] Kamgarpour | Modeling options for demand side participation of thermostatically controlled loads[END_REF]. The flexible load dispatch is formulated as a mixed integer linear programming (MILP) in [START_REF] Wang | Efficient real-time residential energy management through MILP based rolling horizon optimization[END_REF]- [START_REF] Balasubramaniam | A MILP formulation for utility scale optimal demand side response[END_REF]. In [START_REF] Balasubramaniam | A MILP formulation for utility scale optimal demand side response[END_REF], a MILP formulation with detailed modeling of an aggregation of flexible loads is proposed for utility scale DR scheme, but the thermal appliances are not considered. In [START_REF] Roh | Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid[END_REF], the authors proposed a flexible demand model based on thermal appliances, and a formulation as a mixed integer non-linear programming (MINLP) for smart grid applications. In [START_REF] Karfopoulos | Distributed Coordination of Electric Vehicles Providing V2G Services[END_REF], a scheduling of electric vehicle charging is formulated in order to limit the impact on the distribution network in terms of power losses and voltage violation. In [START_REF] Siano | Assessing the benefits of residential demand response in a real time distribution energy market[END_REF], the DR program with thermal and shiftable loads is used for alleviating distribution network congestions and cost savings for end-user. In [START_REF] Ghasemi | Integration of nodal hourly pricing in day-ahead SDC (smart distribution company) optimization framework to effectively activate demand response[END_REF], the nodal hourly pricing in day-ahead optimization framework of DR is proposed. The issue of flexibility dispatch is usually coupled with the generation of intermittent generation like wind turbine or PV in [START_REF] Fang | Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities[END_REF]- [START_REF] Mahmoudi | Modelling demand response aggregator behavior in wind power offering strategies[END_REF]. Due to the uncertainty of DG and demand, the day-ahead optimal scheduling might be inaccurate from the day-ahead forecast [START_REF] Ding | Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets[END_REF]. Probabilistic methods or stochastic optimization techniques can be employed as in [START_REF] Yang | Stochastic security-constrained economic dispatch for random responsive priceelastic load and wind power[END_REF]. The rolling optimization, also known as moving horizon optimization, is capable of solving real time decision making problem and proved to be effective [START_REF] Ding | Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets[END_REF][START_REF] O'connell | Rolling Multi-Period Optimization to Control Electric Vehicle Charging in Distribution Networks[END_REF]. In [START_REF] Wang | Efficient real-time residential energy management through MILP based rolling horizon optimization[END_REF], the load dispatch formulated as a MILP problem is solved by a rolling optimization based approach with a real time pricing policy. In [START_REF] O'connell | Rolling Multi-Period Optimization to Control Electric Vehicle Charging in Distribution Networks[END_REF], the charging schedule of electric vehicles (EV) is obtained from a rolling optimization approach considering the uncertain behavior of users.

Although there are quite a lot of researches studying these two aspects separately, few of them have considered the rolling optimization based flexibility dispatch that is integrated in a SOCP based OPF model. This paper proposes a rolling optimization based approach to dispatch the flexibility from household TCL in order to mitigate distribution grid constraints. The flexibility dispatch is formulated as a two-stage optimization: the first stage is to solve an OPF in a distribution network with a rolling horizon scheme; the second stage is to solve the flexible load dispatch at the aggregator level using a TCL model. The rolling optimization, which periodically updates input data information, is implemented in order to reduce the uncertainty associated with generation and consumption. The OPF problem is formulated using the branch load flow model considering conic relaxation. The SOCP based OPF is an exact convex formulation that can greatly reduce the complexity of network model while no approximation is introduced.

The major contributions of the proposed approach are that 1) it provides a two-stage optimization framework with SOCP-based OPF and detailed flexible load model to solve the flexibility dispatch problem in distribution system; and 2) it proposes a rolling robust optimization tool to tackle with the uncertainty associated with generation of RES and consumption. With a certain vision, the rolling optimization can effectively adjust forecasting errors and maximize the benefits from load scheduling.

This paper is structured as follows: the section 2 outlines system modeling including the flexibility scheme, the modeling of household flexible load, and distribution network model. The section 3 presents the rolling optimization based dispatch approach for flexible load. Then, the proposed approach is carried out on a realistic test distribution network described in the section 4 and simulation results are discussed in the section 5. Finally, conclusions and opened issues are drawn in the section 6.

System Modeling

Architecture of household TCL management in the distribution grid

Smart grids aim at making distribution networks with high penetration of DG more flexible to mitigate critical conditions including 1) large reverse power flow to the transmission system, 2) voltage constraints and 3) line congestions, for the purpose of postponing the grid reinforcement. The organization of household flexibility scheduling in a distribution network is illustrated in Fig. 1. At first a state estimator detects network constraints and then a control center will operate OLTC, DG and sources of flexibility in a centralized way based on the current network state and forecasting of generation and consumption. A local flexible load aggregator is helpful in the management of household flexibility as most flexible loads are small-scaled and decentralized. The load aggregator can communicate with DSO the capabilities to increase or decrease the demand during a certain period and the associated costs. In addition, the load aggregator can offer a better load curve by smoothing the profiles from a massive number of flexible loads. As the intermediary between DSO and end-users, the load aggregator is might better negotiate with DSO for incentives.

In fact, the DSO needs for flexibility will depend on the location of the constraint. So the load aggregator has to control the flexible demand in a nodal approach. A similar structure of nodal DR management can be found in [START_REF] Vanet | Distributed intelligence and hierarchical approach of distributed balancing markets in Smart Grids[END_REF]. Therefore, some smart grid infrastructures are required to permit two-way communication between the load aggregator and flexible loads and enable the real-time control. The sources of household flexibility consist of load shedding, load shifting, and TCL. The second refers to devices that have to complete a prescribed energy requirement with a time limitation, such as electric vehicle charging or washing machine. The TCL refers to appliances with a thermal inertia. This paper only considers the energy management of TCL in residential buildings.

Modeling of household TCL

First, we introduce a household heating device (Fig 2 ), which is modeled as a first order system with a state variable denoting the indoor temperature and a binary control variable denoting the electric power. and are respectively the thermal resistance and thermal capacity of the room. The ambient temperature depends on the external environment.

is switched from 0 to (and inversely) to keep within the temperature ranges [ , ]. The dynamics of in (1) can be found in [START_REF] Saker | Demand side management of electrical water heaters and evaluation of the Cold Load Pick-Up characteristics (CLPU)[END_REF], where is the rated power of appliance. The detailed parameters are presented in table 1. In this paper, we are interested in the nodal flexibility in a MV distribution network. Hereafter the nodal flexibility of thermal appliances is presented by an aggregate model of N heating devices with homogenous characteristics located behind a MV/LV transformer. Let N denote the number of appliances at the bus, and denote the vector of and of N appliances. We define the average internal temperature = Σ / and aggregate power = Σ as two output variables of the aggregate load model. Thus, this aggregate model can be expressed in the state-space representation in [START_REF] Hallberg | Active distribution system management: a key tool for the smooth integration of distributed generation[END_REF], where A, B, C, D are matrices that can be computed from [START_REF] Walling | Summary of Distributed Resources Impact on Power Delivery Systems[END_REF].

( ) ( ) ( ) ( ) ( ) t P C t t C R t in th in th th in ⋅ + - = 1 1 0 θ θ θ & (1)
R th θ 0 θ in (t) P in (t) C th ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )              ⋅ + ⋅ =             ⋅ + ⋅ = t t D t C t t P t t B t A t in in av agg in in in 0 0 θ θ θ P θ P θ θ & (2)
It can be demonstrated that this system is observable and controllable. Anyhow, the control of this aggregate model could be realized for the given output and at the moment t but the detailed control strategy is not in the scope of this paper. Hereafter, we focus on seeking the optimal setpoint of P agg (t) and θ av (t) as they determine the available flexibility of the aggregate TCLs. The evolution of aggregate load model for the timeslot t is obtained by summing up the dynamics in (1) and transformed to a discrete format with a time step ∆t.

th t agg th th t t av th th t av NC P t C R t C R t , , 0 , 1 , 1 ⋅ ∆ + ⋅ ∆ + ⋅         ∆ - = + θ θ θ (3)
From ( 3), the evolution of aggregate model can be determined with the setpoint of P agg (t) and θ av (t) and the environment variable θ 0 (t). P agg (t) can be varied continuously within [0, ] as long as N is large enough.

