Analysis of the operational flexibility of the Italian power system in 2030

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POLITECNICO DI MILANO SCHOOL OF MANAGEMENT

MASTER OF SCIENCE IN MANAGEMENT ENGINEERING ENERGY AND ENVIRONMENTAL MANAGEMENT

Analysis of the operational flexibility of the Italian

power system in 2030

Supervisor:

Prof. Vittorio Chiesa Co-supervisor:

Eng. Daniele Daminelli (CESI SpA) Tutor:

Eng. Cinzia Puglisi (CESI SpA)

Juan David Correa Laguna 10599972

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Abstract

In 2019, the European Commission published the “2030 Clean Energy for All Europeans” package, as part of the 2050 Energy roadmap. To comply with the European directives, Italy published the National Energy and Climate Plan setting a target of 55% for RES share in electricity generation by 2030. This high RES generation target raises concerns about the operational flexibility of the Italian power system. The flexibility of the Italian power system was measured using currently available flexibility frameworks and indexes (e.g.: AFPER, FAST2). Hence, the 2030 Base Scenario and 15 alternative scenarios were simulated using Promed Grid, a market tool developed by CESI and able to perform a coordinated hydro-thermal dispatching over a one-year horizon, considering the adoption of some flexibility sources such as storage systems and demand response. The results were then compared to characterize the performance of each flexibility source and its influence on the power system. BESS, PHS, and DR showed a level of complementarity while covering flexibility needs and controlling price spikes. Daily needs are covered mainly by BESS and weekly needs by PHS, mainly during the summer since both technologies were more used due to high solar power generation. Conversely, peak shedding DR was primarily activated in winter since prices are higher and storage systems lees used. In the case of V2G and DR, the role of aggregators is vital to have a representative capacity to be offered in the power markets. Finally, from a holistic perspective, the benefits of implanting all flexibility source are higher than the costs. Nevertheless, such benefits should be redistributed to incentivize the deployment of certain sources with low benefit-cost ratios. The results of this thesis can serve as a starting point to delve into the technical performance and economic implications of each flexibility source. In addition, it highlighted the advantages for Italy of boosting and supporting some schemes such as DR and V2G, as well as incentivizing the installation of storage systems.

Keyword: 2050 European Energy roadmap, Clean energy for all Europeans package,

Operational flexibility, flexibility sources, national energy and climate plan, system adequacy, residual load, BESS, PHS, V2G, demand response.

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Estratto in Lingua Italiana

Nel 2019, la Commissione europea ha pubblicato il pacchetto "2030 Energia pulita per tutti gli europei", nell'ambito della tabella di marcia per l'energia 2050. Per ottemperare alle direttive europee, l'Italia ha pubblicato il Piano Nazionale per l'Energia e il Clima che stabilisce un obiettivo del 55% per la quota FER nella produzione di elettricità entro il 2030. Questo obiettivo di alta generazione di FER, solleva preoccupazioni sulla flessibilità operativa del sistema elettrico italiano. La flessibilità del sistema elettrico italiano è stata misurata utilizzando i framework e gli indici di flessibilità attualmente disponibili (ad esempio: AFPER, FAST2). Quindi, lo scenario di riferimento per l’anno 2030 e 15 scenari alternativi sono stati simulati utilizzando Promed Grid un software sviluppato da CESI e capace di ottimizzare in modo coordinato il dispacciamento idro-termoelettrico su un orizzonte annuale, considerando l'adozione di alcune fonti di flessibilità come i sistemi di accumulo e il demand side response (DSR). I risultati sono stati quindi confrontati per comparare le prestazioni di ciascuna fonte di flessibilità e la loro influenza sul sistema elettrico. BESS, PHS e DSR hanno mostrato un certo livello di complementarità, coprendo al contempo le esigenze di flessibilità e controllando i picchi di prezzo. Le esigenze quotidiane sono coperte principalmente da BESS mentre le esigenze settimanali da PHS. L’utilizzo di entrambe le tecnologie si concentra durante il periodo estivo caratterizzato da una maggiore produzione solare. Al contrario, la riduzione dei picchi promossa dal DSR è stata attivata principalmente in inverno, periodo in cuii prezzi sono più alti e l’accumulo è meno utilizzato. Nel caso di V2G e DSR, il ruolo degli aggregatori è vitale per avere una capacità rappresentativa da offrire nei mercati dell'energia. Infine, da una prospettiva olistica, i vantaggi di impiantare tutte le fonti di flessibilità sopra elencate sono superiori ai costi. Tuttavia, tali benefici dovrebbero essere ridistribuiti per incentivare lo spiegamento di determinate fonti con rapporti costi-benefici bassi. I risultati di questa tesi possono servire come punto di partenza per approfondire le prestazioni tecniche e le implicazioni economiche di ciascuna fonte di flessibilità. la tesi ha inoltre messo in evidenza i vantaggi per l'Italia di potenziare e supportare alcuni schemi come DR e V2G, nonché di incentivare l'installazione di sistemi di accumulo.

Parola chiave: 2050 tabella di marcia europea per l'energia, pacchetto sull'energia pulita

per tutti gli europei, flessibilità operativa, fonti di flessibilità, piano nazionale per l'energia e il clima, adeguatezza del sistema, carico residuo, BESS, PHS, V2G, risposta alla domanda.

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Acknowledgments

I thank God for opening doors and walking me through them. Throughout the writing of this dissertation, I have received a great deal of support and assistance. I would first like to thank my co-supervisor and tutor, Daniele Daminelli and engineer Cinzia Puglisi at CESI S.p.A., whose expertise was invaluable in the formulating of the research topic and methodology in particular.

Besides my them, I would like to thank professor Vittorio Chiesa for taking from his time to be the supervisor of this thesis. His classes gave me the initial grasp to explore certain topics linked to the subject of this thesis.

Finally, I would like to show my appreciation to my family and friends, who have contributed in several ways to my personal, academic and professional path. Most importantly, I must express my very profound gratitude to my loving and supportive wife for her continuous encouragement throughout my years of study and through the process of researching and writing this thesis. This accomplishment would not have been possible without them

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List of Figures

Figure 2-1. The Indicative trajectory towards 2030 target - EU28. ... 15

Figure 2-2. Evolution of RES in gross energy consumption towards 2030. ... 16

Figure 2-3. Criteria of allocation of EU targets among the Member States. ... 18

Figure 2-4. Evolution of RES in the electricity sector in the EU. Source: Data adapted from Eurostat [3]. ... 19

Figure 2-5. Expected electricity generation capacity in the EU. Source: Primes-2016 [23]. 19 Figure 2-6. Indicative trajectories for the EU by sector, according to PRIMES-2016. Source: Data adapted from EC PRIMES-2016. ... 20

Figure 2-7. Contribution of renewable sources to gross domestic consumption of electricity. ... 21

Figure 2-8. Global weighted average CSP, solar PV, onshore and offshore wind project LCOE data to 2017 and auction price data to 2020, 2010-2020. Source: IRENA [57]. ... 22

Figure 2-9. Mix of renewables according to NECP and PRIMES2016 scenarios. Source: NECP and Primes-2016 [45][46][47][4][48]_{. ... 23}

Figure 2-10. Participation in the penetration of RES-E in Italy. ... 23

Figure 2-11. Expected evolution of electricity from RE and main contributions (TWh) ... 27

Figure 2-12. Indicative trajectories for the share of RES-E. ... 28

Figure 2-13. Share of RES in the final electricity consumption and the evolution of the installed capacity. ... 28

Figure 2-14. Distribution of pumping systems in Italy (2015). Source: TERNA Developing Plan 2018 [69]. ... 31

Figure 2-15. Potential for new pumping storage systems. Source: TERNA Developing Plan 2018 [70]. ... 31

Figure 2-16. Share of RES-E and detail by technology compared with the EU indicative tendency for Germany. ... 33

Figure 2-17. Electricity demand, RES-E and RES installed capacity in the UK. ... 36

Figure 2-18. 2030 Roadmap of the RES installed capacity by technology. ... 37

Figure 2-19. Share of RES-E in the gross electricity consumption by technology, compared with the EC indicative trajectory. Source: adapted from FES-National Grid, Renewable Directive and PRIMES-16 (EUCO30) [23][39][82]. ... 37

Figure 2-20. The share of RES-E from 2019 to 2030, assuming linear annual growth. ... 40

Figure 2-21. The share of RES-E from 2010 to 2030, assuming linear annual growth. ... 43

Figure 2-22. RES installed capacity by technology in 2017. ... 47

Figure 2-23. Share of the different RES technologies in the total generated electricity in 2017. ... 47

Figure 2-24. Share of RES-E in the electricity production according to the targets set in NREAP and NECP of each country. Source: Data adapted from EUROSTAT and, national NREAPs and NECPs [45][46][47][48][3]. ... 48

Figure 2-25. Installed capacity in 2017 and RES capacity targets according to NECPs. ... 49

Figure 3-1. Flexibility charts of France, Germany, Italy, Spain and the UK. ... 55

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Figure 3-3. (a) Time representation of Residual Load for half year. (b) Frequency representation of the DFT for half a year. (c) Frequency representation of Residual Load

with filter the frequencies within a day. ... 56

Figure 3-4. Sources of power system flexibility. ... 57

Figure 3-5. Residual load simulation. Source: ENTSO-E [125]. ... 60

Figure 3-6. Computation of the flexibility needs’ process as implemented by the European Commission in its report. Source: Adapted from European Commission [123]. ... 60

Figure 3-7. Provision of flexibility for each MS by 2030. ... 62

Figure 4-1. Installed power capacity mix by technology in 2017 and the expected in 2030. ... 66

Figure 4-2. Solar and wind generation profiles in GWh. Source: Adapted data from CESI [133]_{. ... 67}

Figure 4-3. Generation and transmission capacity for 2030 Base Scenario. ... 71

Figure 4-4. Input parameters for the simulation in Promed Grid. ... 73

Figure 4-5. The trajectory of BESS installation. ... 79

Figure 4-6. Lowest LCOS for 9 Electricity Storage Technologies in 12 Applications from 2015 to 2050. ... 81

Figure 5-1. Alternative scenarios by category ... 84

Figure 5-2. Adjusted potential for new PHS. ... 86

Figure 5-3. Types of Demand Response services. Source: Liu & RAP [153]. ... 90

Figure 5-4. Demand Side Management techniques. Source: SAIEE [154]. ... 91

Figure 5-5. Map of Explicit Demand Response development in Europe. ... 92

Figure 5-6. Price duration curve Base Scenario, only prices under 100 [€/MWh]. ... 94

Figure 5-7. Effect of the accepted bid of DR in the DAM. ... 95

Figure 5-8. Average hourly price elasticity of the Italian demand in the Base Scenario. ... 96

Figure 5-9. DR average monthly available capacity for load reduction and load increase. 97 Figure 5-10. (a) Modification of demand load profile due to the implementation of DR, average values for one week. (b) Day-ahead electricity price, average values for one week. ... 98

Figure 5-11. Load profile by charging location and the effect on the total demand profile. ... 99

Figure 5-12. Distribution of 1 million EVs in the Scenario-12. ... 101

Figure 5-13. Parameters of simulation of Scenario-14. ... 103

Figure 6-1. Annual energy generation by technology and transmitted energy between zones. All values are expressed in TWh. ... 107

Figure 6-2. Daily, weekly, annual flexibility needs for different BESS-PHS combinations ... 108

Figure 6-3. Charging and discharging BESS and PHS duration curve in Scenario-05, referred to installed ... 109

Figure 6-4. BESS and PHS charging and discharging profile, and solar generation profile during one average day. ... 109

Figure 6-5. Weekly profile of average discharging and charging profiles, referred to maximum installed capacity. ... 110 Figure 6-6. Annual average charging and discharging profile of BESS and PHS, referred to

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the maximum installed capacity. ... 110

Figure 6-7. Possible combinations of PHS and BESS, using a resolution of 1 GW. ... 111

Figure 6-8. BESS and PHS daily, weekly, and annual charging profile in relation to the average price. ... 112

Figure 6-9. BESS and PHS average State of Charge during the day, the week and the year. ... 113

Figure 6-10. Daily average thermal generation profile of the Base Scenario and Scenario-05. ... 113

Figure 6-11. Zoom-in to the price duration curve of the Scenarios 01, 02, 03, 04, and 05 ... 114

Figure 6-12. Capacity factor duration curve of BESS and PHS in Scenario-05. ... 114

Figure 6-13. Prices in different markets where BESS might be used. ... 115

Figure 6-14 (a) Bids in the day-ahead market. (b) effect of DR bid in the day-ahead market. ... 116

Figure 6-15. DR activations during the year in Scenario-10. ... 117

Figure 6-16. Variation of average national price in function of the activation cost modifications. ... 118

Figure 6-17. Numbers of activations of DR and energy accepted in the DAM. ... 118

Figure 6-18. Baseline profile and load profile modified by DR energy shifting in Scenario-11. ... 119

Figure 6-19. Variation of operational flexibility needs in Scenario-14. ... 121

Figure 6-20. Comparison of the annual national average price in the DAM between Base Scenario and Scenario-14. ... 122

Figure 6-21. Comparison of the price volatility between Base Scenario and Scenario-14. ... 122

Figure 6-22. Monthly energy used by DR and Energy Storage Systems in TWh. ... 123

Figure 6-23. Comparison of average price influenced by transmission capacity. ... 123

Figure 6-24. Tiers of benefits related to the implementation of flexibility sources. ... 124

Figure 6-25. Use of the four types of flexibility sources in the Base Scenario and Scenario-14 ... 127

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List of Tables

Table 2-1. Targets for RES in gross energy consumption. ... 16

Table 2-2. Growth targets and trajectories for 2030 for the renewables share in the electricity sector (TWh). ... 22

Table 2-3. Growth targets for power (MW) from RES. ... 23

Table 2-4.Main measures established to achieve the objectives of the PNEC. ... 26

Table 2-5. The indicative trajectory for the share of RES-E in gross electricity consumption. Source: adapted from German NECP [46]. ... 33

Table 2-6. Measures implemented to impulse the adoption of RES ... 34

Table 2-7. Summary of the UK’s policies outlined in the UK’s 7th National Communication. ... 38

Table 2-8. Principal policies and measures of the French Government. ... 41

Table 2-9.Installed capacity path according to Spanish NECP. ... 44

Table 2-10. Main measures implemented by the Spanish government according to the NECP. ... 44

Table 2-11. Share of RES in gross energy consumption and RES-E in electricity consumption by technology. ... 46

Table 2-12. RES-E targets and actual value of 2015. ... 48

Table 2-13. Main type of measures used by Member State to comply with the RES-E and flexibility targets of their NECP. Source: Author adapted from NECPs. [45][46][47][4][48]. ... 50

Table 3-1. Main drivers of the flexibility needed by timescale. ... 59

Table 3-2. Demand and RES generation used by the EC to assess the flexibility of the MS by 2030. ... 61

Table 3-3. Baseload capacity used in the assessment of the flexibility needs of the MS by 2030. ... 62

Table 3-4. Flexibility portfolio potential defined by the EC flexibility report. Source: adapted from the European Commission[129]. ... 62

Table 4-1. Interconnection capacity between Italy and neighbouring countries in 2018 and 2030. Source: Data adapted from TERNA [19][20], ENTSO-E [21], Italian NECP [4]. ... 68

Table 4-2. Transmission capacity between Italian zones in 2018 and 2030. Source: Data adapted from TERNA [19][20], ENTSO-E [21], and CESI [134]. ... 69

Table 4-3. Fossil fuel prices according to Primes-16 and the prices used in the simulation of the 2030 base scenario. Source: European Commission [137]. ... 70

Table 4-4. Electricity imports-exports by zone for the 2030 base scenario. ... 70

Table 4-5. Equivalent parameter of a BESS in Promed Grid. ... 74

Table 4-6. Key performance indicators ... 76

Table 4-7. Qualitative Description of Electricity Storage Applications and Technology Suitability. ... 80

Table 5-1. Input parameters modified for each alternative scenario. ... 83

Table 5-2. Allocation of BESS capacity in Scenario-01. ... 85

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Table 5-4. Allocation of new PHS capacity in Scenario-03. ... 86

Table 5-7. Comparison of RES annual generation between Scenario-00 and Scenario-06. 88 Table 5-8. Segmentation of existing Demand Response programmes. Source: Verrier[155]. ... 92

Table 5-9. