Cécile Bally

Aline Renneville

Claude Preudhomme

Matthieu Legrand

Lionel Adès

Hugues De Thé

Pierre Fenaux

Jacqueline Lehmann-Che
email: jacqueline.lehmann-che@aphp.fr

Comparison of TP53 mutations screening by functional assay of separated allele in yeast and next-generation sequencing in myelodysplastic syndromes

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

Introduction

Myelodysplastic syndromes (MDS) are clonal hematopoietic stem cell disorders, predominating in the elderly, characterized by ineffective hematopoiesis leading to blood cytopenias and progression to acute myeloid leukemia (AML) in approximately 30% of the cases. Among MDS, the 5q-syndrome constitutes a disease subtype defined by the presence of an interstitial deletion in the long arm of chromosome 5 and associated with better prognosis and a lower risk of transformation to AML compared to other MDS [START_REF] Boultwood | Advances in the 5q-syndrome[END_REF]. In terms of treatment, hypomethylating agents like 5-azacitidine have shown a better efficacy than conventional chemotherapy regimens in MDS [START_REF] Fenaux | Efficacy of azacitidine compared with that of conventional care regimens in the treatment of higher-risk myelodysplastic syndromes: a randomised, open-label, phase III study[END_REF]. Moreover, lenalidomide, an immunomodulating agent, was shown to be particularly active in MDS with 5q- [START_REF] List | Lenalidomide in the myelodysplastic syndrome with chromosome 5q deletion[END_REF][START_REF] List | Efficacy of lenalidomide in myelodysplastic syndromes[END_REF].

TP53, located on the short arm of the chromosome 17, is a major tumor suppressor gene that controls genome integrity. It encodes for p53 protein, a transcription factor involved in DNA repair, cell cycle control, apoptosis and additional functions in the cell fate like metabolism, aging, and cell stemness. TP53 mutations occur in 5-10% of MDS and AML, especially in cases with complex karyotype, often including 17p deletion (i.e. loss of the wild-type TP53 allele) [START_REF] Soenen | 17p Deletion in acute myeloid leukemia and myelodysplastic syndrome. Analysis of breakpoints and deleted segments by fluorescence in situ[END_REF] and in the 5q-syndrome, where they are found in about 20% of the cases [START_REF] Jädersten | TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict disease progression[END_REF]. Interestingly, the 5q-syndrome is a model of ribosomopathy, like Blackfan Diamond anemia, with haplo-insufficiency for about 40 genes including RPS14 [START_REF] Benjamin | Molecular dissection of the 5q deletion in myelodysplastic syndrome[END_REF][START_REF] Ebert | Identification of RPS14 as a 5q-syndrome gene by RNA interference screen[END_REF] leading to activation of the p53 pathway and p53-induced apoptosis of the erythroid cells. Furthermore, a mouse model of 5q-syndrome crossed with p53 -/-mouse does not develop the 5q-syndrome phenotype, illustrating the importance of p53 pathway in this disease [START_REF] Barlow | A p53-dependent mechanism underlies macrocytic anemia in a mouse model of human 5q-syndrome[END_REF].

The presence of TP53 mutation is associated with poor prognosis in AML and MDS, even in patients with complex karyotype, where it remains an independent poor prognostic factor [START_REF] Rücker | TP53 alterations in acute myeloid leukemia with complex karyotype correlate with specific copy number alterations, monosomal karyotype, and dismal outcome[END_REF]. Furthermore, TP53 mutations in AML and MDS are associated with poorer response to chemotherapy [START_REF] Wattel | p53 mutations are associated with resistance to chemotherapy and short survival in hematologic malignancies[END_REF], 5-azacitidine [START_REF] Bally | Prognostic value of TP53 gene mutations in myelodysplastic syndromes and acute myeloid leukemia treated with azacitidine[END_REF][START_REF] Bejar | TET2 mutations predict response to hypomethylating agents in myelodysplastic syndrome patients[END_REF], allogeneic SCT [START_REF] Bejar | Somatic mutations predict poor outcome in patients with myelodysplastic syndrome after hematopoietic stem-cell transplantation[END_REF] and, in the case of MDS with isolated del(5q), to poorer response to lenalidomide (especially cytogenetic response) [START_REF] Jädersten | TP53 mutations in low-risk myelodysplastic syndromes with del(5q) predict disease progression[END_REF][START_REF] Kulasekararaj | TP53 mutations in myelodysplastic syndrome are strongly correlated with aberrations of chromosome 5, and correlate with adverse prognosis[END_REF][START_REF] Mallo | Response to lenalidomide in myelodysplastic syndromes with del(5q): influence of cytogenetics and mutations[END_REF]. Therefore assessing TP53 mutations in AML and MDS appears important in routine practice to refine prognosis and to better predict resistance to conventional treatments.

The analysis of TP53 mutational status is important for AML, high-risk and low-risk MDS. Nonetheless, there is no reference method to screen TP53 mutations, which are largely distributed in the DNA binding domain of the protein. However, some recommendations on TP53 mutation analysis in the context of chronic lymphocytic leukemia (CLL) are available and could guide the detection in other hematological disorders [START_REF] Pospisilova | ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia[END_REF]. Immunohistochemistry (IHC) has been widely used as a surrogate for detection of TP53 status and detects the p53 protein accumulation in a cell, often but not constantly caused by TP53 mutation. On the other hand, direct sequencing using the Sanger method allows the identification of the mutation but with a sensitivity of only 15-20% of mutated allele.

Functional assay of separated allele in yeast (FASAY) is a functional method that detects TP53 mutations by assessing p53 transcriptional activity in yeast [START_REF] Flaman | A simple P53 functional assay for screening cell lines, blood, and tumors[END_REF]. This RNA-based method has a detection limit of approximately 10% but evaluates the function of the p53 protein with subsequent identification of the defect by sequencing. It has been largely used by our group and others in solid tumors, especially breast carcinoma [START_REF] Dumay | Distinct tumor protein p53 mutants in breast cancer subgroups[END_REF][START_REF] Bertheau | Effect of mutated TP53 on response of advanced breast cancers to high-dose chemotherapy[END_REF], but less often in hematological malignancies. Finally, over the last few years, the development of next-generation sequencing (NGS) technologies has revolutionized our ability to analyze cancer genomes. Importantly, the detection of low mutant allele burden can now be achieved by NGS assays.

In this study, we investigated the presence of TP53 mutations in patients with high-risk MDS or AML, or low-risk MDS with del(5q) with two different approaches, FASAY and NGS, and we compared the results obtained with both methods.

Material and methods

Patients and samples

We studied a cohort of 91 patients diagnosed with MDS in six French centers and composed of 24 high-risk MDS or AML patients already part of a previous publication [START_REF] Bally | Prognostic value of TP53 gene mutations in myelodysplastic syndromes and acute myeloid leukemia treated with azacitidine[END_REF], and 67 lower-risk MDS with del(5q).

For all patients, bone marrow samples at diagnosis or during disease follow-up were available. All patients provided written informed consent.

Samples preparations and qualification

DNA and RNA were extracted by phenol-chloroform methods from mononuclear bone marrow cells collected without specific RNA conservation recommendations, quantified by Nanodrop spectrophotometer and quality was estimated by agarose gel electrophoresis.

FASAY analysis and direct sequencing by Sanger method

TP53 status was assessed on RNA by the functional analysis of separated alleles in yeast (FASAY) method in the molecular oncology unit of St. Louis Hospital. This assay evaluates the transactivation activity of p53 on a p53-responsive promoter stably integrated in the yeast genome, as initially described [START_REF] Flaman | A simple P53 functional assay for screening cell lines, blood, and tumors[END_REF]. Briefly, RNA was reverse transcribed in cDNA using Random Hexamer and Superscript II, TP53 transcripts were amplified by polymerase chain reaction (PCR) (exon 4-10) and cotransfected with the Gap repair plasmid in yeast [START_REF] Dumay | Distinct tumor protein p53 mutants in breast cancer subgroups[END_REF]. Yeast colonies transformed with wildtype or mutated TP53 sequences appear as white and large or red and small, respectively. TP53 status was considered non-functional when more than 10% of the yeast colonies were red. Analysis of the split version of the test was performed to confirm this result and to localize the defect in the 5 ′ or 3 ′ part of the gene. The detection limit of this technique is 10-15%. Finally, the TP53 defect was identified by direct Sanger sequencing of the FASAY PCR product if the percentage of red colonies was over 40% or of the cloned DNA from red yeast colonies if the percentage of red colonies was low (<40%).

NGS analysis of TP53

NGS analysis was performed in the hematology laboratory of Lille University Hospital using the GS Junior System (Roche, Meylan, France) and the TP53 assay-on-demand from the Interlaboratory RObustness of Next-generation sequencing (IRON) study phase II. TP53 exons 4-11 were amplified from genomic DNA using eight primer pairs including a molecular identifier barcode sequence. Information on primer sequences and the detailed protocol can be provided upon request.

Data analysis and variant detection was performed as previously described by Kohlmann et al. [START_REF] Kohlmann | The Interlaboratory RObustness of Next-generation sequencing (IRON) study: a deep sequencing investigation of TET2, CBL and KRAS mutations by an international consortium involving 10 laboratories[END_REF]. The detection limit of this assay was 1-2% of mutant allele.

Results

Ninety-one patients were analyzed, including 12 AML, 12 highrisk MDS and 67 low-risk MDS with del(5q).

p53 status evaluated by FASAY and NGS method

FASAY technology was performed on the 91 RNA samples available and 83 yeast assays were evaluable (91%), while the remaining eight patients had inconclusive results, which represent an acceptable result regarding the sample collection conditions. Among the 83 evaluable patients, 47 (57%) had a functional and 36 patients (43%) a non-functional FASAY (Table 1). In order to identify the alteration highlighted by the transcriptional tests, Sanger sequencing of cDNA (if % red colonies was >40%) or cloned DNA from red yeast colonies was performed and TP53 mutations could be detected in 35 cases. The remaining single case presented no mutation but lead to the identification of the transcriptional deficient ␤-isoform of p53 (UPN 2, Table 2).

NGS was performed on the corresponding DNA sample of all 91 patients: 50 patients (55%) were wild-type for TP53 and 41 (45%) had a TP53 gene mutation (Table 1). In the 41 mutated cases by NGS, the median proportion of mutated allele was 28% (range 2-99%), including a median of 72%, 35%, 14% in AML, higher risk MDS and lower risk MDS with del(5q), respectively. Finally, by combining results, 42 of the 91 (46%) patients had at least one TP53 gene mutation with a total number of 55 mutations identified (Fig. 1). Because eight patients had inconclusive FASAY (Supplementary Table 1), comparison between the two methods was possible in 83 fully evaluated patients. Results obtained with functional assessment of p53 by FASAY and TP53 mutational status assessed by NGS were concordant in 79/83 (95%) cases. TP53 status according to WHO classification (AML, high risk or low risk MDS) is presented in supplementary data (Fig. S1).

Comparison between FASAY followed by Sanger sequencing and NGS methods

Among the 83 FASAY/NGS analyzed cases, five results were not fully concordant for the alteration identified with both methods (Table 2). In two cases (UPN 1 and 2), FASAY was non-functional but no mutation was found by NGS on genomic DNA. In both cases, Sanger sequencing revealed insertions of intronic regions (part of intron 9 in one case and intron 8 in the other) in the TP53 mRNA, reflecting splicing defects not explored by the NGS design but clearly identified on RNA by FASAY. UPN2 was of particular interest and presented a non-functional FASAY without identification of a mutation on the cloned TP53cDNA, but identification of the transcriptional deficient p53␤ isoform. In two other cases (UPN 3 and 4), a mutation was detected by NGS without functional alteration on FASAY, corresponding to point mutations for which the percentage of the mutated alleles was less than 10% (3% and 6%, respectively). Nevertheless, the p.P222S mutation identified in UPN 4 was reported to present a super transactivating phenotype with a neutral effect in the IARC database. This may explain why it was not detected by FASAY. Finally, in UPN 5, classified as mutated by both methods, the mutation detected was different: FASAY followed by Sanger sequencing identified a deletion in exon 4 leading to a STOP codon on amino-acid 147,whereas a point mutation (M246V) at low frequency (10%) was identified by NGS. In that case, migration of the TP53 cDNA amplicon on agarose gel electrophoresis showed a shorter fragment concordant with presence of a deletion (data not shown). Consequently, this patient may have had two mutations: a deletion of exon 4, not detected by NGS and a point mutation, not detected by FASAY+Sanger because of a low percentage of mutated alleles.

Finally, in the 34 concordant mutated cases, at least the highest frequency mutation could be detected by both methods. The high sensitivity of the NGS technology was able to detect multiple TP53 mutations in nine patients with mutations at low frequency (2-11%) (Table 3). Surprisingly, the FASAY followed by Sanger sequencing was able to detect 14/19 (73.7%) of these multiple mutations, even at a frequency as low as 5% (Table 3). Occurrence of several mutations in patients with an initial diagnosis of low-risk MDS with del(5q) has been more carefully analyzed (manuscript in preparation).

Discussion

In this work, we analyzed the TP53 mutational status in 91 patients with either high-risk MDS or low-risk MDS with del(5q) by two methods: the FASAY in combination with Sanger sequencing presenting a sensitivity around 10-15%, and a NGS assay with a sensitivity around 1-2%. NGS results could be obtained in 100% of analyzed cases while the more tricky analysis of the RNA by FASAY was possible in 91%, without specific sample collection recommendations. Collectively, by FASAY and/or NGS, 42 patients (46%) presented a TP53 alteration with a total of 55 mutations. TP53 alterations were predominantly missense mutations (50/55) but we identified also insertion/deletion in 5/55 cases.

Results and subsequent comparison between the two methods could be obtained for 83 patients with an excellent concordance of 95% (79/83). The five discordant cases were carefully analyzed. The two FASAY false negative cases (2.3%) could be explained by the presence of the mutated alleles at low percentage (3% and 6%), thereby only detectable by NGS. But one case (p.P222S) may also have be explained by the fact that the mutation is described in the IARC database (http://p53.iarc.fr/) as a non-deleterious (functional classification based on SIFT program) mutation that does not inhibit the transcriptional activity of the protein. This functionality is directly addressed by the FASAY test whereas NGS data require further functional interpretations. The one NGS false negative case, consisting of an intron 8-insertion, was not detectable because intronic regions were not analyzed by the assay. The discrepancy in UPN 5, despite a correct classification of the patient as TP53 deficient, also illustrates the aforementioned pitfalls of the two technologies: less sensitivity for FASAY and less specificity of NGS for ins/del mutations.

Finally, UPN 2 is of particular interest because the nonfunctional FASAY lead to the identification of the p53␤ isoform, undetectable by the NGS technology. This case cannot strictly be considered as mutated because the p53␤ isoform exists in normal tissues [START_REF] Flaman | The human tumour suppressor gene p53 is alternatively spliced in normal cells[END_REF], and thus could be considered as a false positive case of the FASAY technology, which was however correctly identified with the subsequent Sanger sequencing. Nevertheless, presence of this p53 isoform could have clinical implications that should be explored. Indeed, the p53 protein was the first isoform identified in the 80s but to date, at least twelve p53 protein isoforms have been described [START_REF] Surget | Uncovering the role of p53 splice variants in human malignancy: a clinical perspective[END_REF]. These isoforms are differentially expressed in human cancer types and the imbalance between p53 isoforms expression could have clinical consequences. Recently, alterations in the SRSF3 splicing factors were shown to induce these alternatively spliced p53␤ isoform with promotion of cellular senescence [START_REF] Tang | Downregulation of splicing factor SRSF3 induces p53␤, an alternatively spliced isoform of p53 that promotes cellular senescence[END_REF]. This finding could be particularly interesting in the context of MDS where somatic mutations of splicing factors have already been described and seem to have prognostic significance [START_REF] Boultwood | The role of splicing factor mutations in the pathogenesis of the myelodysplastic syndromes[END_REF].

Interestingly, in our work, several mutations could be detected in 9/83 patients (total of 19) by NGS. Among these mutations, 73.7% were also detected by FASAY followed by Sanger sequencing despite the fact that the additional mutations were present at low rate (from 5% to 15%). This sensitivity can be further improved by extended cloning and sequencing of red yeast colonies, each colony representing a mutated allele.

It has previously been shown that none of the existing methods of TP53 gene analysis allowed detection of all TP53 abnormalities, especially for the two methods used in our study [START_REF] Pospisilova | ERIC recommendations on TP53 mutation analysis in chronic lymphocytic leukemia[END_REF]. Due to the increased interest in the assessment of TP53 status in hematological malignancies, an accurate validated method is needed. The most suitable technology must be evaluated not only according to the technical feasibility but also to cost, and clinical context. Immunohistochemistry (IHC) has been used for a long time to detect TP53 mutations easily. This method is based on the fact that the TP53 mutated protein accumulates in the cell because no longer degraded by MDM2. Nevertheless, mutations leading to the absence of the p53 protein are undetectable by IHC and represent, 6.06% of TP53 mutations in MDS (IARC database, January 2015). Also, normal p53 accumulates physiologically in stress, inflammatory and senescent situations [START_REF] Harris | The p53 pathway: positive and negative feedback loops[END_REF]. Thus the detection of p53 accumulation by IHC constitutes only an indirect, non-constant, consequence of a TP53 mutation [START_REF] Robles | Clinical outcomes and correlates of TP53 mutations and cancer[END_REF]. Especially in the del(5q) syndrome, the disease related p53 pathway activation is likely to impede the correct evaluation of the p53 status by IHC alone.

The FASAY method is a fast and cheap method, described in 1995, which does not require specific and expensive instrumentation. It must be followed by Sanger sequencing if one wants to characterize the alteration. In our study, FASAY combined to Sanger sequencing appears to be a reliable method to detect TP53 mutations in MDS, as also recently described in the CLL context [START_REF] Kantorova | TP53 mutation analysis in chronic lymphocytic leukemia: comparison of different detection methods[END_REF]. The major limitation of this technology is the need of good quality RNA. Nevertheless, high RNA quality can easily be obtained by blood or bone marrow sample collection with specific reagents stabilizing RNA and avoiding its degradation [START_REF] Yamamoto | Examination of stability of bone marrow blood RNA in the PAXgene tube[END_REF]. The FASAY method has a lower sensitivity compared to NGS but, in our study, the percentage of red yeast colonies (mutated alleles) was often high and allowed an accurate evaluation of TP53 status. Indeed, 67 patients had del(5q)MDS and strong evidence showed that haploinsufficiency for the long arm of chromosome 5 leads to p53 pathway activation and increased levels of TP53 mRNA and p53 protein in this MDS subtype [START_REF] Barlow | A p53-dependent mechanism underlies macrocytic anemia in a mouse model of human 5q-syndrome[END_REF][START_REF] Dutt | Haploinsufficiency for ribosomal protein genes causes selective activation of p53 in human erythroid progenitor cells[END_REF][START_REF] Pellagatti | Induction of p53 and up-regulation of the p53 pathway in the human 5q-syndrome[END_REF][START_REF] Nian | SPARC silencing inhibits the growth of acute myeloid leukemia transformed from myelodysplastic syndrome via induction of cell cycle arrest and apoptosis[END_REF]. This could explain why, on RNA, TP53 mutations were easily found by FASAY followed by Sanger sequencing with quite high percentage of red colonies (median of red colonies = 55%) even when these alterations were detected on DNA at very low level by NGS (median percentage of mutated alleles = 14%).

The main advantage of FASAY is the identification of the TP53 defect by its functional impairment and the exploration of abnormalities only present in RNA.

With the development of the new sequencing technologies, highly sensitive methods of sequencing are now available in routine laboratories. It allows the detection of many mutations at low and very low (deep sequencing) rate in all types of diseases, especially in cancers. It is usually performed on DNA, easily obtained, but needs large laboratories with heavy initial cost for instrumentation but becomes cost-effective if multiple samples are explored together.

In our hand, a combination of different methodologies could help to circumvent known pitfalls of each technique. In a few cases, FASAY followed by Sanger sequencing was helpful to identify some mutations by NGS, such as insertions and deletions. In a few other cases, NGS allowed the detection of some point mutations, which were present at a very low rate. Finally in 2015, while the cost of NGS analysis for TP53 mutations is evaluated at around 200 dollars when performed alone (and around 1000 dollars when combined to analysis of the 30 main other genes involved in MDS and AML) the cost of the FASAY technique is around 20 dollars (prices including reagents only), with 10 dollars additional costs if completed by Sanger sequencing.

Taken together, our data suggest that FASAY in combination with Sanger sequencing is a reliable and cost-effective method to detect TP53 mutations in MDS, especially in del(5q) syndrome. Notably, it allows the detection of insertions/deletions currently not well detected by NGS and gives a functional information, which can be helpful in clinical practice.

Fig. 1 .

 1 Fig. 1. Location of TP53 mutations identified in this study.

Table 1

 1 Comparison of patients analyzed with FASAY and NGS methods.

	NGS
	MUT	WT

Table 2

 2 Detailed results of discordant cases between FASAY+Sanger and NGS. % of red colonies in the global test/% of red colonies in the 5 ′ split version/% or red colonies in the 3 ′ split version. ND, not done; NA, not applicable; NR, not reported.a In that case SPLIT test was performed since the result of the global test showed a result close to the positivity rate.

	UPN	FASAY (% of red	Mutation by Sanger	Mutation by NGS	% of mutated	Number of	% of this mutation among 918
		colonies	sequencing		alleles NGS	mutations in IARC	TP53 mutations in the
		FASAY/spli5 ′ /split3 ′ )				database	hematopoietic system in IARC
							database
	1	36/4/29	Intron 8 insertion:	WT	NA	0	0
			p.A307Gfs*31
	2	20/1/29	Intron 9 insertion:	WT	NA	NA	NA
			p53␤ isoform
	3	3/ND/ND	WT	p.R248Q	6	942	8.93
	4	8/2/3 a	WT	p.P222S	3	3	NR
	5	98/92/7	Exon 4 deletion:	p.M246V	10	61 (M246V)	0.65 (M246V)
			p.G59Tfs*44
	FASAY result (x/y/z):

Table 3

 3 Detailed results of patients with several mutations. NR, not reported.

	UPN	FASAY	Mutation by Sanger	Mutation by NGS	% of mutated	Number of	% of this mutation among 918
			sequencing		alleles NGS	mutation in IARC	TP53 mutations in
						database	hematopoietic system in IARC
							database
	6	55/30/18	p.R175H	p.R175H	11	1210	4.79
			Not detected	p.C238Y	2	89	0.65
			Not detected	p.G245D	3	162	0.33
	7	75/58/16	p.C176R	p.C176R	15	15	NR
			Not detected	p.G245S	8	454	1.74
	8	79/43/68	p.V216M	p.V216M	28	80	0.22
			p.P152L	p.P152L	8	87	0.22
			Not detected	p.R209Lfs*38	28	0	NR
	9	54/4/37	p.V272L	p.V272L	34	52	NR
			Not detected	p.T231 Y234dup	19	0	NR
	10	85/6/60	p.C238F	p.C238F	12	45	0.11
			p.R273H	p.R273H	13	851	2.61
	11	29/3/34	p.R282W	p.R282W	8	602	1.2
			Not detected	p.C238Y	2	89	0.61
	12	44/4/33	p.R249G	p.R249G	25	53	NR
			p.S127F	p.S127F	5	32	NR
	13	27/1/42	p.R280G	p.R280G	10	45	0.33
			p.R248G	p.R248G	8	26	0.22
	14	72/51/43	p.A161D	p.A161D	36	23	NR
			p.S241C	p.S241C	32	35	0.44

Acknowledgments

The authors want to thank Cent pour Sang la Vie! and Fondation de France for their funding support. They thank O. Flinois and B. Filliol for their help in performing the FASAY experiments.

Conflict of interest

The authors have no conflict of interest to declare.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.leukres.2015. 07.001.


J Truc

D Sidobre

R Alami

ICCAS Conference -KHAOS improvements using BSpline

INTRODUCTION

In our previous work, we presented KHAOS Truc et al. (2022), a Kinematic Human Aware Optimizationbased System for Reactive Planning of Flying-Coworker in the context of the "Flying Co-Worker" project1 : a multi-rotor drone that collaborates with workers to fetch small objects. The robot must fly in a human-populated area, where only a handful of human workers may be "aware" of the robot's current task and mission, and a fraction of them may be involved in the physical interaction (e.g., object delivery): the robot must assume that most humans are "observers", i.e., they ignore its current mission and are not involved with it. In such conditions, the drone needs to carefully plan its 3D motion in a reactive way to navigate and act safely in close proximity to humans. Beyond safety, the drone should aim at exhibiting navigation strategies that are, as much as possible, socially aware: for example, it should avoid fast movements that could scare the observers; it should maximize its visibility [START_REF] Sisbot | A Human Aware Mobile Robot Motion Planner[END_REF] for workers, especially when engaging in an interaction. To address the navigation requirements mentioned above, we proposed KHAOS for reactive planning, which produces trajectories in the 3D space satisfying the kinematic constraints of the drone and ensuring the visibility and ease of the humans present in the environment. The human-aware behavior is realized by proposing a visibility cost and a novel discomfort cost and including these along with the kinematic constraints into a stochastic optimization process inspired by the STOMP algorithm [START_REF] Kalakrishnan | STOMP: Stochastic trajectory optimization for motion planning[END_REF]. In this paper, we first show the benefits of an extension of Softmotion [START_REF] Sidobre | Smooth cubic polynomial trajectories for human-robot interactions[END_REF] using BSpline as input to KHAOS to smooth the initial path. Then, in two different scenarios, we show the interest of using BSpline to improve the trajectories generated by KHAOS and to ensure that they are feasible.

INITIAL PATH SMOOTHING

STOMP algorithm optimize an initial path from a set of costs to generate a new smoothed path. This smoothing is dependent on the initial path, indeed STOMP does not allow to smooth the too important breaks of the initial path. These breaks will remain in the final result and may even be amplified. By inheritance, KHAOS suffers from the same problem and thus requires smoothing the initial input path in order to obtain a satisfactory output. Moreover, it generates a 3D trajectory constrained by kinematic limits (bounded velocity, acceleration and jerk) and human-aware costs that can also have an impact on the kinematic parameters. This trajectory must therefore respect the feasibility from a kinematic point of view by avoiding, for example, too sharp turns that would force the robot to slow down during execution.

To this end, we propose the use of the SoftMotion library and in particular an extension of the well known Non Uniform B-Spline [START_REF] Piegl | The NURBS book[END_REF]; [START_REF] Rousseau | Optimal trajectory planning and predictive control for cinematographic flight plans with quadrotors[END_REF] to generate feasible trajectories from a list of waypoints. The main advantage of this extension is to allow the control of the kinematic parameters at the beginning and at the end of the trajectory, which is necessary to ensure continuity between consecutive trajectories generated by two successive plannings. Many planners such as PRM or RRT allow to generate an initial path connecting a start point and an end point. Between each waypoint composing this initial path, we have segments that by nature do not allow to respect a continuity. The use of BSpline will allow to generate a smooth trajectory from this list of waypoints even when they are numerous and very close. In Fig. 1, We compare the outputs of the standard version of KHAOS when no smoothing is applied (Fig. 1a) and when a B-Spline is used (Fig. 1b). When no smoothing is performed, we show that even after many iterations, the continuity defects present in the initial path are transmitted to the KHAOS result. On the other hand, when using B-Spline, we can observe that the trajectory generated by KHAOS is very smooth too. This allows our algorithm to start on a good basis and will facilitate the optimization phase because naturally the continuity will be respected.

KINEMATIC COMPLIANCE

In addition to smoothing and giving a harmonious shape to the trajectory, this SoftMotion extension allows, from the list of waypoints from KHAOS, to ensure continuity from a kinematic point of view but also to respect the human-aware constraints. Indeed, KHAOS provides the position and speed for each waypoint according to constraints adapted to humans in the environment but also considering the kinematic limits of the drone. For example, if the drone starts far enough from any human and outside its visual field, it will accelerate to its maximum speed at maximum acceleration (Fig. 2 between 0 s and 2 s). When it is close enough to a human, the human-aware costs will become important. When approaching a human, for a handover for example, the drone will undergo a deceleration (Fig. 2 between 8 s and 14 s) dictated by the human-aware costs but KHAOS also verifies that this deceleration is achievable by the drone considering its deceleration limits. If it is not feasible from the kinematic limits point of view, KHAOS will generate a trajectory where the drone will slow down earlier so that it is feasible and that the human-aware constraints are also respected. We wish that between each of these waypoints (segments), the respect of these constraints is ensured. For example, in order not to cause too much discomfort to humans in the environment, KHAOS makes sure, among other things, to limit the speed according to the distance to the human. It is therefore necessary to be sure that between two positions provided by KHAOS, the speed along the segment is in agreement with the speeds given at each of these two positions. If we look for example at the KHAOS curve not using BSpline (Fig. 2 in green), we notice around 1.8 s and 8 s, sharp changes if we consider simple line segments connecting the waypoints. These defects are corrected by using the BSpline (Fig. 2 in orange), allowing on the one hand to ensure that it is physically feasible by the drone, but especially to obtain a more fluid movement and therefore less uncomfortable for the human present in the vicinity.

Figure 2: Magnitude of the drone's speed before (green) and after (orange) using BSpline in the KHAOS output in the scenario where the drone approaches a human from the front in a straight line.

The time bases do not match between the two curves because the calculation of the times is different between the calculation of the waypoints and SoftMotion

TWO HUMANS

We take the example of the corridor with two humans presented in our previous paper [START_REF] Truc | Khaos: a kinematic human aware optimization-based system for reactive planning of flying-coworker[END_REF].

The objective for the drone is to navigate from one end of the corridor (Fig. 3a), pass the human in blue, then arrive from the back of the human in green (Fig. 3b) in order to position itself in front of him as if it wanted to exchange an object with him. We show in Fig. 4, the magnitude speed profile along the trajectory actually executed by the drone by concatenating the trajectory portions generated at each iteration of KHAOS. Indeed, another advantage of our SoftMotion extension, is to generate trajectories from a non-zero speed and acceleration set-point needed as input by KHAOS in a reactive planning context. In the situation presented here, the human-aware constraints as well as the environment of the corridor constrains the trajectory of the drone, forcing it to pass close to the human in blue and thus reduce its speed (Fig. 4 between 3 s and 13 s), once out of the influence of the first human, it can accelerate again. Once it gets close enough and in the back of the human in green, it must slow down again (Fig. 4 between 15 s and 20 s) and turn around the human to appears in the human visual field before finishing its approach. We can notice how the speed profile is smoothed when the drone approaches the speed limit, or when it has to make a sharp turn (around 20 s).

CONCLUSION

The use of BSpline is a real gain for KHAOS, allowing our algorithm to propose more realistic trajectories that can be directly used by a basic controller. As we have shown, this makes it possible to improve the initial path at the input of KHAOS and also to ensure to provide at the output a trajectory achievable by the drone and respecting the human-aware constraints even better. From the human-aware point of view, it also improves the behavior of the drone whose movements are more fluid and cause even less discomfort than with the standard version. We also presented in our scenario with two humans, the final result of a trajectory executed by the drone and composed of many iterations of KHAOS, showing the ability of our Softmotion extension to generate BSpline resuming the non-zero speed and acceleration set point of the previous iterations. In the near future, we want to improve this extension of SoftMotion by adding an optimization step to further refine the kinematic parameters. Also, we plan to explore the possibility of implementing the use of BSpline directly in the core of KHAOS, in order to work directly on feasible trajectories at the optimization level.

Figure 1 :

 1 Figure 1: Shapes of the initial path (red) and trajectory generated by KHAOS (green) after several iterations for two cases: a) when no smoothing is applied to the initial path and b) when BSpline are used to smooth.

Figure 3 :

 3 Figure 3: Trajectory execution in a two-humans scenario. a) First trajectory generated by KHAOS, the drone starts by navigating in the corridor. b) A few iterations later, after passing the blue human in the corridor. Red line: represents initial path before smoothing using BSpline. Red arrows: highest speed magnitude. Green arrows: lowest speed magnitude.

Figure 4 :

 4 Figure 4: Magnitude of the drone's speed using BSpline composed of many iterations of KHAOS in the scenario with two humans.

This work was partially supported by the French National Research Agency (ANR) (project Flying Co-Worker, https: //www.laas.fr/projects/flying coworker, grant ANR-18-CE33-0001), the European Commission (AnDy, GA. n. 731540; CHIST-ERA -HEAP) and the Artificial and Natural Intelligence Toulouse Institute -Institut 3iA (ANITI) under grant agreement No: ANR-19-PI3A-0004.


Paul Ortiz

Sylvain Kubler

Éric Rondeau

CANO: A lightweight CArbon emission and inhabitants' energy Needs Optimisation model

Keywords: Distributed Energy Resource Integration and Coordination, Smart grids, Intelligent control of power systems, Control of renewable energy resources, Renewable Energy System Modeling and Integration

Greenhouse gas emissions (GHGE) can be attributed at 30% to the residential sector in Europe. Several solutions are possible to reduce these emissions, whether by improving the insulation of houses or by increasing the e ciency of the integration of renewable energy and storage resources. The research presented in this paper falls within the scope of the last alternative (i.e., management of renewable energy and storage resources). Most of today's energy management systems rely on mathematical programming or metaheuristics to optimize the storage and release of energy. While they provide high-quality optimum or near-optimal solutions, they are computational-intensive and time-consuming, which make them non-suitable to be deployed at edge architecture. To overcome this limitation, this paper suggests adopting a rule-based optimization approach, proposing a lightweight model seeking to reduce GHGE, while meeting the inhabitants' energy needs. This new model, which is an evolution of the model proposed by the European project RED WoLF, is called CANO (standing for "lightweight CArbon emission and inhabitants' energy needs optimization model"). This paper presents, evaluate, and compares CANO to the RED WoLF algorithms based on two real datasets from two di↵erent countries (UK and France). The results evidence that CANO outperforms RED WoLF, and most importantly that it can satisfy all heating demands, which was not the case with the initial version of RED WoLF (only satisfying 80% and 96% of the demands respectively in France and the UK scenarios).

INTRODUCTION

In Europe, the residential sector is responsible for a significant part of the greenhouse gas emissions (GHGE) causing global warming [START_REF] Baek | Potential e↵ects of vacuum insulating glazing application for reducing greenhouse gas emission (ghge) from apartment buildings in the korean capital region[END_REF]. This problem is mainly due to three factors (i) poor insulation of houses and buildings; (ii) a weak integration of distributed energy resources (DER) or the use of polluting production means in the national grid energy source mix [START_REF] Lazarus | Fossil fuel supply and climate policy: exploring the road less taken[END_REF]; (iii) a lack of optimization algorithms for reducing GHGE while maintaining inhabitant comfort and budget [START_REF] Wang | Ten questions concerning future buildings beyond zero energy and carbon neutrality[END_REF].

Di↵erent solutions can be envisioned such as improving houses and buildings insulation, increasing renewable energy generator proportion (e.g., wind turbin, biomass) [START_REF] Ahmed | Assessment of the renewable energy generation towards net-zero energy buildings: A review[END_REF]. Other solutions such as the one investigated in this research article, propose to power houses with distributed energy resources (e.g., photovoltaic array, battery system) to produce and store locally green energy [START_REF] Hannan | Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues[END_REF]. However, it is not only a matter of installing PV arrays and storing solar power in a battery, but also a matter of optimizing the charging/discharging strategy of local storage units [START_REF] Al-Shahri | Solar photovoltaic energy optimization methods, challenges and issues: A comprehensive review[END_REF], knowing that green local production may not be su cient to meet the house demand. In other words, the total energy generated locally may not be su cient to meet the inhabitant needs, thus requiring to draw electricity from the electrical grid, which is often not carbon free. There is thus a need to design optimization algorithms to search for optimal time intervals for drawing electricity from the grid, while taking into account the overall IT (hardware and software) infrastructure (i.e., avoiding to deploy computational intensive optimization algorithms). To achieve this objective, an algorithm named RED WoLF (Rethink Electricity Distribution Without Load Following) has been proposed in previous research by the European Interreg NWE RED WoLF (2019) project. Although the algorithm foundation and philosophy are promising, some improvements are necessary to ensure that the energy needs are always met. To overcome those limitations, a new algorithm named CANO (standing for house CArbon emission and inhabitant Needs Optimisation) is proposed in this article.

Section 2 provides a brief state of the art of existing optimization algorithms for energy management considering RES (standing for Renewable Energy Sources) and storage systems, along with explanations why the RED WoLF philosophy is interesting to reduce the computational burden traditional algorithms (making it suitable to be deployed at the Edge of the electrical grid such as on smart meters) and what limitations still remain to be solved. The proposed CANO algorithm is then introduced in Section 3, while section 4 focuses on the comparison analysis between RED WoLF and CANO algorithms. This comparison study is performed using real-life datasets from France and UK. Conclusion is given in section 5.

STATE-OF-THE-ART

Section 2.1 defines the energy subfield in which our contribution is situated. Section 2.2 explains the di↵erent links between our research and the state of the art. et al. (2019) describe the life cycle of electrical energy as a succession of the following phases: 1. production, 2. transmission, 3. distribution and 4. consumption. This article contribute in the consumption management phase and more specifically in a residential nanogrid [START_REF] Burmester | A review of nanogrid topologies and technologies[END_REF]. As denoted by Fig. 1, a nanogrid is usually composed of five categories:

Positioning

Saleem

• Distributed Energy Resource: corresponds to local energy production sources that can be installed at the house to provide additional energy, often renewable (e.g., photovoltaic array, wind turbine, etc.); • Conventional Energy Resources: corresponds to national power plants from renewable or non-renewable sources, often of significant size (e.g., nuclear power plant, hydroelectric dam, solar farm, etc.); • Electrical Loads: corresponds to all devices that consume energy (e.g., appliances, HVAC, etc.); • Storage Equipment: corresponds to all the equipment having the capacity to store energy in any form to release it at more convenient time (e.g., Battery Energy Storage System (BESS), storage heater, water cylinder, etc.); • Control/Monitoring equipment: corresponds to all the equipment for charging, measuring, transferring, processing, executing energy-related data and commands (e.g., meter, communication network, switch, etc.);

State-of-the-art

This section introduces state-of-the-art research articles dealing with the optimization of energy storage systems, along with their pros and cons. In the literature, two categories of optimization can be defined: (1) mathematical and metaheuristic optimization; and (2) rule-based optimization. In the following, key research articles falling within each of these categories are discussed, with a particular focus on assessing the extent to which the proposed [START_REF] Feng | Smart grid encounters edge computing: Opportunities and applications[END_REF].

Mathematical and metaheuristic programing provide optimal solution(s) or highly accurate optimum(s) demonstrating that this choice is legitimate for the optimization of energy storage systems. [START_REF] Olivieri | Optimization of residential battery energy storage system scheduling for cost and emissions reductions[END_REF] propose a linear optimization model to find the optimal solution to charge/discharge a battery with the national grid as the only energy source. The fitness function can be defined using one or more objectives: (1) minimizing GHGE;

(2) minimizing financial cost; (3) minimizing emissions and financial costs. The control strategy is computed based on 48h appliances consumption and grid carbon load prediction and performed by a smart meter with processing capabilities. Likewise, [START_REF] Haidar | New consumerdependent energy management system to reduce cost and carbon impact in smart buildings[END_REF] propose a linear optimization model, which di↵ers from the previous one, as the authors consider three distinct energy sources: (1) non-renewable energy; (2) renewable energy; (3) local generator. The proposed optimization model seeks to find the best combination of energy sources and the best time slots to charge/discharge the battery, with the overall objective to reduce carbon emissions and/or financial costs, while meeting the inhabitant's energy needs. Furthermore, this research paper introduces a parameter sets by the inhabitant aiming at fixing the acceptable additional financial cost to further reduce his carbon emission. Nevertheless, even though they provide extremely powerful solutions, the computational resource needed to run the algorithm is still very high, which hampers its deployment using Edge computing solutions.

Rule-based optimization provides computationally lightweight strategies, thus making it an interesting choice for an edge computing based architecture. [START_REF] Mahmud | Peak-Load Reduction by Coordinated Response of Photovoltaics, Battery Storage, and Electric Vehicles[END_REF] proposes a rules based optimization to control the charging/discharging of a stationary battery and an electric vehicle (EV) with vehicle to grid (V2G) capability, which is considered as both an electrical load and a storage system to increase the overall system capacity. This algorithm deals with two energy sources: national energy grid and local PV system in order to reduce the peak load, and indirectly the carbon emission and financial costs. To go along the smart meter with processing capabilities there is a monitoring infrastructure in ordre to execute the decision tree strategy, formalized in this research article. The model proposed by [START_REF] Shukhobodskiy | Red wolf: Combining a battery and thermal energy reservoirs as a hybrid storage system[END_REF] and [START_REF] Ortiz | Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs[END_REF] introduces a hybrid energy storage system (HSS) based on a BESS (standing for Battery Energy Storage System) and thermal reservoirs. A two-step approach is defined to generate the HSS charge/discharge strategy: (1) every hour a CO 2 threshold limit is calculated; (2) every minute, a command is generated based on a rule system (e.g., if current CO 2 is lower than CO 2 threshold then HSS charging is allowed). This model allows a fast execution of the command but requires a constant internet connection in order to access the appliances, solar production and grid carbon load predictions and current value. While the underlying philosophy is interesting, we realize that the proposed model does not fully meet the inhabitants' energy needs. To illustrate this state-of-the-a↵aire we analyzed in Fig. 3b the extent to which the initial RED WoLF model is meeting the houses' energy demand considering the two use case scenarios/datasets defined in [START_REF] Ortiz | Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs[END_REF] (namely a house from UK and France). Results show that in both cases the house energy demand is not fully satisfied (only satisfying 80% and 96% of the demands respectively in France and UK scenarios). To adress this problem, a new model called CANO is formalized and proposed in the next section.

CANO STRATEGY

In this section, the CANO strategy will be presented in further details. Section 3.1 deals with the hardware infrastructure used for the reduction of greenhouse gas emissions, while section 3.2 will provide an overview of the software part that controls the system.

Hardware

The Fig. 2 shows three di↵erent types of equipment: the energy sources that will power the house and whose carbon intensity is more or less important depending on the type of source and the time of day (e.g., utility grid, solar array, etc.); the appliances that cannot be controlled and will have to be powered based on demand of the inhabitant (e.g., TV, oven, computer, etc.); Finally, the energy storage that o↵er the capacity to store energy in di↵erent forms to release it at more opportune times during the day (e.g., water cylinder, storage heater, electrochemical storage, etc.).

This figure also provides a high level view of the hardware architecture. Three flows are highlighted in this figure: the power flow representing the high voltage network coming from the grid to supply the equipments (e.g., hybrid storage system, appliances, etc.); the monitoring flow schematizing the network infrastructure necessary to retrieve the di↵erent local data (e.g., solar production, appliances consumption, etc.); the control flow representing the sending of the charging/discharging commands from the PLC to the controllable equipments (e.g., BESS discharging, charge storage heater, etc.)

Software

The table 1 provides the list of variables used by the algorithm, their units associated with a description as well as their types indicated by the Class column (e.g.,

Appliances

Hybrid Storage System PLC / Edge computing HSS Control Monitor Power Fig. 2. Hardware infrastructure Output). The variable C represents the prediction of the carbon intensity of the electricity supplied by the national grid. Like variable C, the vector E represents the prediction of the instantaneous power available over the current minute for charging the hybrid energy storage system. E is computed by subtracting the predicted consumption of the appliances from the maximum available power (sum of the limit set by the energy provider and the instantaneous solar production). The scalar L is defined by the sum of energy based on the heating setpoints of the storage heater, the cylinder and the capacity of the battery. It thus ensures that the required comfort level is reached. The transfer matrix M allows the ascending sorting of the vectors C and E according to the carbon intensity (C). Finally, the Boolean vector S represents the best moments to consume the energy supplied by the electrical network. The algorithm presented in this research article is a threshold-based computational strategy inspired from the algorithm proposed by [START_REF] Shukhobodskiy | Red wolf: Combining a battery and thermal energy reservoirs as a hybrid storage system[END_REF] as it used the same hardware infrastructure and computational philosophy (e.g., threshold-based). The generic problem addressed by the strategy proposed in this article is expressed by the equations 1 and 2. The objective being to minimize the greenhouse gas emissions, the optimization will aim at finding the vector S which minimizes the product expressed in the equation 1 while satisfying the load set point of the equipments (cf., equation 2).

minimize C t S, S 2 B 24/H (1) subject to E t S H L (2)
The computational resources provided by the PLC being limited (e.g., edge computing), equations 3 to 5 model the proposed heuristic to reduce the computational time/load required to generate the control strategy. The assumption on which this heuristic is based is : "by consuming the least amount of time on the grid and at the least carbon intensive times possible, a satisfactory optimum can be found". Thus, by sorting in ascending order the vectors C and E using the transfer matrix M (cf., equation 5), it is possible to simplify the initial optimization model by searching for the minimum time sum needed to reach the HSS setpoints by supplying with electricity grid (e.g., sum of the S vector) computed using equations 3 and 4.

minimize Finally, the CO 2 threshold can be obtained by using the sum of the S vector as an index of the C T vector, thus giving the command value below which it will be possible to charge the HSS via the electrical network.

C t T S, S 2 B 24/H , S = (1, . . . , 1, 0, . . . , 0) (3) subject to E t T S H L (4) with C T = C t M and E T = E t M (5)

EXPERIMENTAL EVALUATION

This section is dedicated to the analysis of the results of the comparison between the algorithm used in the RED WoLF project [START_REF] Shukhobodskiy | Red wolf: Combining a battery and thermal energy reservoirs as a hybrid storage system[END_REF] and [START_REF] Ortiz | Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs[END_REF] and the model presented in Section 3. These two models use the same infrastructure and the same inputs as shown in Fig. 2. Both strategies use the same type of optimization, namely the use of a heuristic allowing the fast optimization (compared to a mathematical or metaheuristic optimization) of the di↵erent phases of charge/discharge of the equipments composing the hybrid energy storage system but CANO adds demand management constraint.

The datasets used to generate the results are detailed in Section 4.1. Section 4.2 presents the results obtained on a specific day for both models. Finally, Section 4.3 is dedicated to the analysis of the monthly results.

Input datasets

As illustrated in Fig. 3, both algorithms deal with appliances consumption, photovoltaic production and grid carbon load. For a fair comparison, both algorithms use the same state-of-the-art datasets and fully accurate prediction (e.g., real values use as predictions too). Four input data sources are used regarding the conducted experiments:

• Appliances:

1-day analysis

First of all, the results of the comparison of the CANO and RED WoLF algorithms will be compared over a one-day horizon from the whole October 2019 month with the UK dataset. This day has been selected to highlight a number of parameters that will be detailed later in this section. To perform these experiments, two parameters have been set: the physical characteristics of the battery (capacity: 6.5 kWh, maximum charge and discharge power: 4.2 kW), as well as the maximum power drawable from the grid set by the utility provider (9 kW). Fig. 3a (i) and 3b (i) provide quick view of power generate from PV array (PFP), power consume by appliances (P2A) and power send back to the grid (P2G) respectively for CANO and RED WoLF over the day. The two second graphs provide evolution overview of the storage equipment state of charge (HLV : storage heater level, CLV: cylinder level and BLV: battery level).

The dashed lines of the same color as HLV and CLV (solid lines) show the level demanded by the inhabitants over the day on these two storage systems. If the dashed line does not reach the full line, this will result in an area of discomfort such as lack of hot water for a shower or other inconvenience. In the next section, each unsatisfied day will be counted over a month and given as a percentage as "daily needs filled" in Fig. 4. The grid carbon intensity (CO2) and the CO 2 threshold (THR) are provided by the thirds graphs of the Fig. 3. Finally, the last graphs show the power consumed at each moment of the day, the color representing the power intensity range drawn from the grid (e.i., red 7 kW; 7 kW > yellow 3.5 kW; green < 3.5 kW). 3b (i) show that for both algorithms in the case of this particular day, the performances related to self-consumption are exactly the same (i.e., no injection to the grid). On the other hand, the times of the day when the grid is solicited are totally di↵erent. Indeed, we notice on Fig. 3b (iv) that the strategy adopted by RED WoLF is to charge the battery at the maximum at the beginning of the day to be able to isolate itself completely from the grid. The strategy presented in this article will divide the charge on periods where the carbon intensity is lower, even if it means to use the grid in case of peak consumption of the appliances. Moreover, it could be denoted that these behavior are tied to the CO 2 threshold command Fig. 3a (ii) and 3b (ii) underline that in some cases, the thermal storage needs (e.g., storage heater and water cylinder respectively represented by blue and orange dashed line) are not satisfied until the end of the day with the algorithm proposed by [START_REF] Shukhobodskiy | Red wolf: Combining a battery and thermal energy reservoirs as a hybrid storage system[END_REF] and [START_REF] Ortiz | Greenhouse gas emission reduction system in photovoltaic nanogrid with battery and thermal storage reservoirs[END_REF]. This leads to a lack of energy for space heating and dwelling hot water, thus reducing the inhabitant's comfort. On the other hand, this tendency is not present with the strategy proposed in this article. Finally, one last thing about the number of charges specific to each of the two solutions. Based on Fig. 3a (ii) and 3b (ii), it is possible to see that the CANO model on this particular day uses a larger amplitude of charge/discharge of the battery (e.g., about 3 charge/discharge against 2 for RED WoLF) which could reduce the lifetime of the battery on a longer horizon [START_REF] Hu | Battery lifetime prognostics[END_REF].

1-month analysis

In order to get a more representative view of the performance of the CANO and RED WoLF algorithms, this section aims to analyze the results over a month. Fig. 5 provides an overview of the energy flows into/out of the house. First, the energy produced by the photovoltaic panels (denote PFP) is common to both algorithms as it is an input to both optimization systems. The energy injected to the grid (denoted P2G) is the part of energy produced by the solar installation that can neither be used by the appliances nor stored in the storage system. Finally, the energy imported from the grid (denote PFG) is a variable controlled by the algorithms and therefore provides an indicator for comparison.

From the Fig. 5, two points can be highlighted: first, the results for each algorithm are quite close, with the exception of the 5 th day, thus justifying the choice to analyze this day specifically in Section 4.2; second, by comparing Fig. 5 (i) and (ii), It can be observed that CANO outperforms RED WoLF on the self-consumption indicator by injecting less locally generated energy to the national power grid.

Until now the results presented in this paper were based only on the UK dataset, Fig. 4 provides a quick view of the results for both UK and France (Fig. 4(i) and (ii)). This figure allows to quickly identify which algorithm is the best with the color code (green corresponding to the best case) according to the parameter and the country of interest. First of all, it could be denoted that for the first two indicators (see Fig. 4 carbon emission and financial cost) and in both countries, the results are very close but in favor of CANO. Secondly, the computation time indicator denote that for both country, the RED WoLF strategy is twice less time consuming than the strategy propose in this article. This di↵erence could be attributed to the rough cumulative sum action in the CANO process where RED WoLF use a numerical integration task. Then, the results given by the energy throughput indicator shows an inversion between the two datasets while remaining very close. The big di↵erence between the results for France and the UK comes from the fact that the house chosen in the UK consumes less (about twice as much). Table 2 exposes a prediction of the battery life according to the use that the algorithms make of it. Note that the greater this indicator is the shorter the battery life will be. Then, on the Fig. 4, the PV self-consumption indicator shows the share of the local production used to power the appliances or stored in Day of the month Energy consumption production (in kWh)

Energy consumption production (in kWh) (i) CANO (ii) RED WoLF Fig. 5. day 5 -1-day analysis the hybrid energy storage system. Two comments could be made: (i) in France, the performance is very close between both algorithm but lower than in the UK; (ii) in the UK, the gap is larger in favor of the model proposed in this paper. Finally, the last but not the least, the daily needs filled indicator highlights probably the biggest advantage of the CANO model: capacity to always fulfill the heating needs. For both countries, CANO reaches the water cylinder and space heating setpoints whereas RED WoLF failed to do the same in one and four days respectively in UK and France.

CONCLUSION

This research presents the CANO algorithm which is an evolution of the RED WoLF algorithm. Both algorithms are based on the same facility and therefore have an equivalent initial cost. They also have the advantage of being less demanding in terms of computer resources as the processing is simple compared to exact optimisation methods. The performance comparison between CANO and RED WoLF are relatively close but still to CANO's advantage in terms of reduced carbon emissions, lower electricity bills and increased battery life. On the other hand, CANO's strong point is that it always satisfies the needs of the inhabitants for energy storage, both HLC and CLV, and thus ensures optimal comfort throughout the day. This point is essential in the development of intelligent solutions in the environmental field. Indeed, it is important that the preservation of the planet is not done at the expense of people's comfort and to show that we can find win-win solutions, (i.e., win for the environment and win for the inhabitants). CANO is an algorithm that allows to have this sustainable vision from both a social (people's well-being) and environmental point of view. To go even further in a social direction, we are currently looking at extending CANO to eco-neighbourhoods for battery stored electricity sharing amongst a cluster of homes within the community.

  Fig. 3. Indicators overview

  Fig. 4. Monthly indicatorsFig.3a(i) andFig. 3b (i) show that for both algorithms in the case of this particular day, the performances related to self-consumption are exactly the same (i.e., no injection to the grid). On the other hand, the times of the day when the grid is solicited are totally di↵erent. Indeed, we notice on Fig.3b(iv) that the strategy adopted by RED WoLF is to charge the battery at the maximum at the beginning of the day to be able to isolate itself completely from the grid. The strategy presented in this article will divide the charge on periods where the carbon intensity is lower, even if it means to use the grid in case of peak consumption of the appliances. Moreover, it could be denoted that these behavior are tied to the CO 2 threshold command

Table 1 .

 1 Variables

	Class	Variable	Unit	Description
		C	ge q .C O 2	grid carbon load
	Input	E	k W	p o w e ra v a i l a b l ep r e d i c t i o n
		L	kWh	sum of HSS setpoint
		M	/	t r a n s f e r tm a t r i x
	Internal	C T	g eq. CO 2	sorted grid carbon load
		E T	kWh	sorted energy available
		H	h r s	i n t e r v a lt i m el e n g t h
	Output	S	/	solution vector {1,1... 0,0}

Table 2 .

 2 Battery lifetime

	Algorithm	Country	Throughput Warranty	Lifetime
	CANO	FR UK	514 kWh 273 kWh	20 MWh 20 MWh	3.2 years 6.1 years
	RED WoLF	FR UK	495 kWh 279 kWh	20 MWh 20 MWh	3.3 years 5.9 years

ACKNOWLEDGEMENTS

This work has been supported by the European Regional Development Fund and Interreg NWE, project RED WoLF, project number NWE847.