θ av (t) is limited within [ , ]
, because it is a necessary condition for limiting all the variables of within this range. The setpoint of θ av (t) is preset to ! = + /2. A daily power demand profile of this aggregate load model with a typical ambient temperature pattern is shown in Fig. 3. The detailed parameters of heating advice can be found in table 1 (issued from [START_REF] Saker | Demand side management of electrical water heaters and evaluation of the Cold Load Pick-Up characteristics (CLPU)[END_REF]). It can be seen that the power demand is influenced by the ambient temperature . With higher ambient temperature, the heating advices consume less electric energy for keeping the indoor temperature, and vice versa. This can be used to model the dynamics of homogeneous loads. When including heterogeneous loads, we can partition them using clustering methods according to their parameters and model them separately, which would be studied in future work.

Power of N thermal appliances

Modeling of distribution network

The OPF in distribution networks is formulated using a branch flow model introduced in [START_REF] Farivar | Branch Flow Model: Relaxations and Convexification -Part I[END_REF]. Let B denote the set of busses (i) and L denote the set of branches (i,j). For each branch (i,j), let $ %, = & %, + '( %, denote the complex impedance of the branch, ) %, denote the complex current and * %, = %, + '+ %, denote the complex power flow from buses i to j at time slot t. For each bus (i), , , denote the complex voltage and * , = , + '+ , denote the complex bus load.

The whole power demand at bus i is divided into three components: unflexible demand, flexible demand (which is modeled in subsection 2.2), and DG connected at this bus, as shown in [START_REF] Codani | Financial shortfall for electricvehicles: Economic impacts of transmission system operators marketdesigns[END_REF]. The unflexible demand doesn't participate in the demand response program and its power profile comes from a public database of Enedis (the main DSO in France) for the region of Provence in France.

   - + = - + = t i DG t i agg t i un t i t i DG t i agg t i un t i Q Q Q Q P P P P , , , , , , , , , , , , , , (4)
The electric circuit characteristics of the radial distribution network are presented by the following equations:

t ij ij t j t i I z V V , , , = - (5) * , ; , t ij t i t ij I V S = (6) t j k j t jk t ij ij ij t ij s S I z z S , ) ; ( , 2 , , = - - ∑ ∈L (7)
By expressing ( 5)-( 7) in terms of real variables, it becomes:

t j k j t jk t ij ij t ij P P l r P , ) ; ( , , , = - - ∑ ∈L (8) t j k j t jk t ij ij t ij Q Q l x Q , ) ; ( , , , = - - ∑ ∈L (9) ( ) ( ) t ij ij ij t ij ij t ij ij t i t j l x r Q x P r v v , 2 2 , , , , 2 ⋅ + + + - = (10) t i t ij t ij t ij v Q P l , 2 , 2 , , + = (11)
where -%, = .) %, .²and 0 , = ., , .².

The computational burden is considerable due to the non-convex nature of the described network model. Thus, a semi-definite relaxation of (11) to a convex condition is implemented in order to formulate a SOCP [START_REF] Farivar | Branch Flow Model: Relaxations and Convexification -Part I[END_REF].

t i t ij t ij t ij v Q P l , 2 , 2 , , + ≥ (12)
The HV/MV transformer with on load tap changer (OLTC) can be switched for keeping the voltage magnitude at lower side within a preset dead band. In this paper an ideal equivalent transformer model is used to adjust the voltage at slack bus [START_REF] Kamgarpour | Modeling options for demand side participation of thermostatically controlled loads[END_REF], and the tap position must be within limits [START_REF] Remani | Load scheduling problems under demand response schemes: A survey[END_REF].

( ) 0 2 , slack t t slack v Tap n v = (13) max min Tap Tap Tap t ≤ ≤ (14)
Besides, the bus voltage and branch apparent power must be kept within system secure constraints ( 15) and ( 16).

V v V t i ≤ ≤ (15) 2 max 2 , 2 , S Q P t ij t ij ≤ + (16)
The DG connected in the distribution network includes wind turbines (WT) and roof-top PV. It is assumed that the maximum power point tracking (MPPT) technique is used to determine maximum active power 12, , from RES at timeslot t in [START_REF] Balasubramaniam | A MILP formulation for utility scale optimal demand side response[END_REF], where 3 , depends on weather conditions (e.g. wind speed and solar insolation). The active and reactive power regulation of DG at the bus i is expressed in ( 18)- [START_REF] Roh | Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid[END_REF].

Q Q Q ≤ ≤ (19) 2 max , 2 , , 2 , , i DG t i DG t i DG S Q P ≤ + (20)

Rolling optimization formulation for TCL scheduling

The rolling optimization is an effective approach to make real time decision of the load dispatch. The procedures of rolling optimization of flexible load dispatch are described as follows:

1) The total scheduling horizon from 0 to T is determined as 24 hours of a day.

2) At the first time step, compute the OPF over the forecasting horizon 4 and generate a set of scheduling of flexible load. Only the scheduling at real time period is ever implemented.

3) At each time step, move one step ahead the forecasting horizon and repeat the step 2) until the end of the day based on current operational status and updated information and generate the a new set of load dispatch. It is assumed in this work that new forecasts are available every 30 min.

) ( ) ( ) [ ] τ τ + ∈ ≤         + ∑ + + = 0 0 1 , , 0 . . , min 0 0 0 t t t x G t s x F x F t t t t t t x t (21)
where 5 ( 6 is the objective at the current timeslot and 5 7 ( the forecast of objective at future timeslots, 8 ( is the set of constraints at each timeslot.

The detailed formulations at distribution network level and load aggregator level are presented as follows.

Stage of optimization at distribution network level

The OPF for distribution network in this paper aims at minimizing power losses in the feeder while maximizing the generation from RES subject to system secure constraints.

Firstly a load flow calculation is run within the optimization window [ , + 4] according to the demand and RES generation forecastings. From these results, the set of timeslots T crt are identified during which there are critical conditions on the network that are due to RES or peak loads.

Then the OPF is carried out to determine the setpoint of DG, OLTC, and DR at each timeslot. The objective function of OPF to be minimized is composed of power losses on the lines, DG curtailment and reverse active power at the slack bus for each timeslot t as expressed in [START_REF] Fang | Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities[END_REF].

(

)

[ ] τ + ∈ ∈ ∈         - + - + ⋅ + ∑ ∑ 0 0 ) , ( , 3 , , max , , 2 , 1 , ) , ( , min
t t k j i P c P P c l r c j i t slack k t k DG t k DG t ij ij t G, L, (22)
where G is the set of busses with a dispatchable DG, the notation |x| + =x if x≥0, |x| + =0 if x<0; 9: ;<, is the active power import at slack bus and negative value of 9: ;<, means the electrical power is fed back towards the higher voltage grid; = > , = ? and = @ are weighting coefficients of each objective. In this study, the choice of the weighting factors is identical, i.e. = > = = ? = = @ = 1/3.

The OPF is subjected to the network equality and inequality constraints including ( 4), ( 8), ( 9), ( 10), ( 12), ( 13), ( 14), ( 15), ( 16), ( 17), ( 18), [START_REF] Siano | Assessing the benefits of residential demand response in a real time distribution energy market[END_REF], and [START_REF] Roh | Residential Demand Response Scheduling With Multiclass Appliances in the Smart Grid[END_REF]. It is noticed that the control variable of DR , , appears in the equation [START_REF] Sullivan | Using Residential AC Load Control in Grid Operations: PG&E's Ancillary Service Pilot[END_REF]. Let , C denote the vector at bus i obtained from the results of OPF.

, C will be used as a reference of the optimization at load aggregator. In fact, the need of flexibility for DSO in terms of DR duration and magnitude has been modulated in T crt and , C . Then the DSO sends T crt and , C of each bus to the load aggregators for determining the DR scheduling at bus i.

Stage of optimization at load aggregator level

The optimization run by a load aggregator is targeted to keep the temperature comfort of thermal appliances and respond to the DSO need for flexibility. As described in the previous section, the control of thermal appliances can be realized with the setpoint and . Let , and , denote respectively the vector of and over the optimization window at bus i. The objective function of DR aims at minimizing the power deviation from the setpoint of OPF during timeslots of T crt and minimizing the influence on the temperature comfort during the other timeslots, as expressed in [START_REF] Ali | Increased Utilization of Wind Generation by Coordinating the Demand Response and Real-time Thermal Rating[END_REF]: [START_REF] Ali | Increased Utilization of Wind Generation by Coordinating the Demand Response and Real-time Thermal Rating[END_REF] where a 1 is a 4-element vector and D > is the element of a 1 . The optimization of thermal appliances is subject to the equality constraint in (3) as well as the inequality constraints in [START_REF] Mahmoudi | Modelling demand response aggregator behavior in wind power offering strategies[END_REF] [START_REF] Ding | Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets[END_REF] The lower and upper limits , and , are determined by the number of thermal appliances at bus i, i.e. , = 0 and , = . The optimal setpoint of DR is denoted , EF and , EF . It is noticed that there might be some deviations between , C and , EF due to operational constraints of DR. Therefore, the DSO has to relaunch the OPF using a fixed value , EF from optimization of DR to obtain the new optimal setpoint of DG.

( ) ( ) ( ) [ ] τ θ + ∈ ∉ = ∈ = ∈       - ⋅ - + - ⋅ 0 0 2 , 1 2 ,

Flow chart of proposed DR scheduling

The procedure of DR scheduling is organized in Fig. 5. The rolling optimization based control runs for each timeslot over the horizon T. At each timeslot , the optimization window covers [ , +4]. The stages of optimization on the level of distribution network and load aggregator are carried out consecutively to obtain the vector of control variables , EF and , EF . Only the optimal value of DR at the first timeslot is implemented.

The optimal value of DG regulation obtained at timeslot has to be renewed with the output of load aggregator before being used by their local controller.

Case Study

Test distribution network setting

To demonstrate the usefulness and effectiveness of proposed method, we perform simulation using an IEEE 34-bus test feeder in Fig. 6 with several modifications. Typical values of the French network have been used for cabled lines and overhead lines parameters. The total demand on the feeder is up to 9.52MW. The detailed characteristics of this test network can be found in [START_REF] He | Valorization of Demand Response for Voltage Control in MV Distribution Grids with distributed generation[END_REF]. The RES connected in this feeder include WT and rooftop PV. We assume two WTs, each with installed power of 4MW, are connected within the feeder and controllable through an agreement between the DSO and the WT operator. The reactive power of WT is The OLTC has 17 tap positions to vary the transformer voltage from -10% to +10%, so with each change of tap position the voltage variation is 1.25%=0.0125pu. The OLTC is set to keep the bus voltage at substation within [1.02pu, 1.04pu], which is commonly used by French DSO.

Simulation scenarios

The forecasting patterns of unflexible demand, wind and PV generations are shown in Fig. 7. It can be seen that during 13-17h, there is reverse power flow to the transmission system due to the large penetration of RES. The temperature pattern used in the simulation has been shown in Fig. 3. The time step is set to ∆t = 30min, so the total horizon T= 48 timeslots for the whole day simulation. A forecasting window of 6h which is equal to 12 timeslots is adopted in the rolling optimization. The simulations are conducted in four scenarios to demonstrate the effectiveness and efficiency of proposed method as follows:

-Scenario 1, the base case where the flexible loads are not controlled in the network operation; -Scenario 2, the flexible loads are controlled using rolling optimization approach but the uncertainty of demand and generation are not taken into account, i.e. we suppose that the measured value ("real value") is exactly the predict value; -Scenario 3, the flexible loads are controlled using rolling optimization approach and the uncertainty of demand and generation are taken into account, i.e. we suppose the measured value is deviated from the forecasted value, and the forecast errors follow a normal distribution; -Scenario 4, the flexible loads are controlled using 24h off-line information of demand and generation without forecast errors, like a day-ahead scheduling. The prediction and optimization horizon in scenarios 2 and 3 is 6h. We suppose the prediction errors in scenario 3 are described by a normal distribution N(1, 0.1). The settings of these contrastive scenarios aim at verifying the robustness of proposed method against forecast errors and the length of optimization horizon.

Simulation Results and Discussions

The simulations are performed using MATLAB and the load flow is computed using the toolbox MATPOWER 5.0. First we compare the results of scenarios 1 and 2, respectively without and with DR scheduling. The demand and generation patterns in Fig. 8 implicate a large amount of reverse power flow towards the HV grid during the time intervals 5h~7h and 13h~17h. In the conventional market structure, the reverse power flow to the HV grid is unlikely to be refunded. Thus, the DR scheduling is increasing the demand to absorb a part of this production, and thus to reduce the excess of local generation. It can be found that in the proposed method the demand shifting can be done after and before the peak of generation. This is because the rolling optimization is able to anticipate the need of flexibility using prediction data. The level of flexibility is limited by the operational constraints of flexible loads, which will be presented lately. Then, the voltage profiles at two critical hours 16h and 19h30, respectively representing the hours with full production and peak demand hours, are shown in Fig. 9. At 16h, both WT and PV are around their maximum power production and the voltage profile could exceed the upper limit at some busses. The reactive power regulation of WT is used to offer voltage support as shown in Fig. 10, but it is not enough to mitigate the voltage deviations (this is due to the low x/r ratio of MV lines). The absorption of reactive power of WT1 and WT2 during 15~17h is for mitigating the voltage rise due to DG. At other hours, the WT1 injects reactive power for supporting the voltage profile as high as possible to reduce the power losses. As the reactive power regulation is not efficient enough to keep voltage inside the limits, the proposed method is able to remove the voltage constraint by both reducing the active power of DG and filling the valley of demand. In this example, the DR action helps to decrease the active power reduction of WT1 at 16h by 0.20MW. Thus, the rate of use of RES is improved with the use of DR. The DR scheduling can also remove low voltage constraints at 19h30 by shifting the demand to other hours. Although congestions are not observed in this feeder, it could be obviously solved by shifting the flexible demand as well. The optimal setpoint , EF of DR scheduling is shown in Fig. 11. As the DR scheduling works in a nodal approach, we only display the profiles at the busses 8 and 24 that are respectively upstream and downstream the WT1. The proposed method has shifted the demand of thermal appliance from morning peak period (10h~12h) to afternoon off-peak period (13h~17h). As it is not enough to mitigate the voltage constraints, the demand during 19h~20h30 is also partially shifted before 19h (anticipation of the heating with an increase of internal temperature before 19h). It is noticed that the flexible demand at bus 24 shows intensive solicitations in the 4h~8h period. This is because the downstream busses are with greater voltage sensitivity than the upstream busses, so the use of their flexibility is more excessive which results in some oscillations of thermal loads and CLPU effect. For avoiding this situation, the use of flexibility should be shared among busses in a more moderate way, for example by applying solicitation limits for DR at each bus. The internal temperature profiles of thermal appliances at busses 8 and 24 are shown in Fig. 12. The internal temperature of thermal appliances is always kept within the limits to ensure the comfort of users who allow their devices participation in the grid operation. It can be found from the action of DR that the temperature constraint (then the users comfort) is the strongest condition to impede the use of thermal appliance arbitrarily. The detailed results of four scenarios are presented in table 2. It is shown that all performance indicators including power losses, DG curtailment and reverse power flow in the scenarios 2, 3 and 4 have been improved in presence of DR scheduling. For the single day that was studied, the energy injected into the HV grid is respectively reduced by 11.8%, 10.6% and 12.0% and the energy savings are respectively 12.2%, 11% and 12.3%. The energy savings, i.e. the energy withdrawn from the HV grid, are constituted by the minimization of power losses, the reduction of DG curtailment, and the shifting of demand. This performance is limited by the TCLs as their temperature constraints are always respected. The overall demand of TCL is almost constant in the four scenarios. The results have shown that the performances of scenarios 2 and 3 are similar, and are close to scenario 4. Thus, it confirms the effectiveness of the proposed rolling optimization with respect to the off-line optimization. In particular, the results of scenario 3 compared with those of scenario 2 have proven the robustness of proposed method against the uncertainty of production of DG. This is because the optimal status DR is adjusted when the rolling optimization is performed with updated forecasting of RES at each step. Thus, it is demonstrated that the proposed method can be implemented in a realistic situation considering the uncertainty of forecast. In addition, the line losses are always reduced in scenarios 2, 3 and 4 because the objective function of OPF takes them into account. Furthermore, a sensitivity analysis is conducted with different penetration of DG and flexibilities to verify the performance of proposed method. First the installed capacity of WT1 and WT2 is varied from 1 to 6MW for each WT. We use the energy savings by DR as the indicator to assess the performance of DR. In Fig. 13, the energy saving on the distribution network increases almost linearly with installed power capacity. The influence of available flexible demand in the feeder is also investigated in the sensitivity analysis. Three percentages of flexible demand are used, respectively 15%, 30% and 50%. The percentage of energy savings as indicator is shown in Fig. 14. It is indicated that the capabilities of DR is increasing when it takes greater part in the overall demand. The rolling optimization in scenario 2 can reach the performance of an off-line optimization with full information (scenario 4), while it requires less forecasting window for the production of DG. The robustness of proposed method under prediction errors is also assessed as the indicator of scenario 3 is always comparably close to the optimal results when WT keeps increasing. Then, the influences of forecast error and the length of prediction horizon 4 are taken into consideration. The length of window is varied from 1h to 8h to test how the predictive vision can influence the optimization. Three levels of forecast errors, respectively normal distribution N(1, 0.05), N(1, 0.1) and N(1, 0.2), are used to compare with the scenario 2. In order to smooth the fluctuations due to randomness, a mean value of indicator for 10 realizations is used. The indicator of energy saving as a function of the prediction horizon 4 with these error settings is presented in Fig. 15. It is shown that the performance of DR can be improved in increasing the window 4 and the forecast accuracy. This is because the proposed method can better anticipate the use of flexibility with a longer window. The curve without forecast error indicates that the capabilities of flexibility are almost saturated when 4 reaches 6h. It is also shown that the forecast error leads greater influence with longer window, since the uncertainties related to the rolling optimization is greater.

Energy saving(MWh)

Percentage of energy savings (%) Fig. 15. Energy saving for different forecast horizon 4

Conclusions

The DR scheduling for thermal appliances using a rolling multi-period optimization based control has been proposed for the operation of a MV distribution network with high penetration of DG. The approach is based on the forecasts of the production and demand, and the availability of the flexible demand. The DR scheduling can be used to mitigate the voltage and congestion constraints when a great amount of DG is connected, and reduce the power losses in the distribution network. The DR will also help the DSO to respect its forecasted power profile with the transmission system operator (TSO) at the HV/MV substation. The overall profit in the distribution feeder is improved as the energy import from HV grid is reduced. The DSO will then be able to optimize its contracted power with the TSO. The DR will also help the DSO in postponing expensive reinforcements (building of a new substation, new feeders, and new transformer). Finally, the operational constraints of temperature for the TCLs are always respected in the proposed method, so the comfort of endusers is not seriously disturbed.

The proposed method is conducted in a test distribution feeder with high penetration of DG that has critical reverse power flow and voltage deviations. The prediction uncertainty of production and demand is taken into account. The results have shown that the proposed method can effectively employ the flexible loads to support the distribution network operation. The robustness of the proposed method is assessed against the uncertainty related to the forecasted data. Although the results may depend on the specific feeder characteristics, this paper proposes a methodology of DR scheduling for the distribution network optimization. Furthermore, the modeling of household TCL is based on the homogenous assumption. The future work for heterogeneous loads can be done using some innovative energy management approaches, and by developing a more detailed aggregated model of the flexible loads at the MV/LV substation level. Finally, the scheduling of flexibility is based on technical issues (smart devices to control the loads), the socio-economic assessment (consumer behavior and cost of smart solutions) and regulatory issues (market organization to valorize the flexibility at distribution level) that will have to be addressed for a successful implementation of demand flexibility.

Fig. 1 .

 1 Fig. 1. Architecture of household TCL management in a distribution network

Fig. 2 .

 2 Fig. 2. Equivalent thermal circuit scheme of a household heating device

  Temperature

Fig. 4 .

 4 Fig. 4. Procedures of rolling optimization

(

ig. 4 .Fig. 5 .

 45 Fig. 5. Flow chart of proposed DR scheduling

= + 1 Y 1 Send

 11 DSO identifies the timeslots with critical network conditions over [ , + 4] DSO runs the multi-period OPF over [ , + 4] using updated forecast data for offsetting the uncertainty of generation and demand = I0.35 • 12, 9 :: , 0.4 • 12, 9 :: ] to offer voltage support. The installed power of roof-top PVconnected at each bus is sized as a function of MV/LV transformer capacity and the PV is non-dispatchable. The overall demand at each bus is shared between unflexible demand (70%) and flexible demand (30%).

Fig. 6 .

 6 Fig. 6. Test distribution network

Fig. 7 .

 7 Fig. 7. 24hrs forecasting patterns of demand, PV and WT on the test feeder.

Fig. 8 .

 8 Fig. 8. 24hrs of demand and RES production profiles

Fig. 9 .

 9 Fig. 9. Voltage profiles at 16h and 19h30.

Fig. 10 .

 10 Fig.10. Reactive power regulation of WT generation (reactive power is positive if delivered into the grid)

Fig. 11 .

 11 Fig. 11. Power profile , EF of DR scheduling a) at bus 8, b) at bus 24.

Fig. 12 .

 12 Fig. 12. Internal temperature , EF of DR scheduling a) at bus 8, b) at bus 24.

Fig. 13 .Fig. 14 .

 1314 Fig. 13. Energy saving with DR as a function of installed capacity of WT.

  forecast error N(1, 0.05) Scenario 3 with forecast error N(1, 0.1) Scenario 3 with forecast error N(1, 0.2)

Table 1 .

 1 Parameters of thermal appliances in the simulation.

	19.3°C
	18.3°C

Parameter Value

Rth(Thermal resistance) 7.5°C/kW Cth(Thermal capacity) 2.19kWh/°C θmin(Minimum internal temperature) 17.3°C θmax(Maximum internal temperature) θref(Reference indoor temperature)

  and[START_REF] Ding | Rolling Optimization of Wind Farm and Energy Storage System in Electricity Markets[END_REF].

	θ	min		≤	θ	av	,	i	≤	θ	max	(24)
	min , agg P	i	≤ P	agg	,	i	≤	max , i agg P

Table 2 .

 2 Performance of network operation with DR for a single day. Total generation by DGs: 124.3 MWh

	Scenario 1	Scenario 2	Scenario 3	Scenario 4


Julia Mougin
email: julia.mougin@univ-littoral.fr

Stéphanie Copin
email: stephanie.copin@anses.fr

Daline Bojolly
email: daline.bojolly@gmail.com

Virginie Raguenet
email: virginie.raguenet@anses.fr

Annick Robert-Pillot
email: annick.robert-pillot@pasteur.fr

Marie-Laure Quilici
email: marie-laure.quilici@pasteur.fr

Graziella Midelet-Bourdin
email: graziella.bourdin@anses.fr

Thierry Grard
email: thierry.grard@univ-littoral.fr

Maryse Bonnin-Jusserand

Annick Robert- Pillot

Maryse Bonnin- Jusserand

Annick Robert

Adhesion to stainless steel surfaces and detection of viable but non cultivable cells of Vibrio parahaemolyticus and Vibrio cholerae isolated from shrimps in seafood processing environments: Stayin' alive?

Keywords: Vibrio parahaemolyticus, Vibrio cholerae, biofilm, shrimps, VBNC, PMA-qPCR, seafood industries

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Seafood can be contaminated by Gram-negative bacteria of the genus Vibrio present in coastal areas. In particular, Vibrio parahaemolyticus and Vibrio cholerae are two species potentially pathogenic for humans [START_REF] Robert-Pillot | Occurrence of the three major Vibrio species pathogenic for human in seafood products consumed in France using real-time PCR[END_REF]. V. cholerae can be divided into two major groups: the cholera-causing strains of serogroups O1 and O139, and non-O1/non-O139 V. cholerae. The pathogenicity of the strains depends on virulence factors, the most important of which is the cholera toxin (CT) encoded by the ctxA and ctxB genes.

The non-O1/non-O139 strains rarely possess these cholera toxin genes but may also cause human illness. V. parahaemolyticus has been implicated in more than 45% of all outbreaks of bacterial gastroenteritis worldwide, and this proportion is increasing with the emergence of pandemic strains spreading over large geographic areas [START_REF] Nair | Global dissemination of Vibrio parahaemolyticus serotype O3: K6 and its serovariants[END_REF]. Typical V. parahaemolyticus illness results in mild to moderate acute gastroenteritis. As foodborne pathogens, V. parahaemolyticus and V. cholerae are responsible for vibriosis outbreaks and represent a risk to consumer health, especially in case of ingestion of raw, undercooked or mishandled seafood [START_REF] Bonnin-Jusserand | Vibrio species involved in seafood-borne outbreaks (Vibrio cholerae, V. parahaemolyticus and V. vulnificus): review of microbiological versus recent molecular detection methods in seafood products[END_REF]; (M. F. R. [START_REF] Mizan | Microbial biofilms in seafood: A food-hygiene challenge[END_REF].

The risk associated with consumption of crustaceans, such as shrimps is well-documented for V. parahaemolyticus in several studies [START_REF] Zarei | Seasonal prevalence of Vibrio species in retail shrimps with an emphasis on Vibrio parahaemolyticus[END_REF]; [START_REF] Sani | The risk assessment of Vibrio parahaemolyticus in cooked black tiger shrimps (Penaeus monodon) in Malaysia[END_REF]; [START_REF] Xu | Prevalence, pathogenicity, and serotypes of Vibrio parahaemolyticus in shrimp from Chinese retail markets[END_REF] and for both V. parahaemolyticus and V. cholerae in others [START_REF] Rosec | The international standard ISO/TS 21872-1 to study the occurence of total and pathogenic Vibrio parahaemolyticus and Vibrio cholerae in seafood: ITS improvement by use of a chromogenic medium and PCR[END_REF]; [START_REF] Yano | Prevalence and antimicrobial susceptibility of Vibrio species related to food safety isolated from shrimp cultured at inland ponds in Thailand[END_REF]. Vibrio spp. can adopt specific survival strategies in harsh environments.

They are found either free-living or associated with sediments, suspended particles and plankton [START_REF] Thomas | Salinity-induced survival strategy of Vibrio cholerae associated with copepods in Cochin backwaters[END_REF]; [START_REF] Vezzulli | Vibrio infections triggering mass mortality events in a warming Mediterranean Sea[END_REF]; [START_REF] Vezzulli | Ocean warming and spread of pathogenic Vibrios in the aquatic environment[END_REF]). Their ability to adhere to the surface of the exoskeleton of animals composed of chitin, some zooplankton organisms, especially copepods [START_REF] Zidour | Isolation and characterization of bacteria colonizing Acartia tonsa copepod eggs and displaying antagonist effects against Vibrio anguillarum, Vibrio alginolyticus and other pathogenic strains[END_REF], and to plastic marine debris [START_REF] Zettler | Life in the "plastisphere": microbial communities on plastic marine debris[END_REF] allows them to survive in seawater longer than in a free-living state. Vibrio spp. have the ability to form organized structures called biofilms on various biotic and abiotic surfaces [START_REF] Watnick | Steps in the development of a Vibrio cholerae El Tor biofilm[END_REF]. It has been shown that Vibrio spp. are able to form biofilms on aquaculture equipment such as larval rearing tanks [START_REF] Bourne | Biofilm development within a larval rearing tank of the tropical rock lobster, Panulirus ornatus[END_REF] as well as on several other substrates, including glass, polystyrene, polyethylene and polyvinyl chloride [START_REF] Snoussi | High potential of adhesion to abiotic and biotic materials in fish aquaculture facility by Vibrio alginolyticus strains[END_REF]. Vibrio spp. biofilms have also been detected on fishing industry equipment [START_REF] Bagge-Ravn | The microbial ecology of processing equipment in different fish industries -Analysis of the microflora during processing and following cleaning and disinfection[END_REF]. Furthermore, biofilms represent a well-known hazard in the food industry [START_REF] Shi | Biofilm formation and food safety in food industries[END_REF]; [START_REF] Bridier | Biofilm-associated persistence of food-borne pathogens[END_REF], and many environmental factors influence biofilm formation. For example, recent studies on the role of additives currently used in seafood processing, like NaCl and glucose, on the biofilm formation of V. parahaemolyticus on shrimp surfaces have been carried out (M. F. [START_REF] Mizan | Variability in biofilm formation correlates with hydrophobicity and quorum sensing among Vibrio parahaemolyticus isolates from food contact surfaces and the distribution of the genes involved in biofilm formation[END_REF]. Within the biofilm, cells persist in different physiological stages, including in the viable but non cultivable (VBNC) state. The VBNC state is defined as a form of resistance of bacteria to adverse environmental conditions. The cells conserve their metabolic activity but cultivability is lost. Therefore, bacteria cannot be detected on agar Petri dishes [START_REF] Oliver | In vivo resuscitation, and virulence towards mice, of viable but nonculturable cells of Vibrio vulnificus[END_REF]. V. parahaemolyticus (H. [START_REF] Zhong | Virulence of thermolable haemolysi tlh, gastroenteritis related pathogenicity tdh and trh of the pathogens Vibrio Parahemolyticus in Viable but Non-Culturable (VBNC) state[END_REF]; [START_REF] Su | Changes of ultrastructure and stress tolerance of Vibrio parahaemolyticus upon entering viable but nonculturable state[END_REF]; [START_REF] Mizunoe | Resuscitation of viable but nonculturable cells of Vibrio parahaemolyticus induced at low temperature under starvation[END_REF] and V. cholerae [START_REF] Rowan | Viable but non-culturable forms of food and waterborne bacteria: Quo Vadis?[END_REF] can enter the VBNC state. Importantly, the ISO standard 21872-1 applied to detect potentially pathogenic Vibrio in seafood cannot detect bacteria in the VBNC state. This could lead to underestimation of the bacterial population.

This study focused on the biofilm-forming ability on stainless steel of two different potentially pathogenic for humans Vibrio species (V. cholerae and V. parahaemolyticus) isolated from shrimps. The processing of shrimps in the seafood industry follows different steps [START_REF] Gbph | Guides de bonnes pratiques d'hygiène : crustacés cuits[END_REF]. Considering the possibility of a recontamination risk after cooking, brine could play a role by transporting Vibrio. Interestingly, there is no further critical control point (CCP) making it possible to limit the microbial population level after this step. Moreover, brine is recycled. Therefore, pathogen or spoilage flora could be present for instance in the VBNC state if renewal is not sufficiently frequent. VBNC cells could hardly be detected by classical microbiological methods and thus represent a potential risk for consumers. In order to estimate the VBNC proportion of V. parahaemolyticus and V. cholerae within a biofilm closest to industrial conditions, in our study, the experiments required the comparison of three different techniques: qPCR, PMA-qPCR (propidium monoazide real-time PCR) ((Q. [START_REF] Zhong | iTRAQ-based proteomic analysis of the viable but nonculturable state of Vibrio parahaemolyticus ATCC 17802 induced by food preservative and low temperature[END_REF]; [START_REF] Zhu | Quantitative study of viable Vibrio parahaemolyticus cells in raw seafood using propidium monoazide in combination with quantitative PCR[END_REF]; [START_REF] Wu | Enumeration of viable non-culturable Vibrio cholerae using propidium monoazide combined with quantitative PCR[END_REF]) and enumeration on agar plates. The influence of environmental parameters such as temperature and media, including industrial brine, has been assessed. The aim of this study consists of assessing the adhesion to stainless steel surfaces and detecting Viable But Non Cultivable cells of Vibrio parahaemolyticus and Vibrio cholerae isolated from shrimps in seafood processing environments.

Materials and methods

Bacterial strains and culture conditions

Two environmental strains, isolated by the National Reference Laboratory (NRL: Vibrio sp. in fishery products), ANSES Boulogne-sur-mer, from seafood industries, were used in this study. The first one, Vibrio parahaemolyticus (ANSES collection 14-B3PA-0046) was isolated from shrimps imported from Nigeria. The second one, Vibrio cholerae (ANSES collection 09-B3PA-2350) was isolated from shrimps imported from Madagascar. The strains were maintained in marine broth with 10% glycerol and stored at -80 °C. Prior to the experiments, bacteria were initially streaked on nutrient agar supplemented with 2% NaCl and incubated overnight at 37 °C. The strains were then transferred into tryptic soy broth (TSB 2% NaCl) and incubated overnight at 37 °C. Subsequently, cells were centrifuged at 5,000 g for 10 min and resuspended in TSB 2% NaCl or artificial sea water (ASW) or industrial brine (4 m 3 brine bath composed of water with a salinity of 1.17 ± 0.02 (>1.10), pH 4). This step was repeated twice. Bacterial cell concentrations were adjusted to 10 8 CFU/mL by spectrophotometry (LibraS11, BioChrom Ltd., Cambridge, UK). In order to check the bacterial concentrations, suitable dilutions of suspensions were made and plated on salted nutrient agar using an automatic spiral plater (easySpiral ® , Interscience, St-Nom-la-Bretèche, France). The plates were incubated at 37 °C for 24 h. Cell numbers were then quantified using an automatic colony counter (Scan500 ® , Interscience).

Preparation of 316 2B stainless steel coupons

AISI 316 2B (Fe/Cr18/Ni10/Mo3) stainless steel coupons (37x16x1 mm) were purchased from Goodfellow SARL (Lille, France). First of all, 316 2B stainless steel coupons were rubbed with paper, soaked in a 50% acetone/50% ethanol (v/v) solution. Coupons were then immersed in a 20% alkali detergent solution (v/v) (RBS50, cleaning agent; Chemical Products R. Borghgraef, Brussels, Belgium) at 50 °C under agitation for 10 min. Coupons were rinsed in a 50 °C water bath under agitation for 20 min. Five successive rinses of 1 min each were carried out in ultra-pure water, while stirring at room temperature. Coupons were autoclaved for 15 min, at 121 °C.

Biofilm formation on stainless steel coupons

The bacterial suspension (11 mL) was transferred to a small sterile Petri dish (67 mm x 15 mm) containing a sterile coupon. In order to create a humid atmosphere, the Petri dishes were then placed in bigger Petri dishes (140 mm x 20 mm) containing paper towels soaked with 25 mL of sterilized distilled water. Sterile medium was used as a negative control. The Petri dishes were inoculated at 8 °C or 37 °C for 3 h, 24 h and 48 h.

Quantification of viable, VBNC and dead cells attached to stainless steel coupons after 3 h, 24 h and 48 h of incubation at either 37 °C or 8 °C

After incubation, cell suspensions were removed and coupons were washed with 25 mL of sterile physiological water (0.9 % NaCl) in order to remove non-adhered bacteria. Washed coupons were then transferred to sterile Petri dishes.

Two coupons were used per experiment. Each coupon was swabbed twice with the same swab. This swab was vortexed into a tube containing 2 mL of physiological water. A second swab, after undergoing the same process, was added to the same tube. This tube containing the two swabs was then vortexed for 20 sec. The contents of the tube were divided into three fractions. Each fraction underwent one of the following three techniques:

-First fraction: the proportion of viable cultivable bacteria was obtained by plating serial dilutions on salted nutrient agar using an automatic spiral plater (easySpiral ® , Interscience).

The plates were incubated at 37 °C for 24 h. Cell numbers were then quantified using an automatic colony counter (Scan500 ® , Interscience).

-Second fraction: qPCR was used to amplify DNA of viable, VBNC and dead bacteria.

Genomic DNA from cell suspensions (495 µL) was extracted using the DNeasy ® Blood & Tissue Kit 250 (Qiagen, Courtaboeuf, France), following the manufacturer's instructions.

Then, V. parahaemolyticus DNA was quantified by qPCR following the protocol described by [START_REF] Robert-Pillot | Occurrence of the three major Vibrio species pathogenic for human in seafood products consumed in France using real-time PCR[END_REF], with modifications. First at all, the LightCycler ® 480 (Roche Diagnostics, France) was used for qPCR. The Qiagen Master Mix QuantiTect ® Probe PCR Kit was used. This method was optimized for V. cholerae. Concentrations of reagents were adjusted to reach 4 mM Mg 2+ (Qiagen) and 6% glycerol using the Master Mix Light Cycler ® 480 Probes Master (Roche). The PCR cycling conditions were adjusted by including initial denaturation at 95 °C for 15 sec, followed by annealing at 61 °C for 1 min, extension at 72 °C for 15 sec.

-Third fraction: PMA-qPCR was used to amplify DNA of viable and VBNC bacteria.

Propidium monoazide, PMA (Biotium Inc., Hayward, CA, USA) dissolved in ultra-pure water was added to 495 µL of cell suspension to reach a final concentration of 50 mM. Following an incubation period of 5 min in the dark, samples were light-exposed using a photo-activation system (PhAst Blue, GenIUL, Barcelona, Spain), with an intensity of 80 for 10 min. DNA from cell suspensions was extracted using the DNeasy ® Blood & Tissue Kit 250 (Qiagen), following the manufacturer's instructions. Then, V. parahaemolyticus and V. cholerae DNA was quantified by qPCR following the protocol described above for the second fraction. The genome equivalents (GEs) of the V. parahaemolyticus and V. cholerae strains were calculated based on a V. parahaemolyticus genome of 5.0.10 6 bp [START_REF] Makino | Genome sequence of Vibrio parahaemolyticus: a pathogenic mechanism distinct from that of V cholerae[END_REF] and on a V. cholerae genome of 4.0.10 6 bp [START_REF] Heidelberg | DNA sequence of both chromosomes of the cholera pathogen Vibrio cholerae[END_REF].

Microscopy observation

A coupon was used to image biofilm samples on stainless steel. The biofilm was stained for 15 min in the dark with 200 µL taken from a LIVE/DEAD ® BacLight TM bacterial viability kit (Invitrogen, Carlsbad, CA, USA). Residual LIVE/DEAD ® BacLight TM solution was removed.

Bacteria attached to the coupon were then examined in a wet state under an epifluorescence microscope (Imager.Z1, Zeiss, Marly-le-Roi, France) connected to a CCD camera (Axiocam -MRm, Zeiss) with apotome (x 40 magnification). A minimum of five observations were taken for each observed coupon.

Statistical analysis

Biofilm experiments were carried out in triplicate (three independent experiments). Data were analyzed by one-way analysis of variance (ANOVA) using R3.3.3 software (R Core Team, 2017). The ANOVA model was verified using a Shap-iro-Wilk normality test and Bartlett test of homogeneity of variances. If the normality and homogeneity of variances of distribution were not verified, a Kruskal-Wallis test was carried out, followed by a post hoc test. Post hoc tests were performed with Bonferroni correction and Fisher's least significant difference (LSD) criterion, using the agricolae 1.2-8 package [START_REF] De Mendibru | Statistical procedures for agricultural research[END_REF]. The results were expressed as means ± standard error. Statistical significance was considered at p<0.05.

Results

Vibrio parahaemolyticus ANSES collection 14-B3PA-0046

Total population (viable cultivable, VBNC and dead bacteria; qPCR)

In TSB 2% NaCl and ASW broths, there was no impact of the incubation time at either 37 °C (Fig. 1) or 8 °C (Fig. 1) on the total population.

Viable cultivable and VBNC bacteria (PMA-qPCR)

In TSB 2% NaCl and ASW broths, the same proportion of bacteria was observed for 3 h (Fig. 1.a, 1.b), 24 h (Fig. 1.c, 1.d) and 48 h (Fig. 1.e, 1.f) at either 37 °C or 8 °C.

No additional viable bacteria were detected in brine, regardless of the conditions (Fig. 1).

Viable cultivable bacteria (enumeration on Petri dishes)

A significant decrease in bacteria numbers was observed in TSB 2% NaCl broth depending on biofilm formation kinetics (3 h, 24 h and 48 h), regardless of the temperature (p<0.05) (Fig. 1). However, in ASW broth, there were more adherent bacteria at time point 24 h (Fig. 1.c, 1.d) compared to 3 h (Fig. 1.a, 1.b) and 48 h (Fig. 1.e, 1.f), regardless of the temperature (p<0.05). No viable cultivable bacteria were detected in brine at either 37 °C (Fig. 1) or 8 °C (Fig. 1).

Vibrio cholerae ANSES collection 09-B3PA-2350

Total population (viable cultivable, VBNC and dead bacteria; qPCR)

In TSB 2% NaCl broth, the total population had decreased at 37 °C at time point 48 h (p<0.05) (Fig. 2.e). Otherwise, the total population was almost equivalent at 8 °C at 3 h (Fig.

2.b)

, 24 h (Fig. 2.d) and 48 h (Fig. 2.f). In ASW broth, there were no differences regardless of the incubation time or the temperature. In brine, equivalent bacteria proportions were detected at 8 °C, regardless of the incubation time (Fig. 2.b, 2.d, 2.f). In contrast, bacterial populations had decreased at time point 48 h compared to 3 h and 24 h at 37 °C (p<0.05) (Fig. 2.a, 2.c, 2.e).

Viable cultivable and VBNC bacteria (PMA-qPCR)

In TSB 2% NaCl and ASW broths, the proportion of viable bacteria was equivalent regardless of the incubation time and temperature (Fig. 2). In brine, bacteria in the VBNC state were detected at 8 °C for each incubation time, including 48 h. (Fig. 2.b, 2.d, 2.f). This means that the VBNC V. cholerae cells remained in brine at 8°C even for a long period of time.

However, at 37 °C, at time point 48 h, viable bacteria were no longer detected (Fig. 2.e).

Indeed, results showed no amplification curve of PMA-qPCR for an incubation time of 48 h at 37 °C in brine only. Thus, all V. cholerae cells were dead (Fig. 2.e).

Viable cultivable bacteria (enumeration on Petri dishes)

In TSB 2% NaCl, the proportion of viable cultivable bacteria was not significant regardless of the incubation time and the temperature (Fig. 2). In ASW, cultivable V. cholerae were not detected at 37 °C regardless of the incubation time (Fig. 2.a, 2.c, 2.e). Furthermore, cultivable V. cholerae were not detected at 8 °C at time point 3 h (Fig. 2.b), but the proportion of bacteria was equivalent at 8 °C at 24 h (Fig. 2.d) and 48 h (Fig. 2.f) of incubation. No additional viable cultivable V. cholerae were detected in brine, regardless of the incubation time and temperature (Fig. 2).

Comparisons between V. cholerae and V. parahaemolyticus in brine for an incubation time of 48 h

In Brine, for an incubation time of 48 h, temperature had significant effect on the adhesion of total bacteria regardless Vibrio species. Indeed, more bacteria were detected at 8 °C compared to 37 °C (Fig. 3.1).

VBNC cells of V. parahaemolyticus were no longer detected either at 37 °C or 8 °C, while VBNC cells of V. cholerae were detected at 8 °C only. According to these results, the adhesion of VBNC cells could be strain-dependent. Moreover, these results demonstrated that temperature has a significant effect on the adhesion of VBNC V. cholerae cells in brine (Fig.

3.2).

Viable cultivable bacteria were not detected either at 37 °C or 8 °C on Petri dishes (Fig. 3.3).

Observations of adherent cells

Micro-colonies were observed at 37 °C and 8 °C for V. parahaemolyticus at time points 3 h, 24 h and 48 h (Fig. 4). In brine, aggregates were observed from the early stages of adhesion (3 h), regardless of the temperature (Fig. 4.g, 4.p). These observations clearly showed a mix of live and dead bacteria during the establishment of these aggregates. Regarding V. cholerae (Fig. 5), dense cell clusters were observed from 3 h at 37 °C in TSB 2% NaCl (Fig. 5.a).

These clusters decreased at 48 h, suggesting dispersal of the biofilm (Fig. 5.c). However, at 8 °C at time point 48 h (Fig. 5.l), clusters especially composed of dead cells were still present.

In ASW broth, minor changes in the structure of the biofilm were observed, regardless of the temperature and the incubation time (Fig. 5.d,5.e,5.f,5.m,5.n,5.o). In brine, small aggregates were observed regardless of the temperature and time (Fig. 5.g,5.h,5.i,5.p,5.q,5.r). No further viable V. cholerae were observed in brine at 37 °C at time point 48 h (Fig.

5.i).

Viable bacteria could be VBNC cells not detected by the molecular methods used or Petri dish enumeration. This could be explained by a detection threshold level of the techniques used that may be too low.

Discussion

This study provides a deeper understanding of the viability of bacteria inside the biofilm. qPCR and PMA-qPCR methods were developed for both Vibrio species, in addition to cell enumeration on agar. Importantly, the combined findings of these three techniques and their comparison provided the proportions of viable cultivable, VBNC and dead bacteria.

Additionally, microscopy using a LIVE/DEAD ® staining kit coupled with Apotome technology enabled us to supplement the results obtained by the methods mentioned above. In this way, we were able to observe bacterial adhesion on stainless steel coupons. These observations were corroborated by results obtained by enumeration on agar plates and molecular biology (qPCR and PMA-qPCR methods).

Many factors influence biofilm formation: the bacteria themselves, that is to say their own genetics and physiology, the type of attachment surface, and the environment (pH, temperature, nutrient availability) [START_REF] Whitehead | Formation, architecture and functionality of microbial biofilms in the food industry[END_REF]. Among these factors, temperature seems to play a key role in Vibrio biofilm formation. In this study, small differences were observed regarding V. parahaemolyticus and V. cholerae biofilms at either 37 °C or 8 °C, regardless of the incubation time (3 h, 24 h, 48 h). Indeed, the only significate influence of temperature determined was in brine but no influence was detected in TSB or ASW broths. Nonetheless, [START_REF] Ahmed | Molecular characterization, antibiotic resistance pattern and biofilm formation of Vibrio parahaemolyticus and V. cholerae isolated from crustaceans and humans[END_REF] showed that the biofilm formation of V. parahaemolyticus and V. cholerae was significantly higher at 37 °C and 25 °C than at 4 °C. In the study carried out by [START_REF] Beshiru | Characterization of extracellular virulence properties and biofilm-formation capacity of Vibrio species recovered from ready-to-eat (RTE) shrimps[END_REF], V. cholerae showed better abilities to form biofilm at 21 °C and 37 °C, while the highest biofilm production of V. parahaemolyticus was observed at 30 °C in TSB under dynamic culture. Similarly, [START_REF] Song | Effect of temperature on pathogenic and non-pathogenic Vibrio parahaemolyticus biofilm formation[END_REF] observed better biofilm production of V. parahaemolyticus in TSB 3% NaCl at 25 °C compared to 15 °C and 37 °C. [START_REF] Han | Biofilm formation by Vibrio parahaemolyticus on food and food contact surfaces increases with rise in temperature[END_REF] showed that V. parahaemolyticus biofilm formation was better at 30 °C than 4 °C on stainless steel coupons immersed in fresh water.

Furthermore, H.-C. [START_REF] Wong | Attachment and inactivation of Vibrio parahaemolyticus on stainless steel and glass surface[END_REF] showed that bacterial adhesion on stainless steel surfaces decreased when V. parahaemolyticus was incubated at 4 °C. All these results showed better biofilm formation for high temperatures (between 21 °C to 37 °C) compared to 4 °C, contrary to our findings.

Adhesion is also media-dependent. In this study, we did not observe the same behavior on either TSB 2 % NaCl or ASW between Vibrio spp. V. parahaemolyticus exhibited the same behavior in TSB or ASW, regardless of the incubation temperature. However, in ASW for 24 h and 48 h, better survival was observed for V. cholerae at 8 °C compared to 37 °C due to the presence of viable cultivable bacteria. The amount of viable cultivable bacteria at 37 °C in ASW was not higher than in TSB. H.-C. [START_REF] Wong | Attachment and inactivation of Vibrio parahaemolyticus on stainless steel and glass surface[END_REF] showed that V. parahaemolyticus exhibited better adhesion on stainless steel when cultured in chemically defined MM9 medium than TSB. Regarding V. cholerae, this behavior was dependent on the temperature of incubation. At 8 °C for 24 h and 48 h, there were no differences in adhesion on either TSB 2% NaCl or ASW. The fact that a nutrient-poor medium promotes adhesion has already been shown especially for L. monocytogenes [START_REF] Kadam | Diversity assessment of Listeria monocytogenes biofilm formation: impact of growth condition, serotype and strain origin[END_REF]. The composition of ASW medium is close to that of the marine environment, which is the natural habitat of Vibrio.

Therefore, better biofilm formation ability could be expected in this medium, which was not the case in our study. Moreover, [START_REF] Beshiru | Characterization of extracellular virulence properties and biofilm-formation capacity of Vibrio species recovered from ready-to-eat (RTE) shrimps[END_REF] showed that V. cholerae and V. parahaemolyticus biofilm formation was not medium-dependent (even when the medium was rich or poor). Both tested strains also exhibited different behaviors in brine. Indeed, in this study, only dead bacteria were detected regardless of the temperature and incubation time for V. parahaemolyticus. However, VBNC bacteria of V. cholerae were detected at 37 °C for 3 h and 24 h. VBNC cells were detected for each incubation time at 8 °C. These results indicate a greater survival capacity of V. cholerae at 8 °C than at 37 °C in poor media. The induction of the VBNC state is triggered by a low temperature, more often 4 °C, and under starvation conditions (Q. [START_REF] Zhong | iTRAQ-based proteomic analysis of the viable but nonculturable state of Vibrio parahaemolyticus ATCC 17802 induced by food preservative and low temperature[END_REF]; [START_REF] Su | Changes of ultrastructure and stress tolerance of Vibrio parahaemolyticus upon entering viable but nonculturable state[END_REF]; (H. C. [START_REF] Wong | Resuscitation of viable but nonculturable Vibrio parahaemolyticus in a minimum salt medium[END_REF]. This could explain the presence of VBNC cells of V. cholerae in brine at 8 °C.

As for temperature and media, bacterial adhesion depends on the food contact surface.

Stainless steel coupons were used in this study to mimic the conditions encountered in seafood industries. Also, [START_REF] Han | Biofilm formation by Vibrio parahaemolyticus on food and food contact surfaces increases with rise in temperature[END_REF] studied V. parahaemolyticus biofilm formation on stainless steel, crab and shrimp. In addition to surface hydrophoby, cell hydrophoby must also be taken into account: Vibrio are strongly hydrophilic [START_REF] Beshiru | Characterization of extracellular virulence properties and biofilm-formation capacity of Vibrio species recovered from ready-to-eat (RTE) shrimps[END_REF]. However, it is difficult to compare studies of Vibrio biofilms with one another because, most of the time, biofilm formation was studied via the microtiter plate assay with crystal violet staining. This assay remains a useful screening tool, but does not reflect real biofilm formation conditions; in particular, the surface type is not considered. Moreover, Vibrio biofilm formation is strain-dependent [START_REF] Song | Effect of temperature on pathogenic and non-pathogenic Vibrio parahaemolyticus biofilm formation[END_REF]; [START_REF] Odeyemi | Population dynamics, antibiotics resistance and biofilm formation of Aeromonas and Vibrio species isolated from aquatic sources in Northern Malaysia[END_REF]; (M. F. [START_REF] Mizan | Variability in biofilm formation correlates with hydrophobicity and quorum sensing among Vibrio parahaemolyticus isolates from food contact surfaces and the distribution of the genes involved in biofilm formation[END_REF][START_REF] Wong | Attachment and inactivation of Vibrio parahaemolyticus on stainless steel and glass surface[END_REF]. To supplement our results, different V. parahaemolyticus and V. cholerae strains of different origins (isolated from different seafood products or different countries), including pathogenic strains (carrying pathogenicity factors such as ctxA/ ctxB genes for V. cholerae and tdh or trh genes for V. parahaemolyticus) should be assessed. Clearly, biofilm formation may be different depending on the strain's origin. The formation of mixed biofilm, for example Vibrio in association with Listeria monocytogenes, could also be evaluated. In addition to developing survival strategies (VBNC, biofilm), strains are constantly adapting to their environment. More and more studies point to the extent of antibiotic resistance for Vibrio [START_REF] Yano | Prevalence and antimicrobial susceptibility of Vibrio species related to food safety isolated from shrimp cultured at inland ponds in Thailand[END_REF]. This characteristic should therefore be taken into account in the biofilm survival state. [START_REF] Elexson | Effect of detergents as antibacterial agents on biofilm of antibiotics-resistant Vibrio parahaemolyticus isolates[END_REF] tested several detergents against biofilms of antibiotic-resistant V. parahaemolyticus isolated from seafood and found hygiene issues and cross-contamination hazards in the seafood industries.

Conclusion

For the first time, the cell viability was taken into account in order to better understand the survival strategies of potentially pathogenic human Vibrio. The aim of this study was to get as close as possible to the conditions encountered in seafood industries, even if this test remained in vitro for the standardization and exploitation purposes of the results. This scoping study highlighted the behavior of both Vibrio strains under laboratory conditions for different environmental parameters. Despite the fact that there are few data on the risk for consumer health of V. cholerae from shrimps, the strain tested in this study showed a greater survival capacity than the V. parahaemolyticus strain. Indeed, VBNC V. cholerae cells were detected at 8 °C in brine for each incubation time, including 48 h. Therefore, it could be useful to monitor V. cholerae contamination in shrimps. Seafood processing companies need to be aware of and take into account the Vibrio risk in their hazard analysis critical control point (HACCP) approach. The possible presence of VBNC cells cannot be ruled out. The use of molecular biology methods like PMA-qPCR is recommended to ensure the safety of seafood products.

Figures caption:

Figure 1: Vibrio parahaemolyticus ANSES collection 14-B3PA-0046 at 37 °C (a,c,e) and 8 °C (b,d,f): enumeration of total bacteria: viable cultivable, VBNC, and dead bacteria (qPCR: black squares); viable cultivable and VBNC bacteria (PMA-qPCR: white squares); viable cultivable bacteria (on Petri dishes: grey squares) for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. Values are expressed by means, ± 95 % confidence interval, of three independent experiments. (a,b,c,d,e,f,g,h,i) and 8 °C (j,k,l,m,n,o,p,q,r): relevant microscopic photographs of stainless steel coupons after LIVE/DEAD ® BacLight TM staining for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media. 20 Experiments were repeated three times and a minimum of five images were taken for each observed stainless steel coupon. (a,b,c,d,e,f,g,h,i) and 8 °C (j,k,l,m,n,o,p,q,r): relevant microscopic photographs of stainless steel coupons after LIVE/DEAD®BacLight TM staining for 3 h, 24 h and 48 h of incubation on TSB (2% NaCl), ASW and brine media.

Experiments were repeated three times and a minimum of five images were taken for each observed stainless steel coupon.

Figure 2 :

 2 Figure 2: Vibrio cholerae ANSES collection 09-B3PA-2350 at 37 °C (a,c,e) and 8 °C (b,d,f):

Figure 3 :

 3 Figure 3: Vibrio cholerae ANSES collection 09-B3PA-2350 (hatched square) and Vibrio

Figure 4 :

 4 Figure 4: Vibrio parahaemolyticus ANSES collection 14-B3PA-0046 at 37 °C

Figure 5 :

 5 Figure 5: Vibrio cholerae ANSES collection 09-B3PA-2350 at 37 °C (a,b,c,d,e,f,g,h,i) and

Acknowledgements

Julia Mougin would like to thank the "Hauts-de-France" regional council for its financial support of her PhD studies. This work was funded by the French government and the region Hauts-de-France in the framework of the CPER 2014-2020 MARCO project. This work was supported by a grant from FranceAgriMer (2014-0813) as part of the SURVIB project. The authors also thank the CITPPM (Confédération des industries de traitement des produits des pêches maritimes et de l'aquaculture) as a contributor to the SURVIB project. The authors are also grateful to Thomas Brauge for his scientific assistance, to Cindy Ducrocq and Marie-Ange Huchin for technical assistance and to Alexandre Dehaut for advices in statistical analyses.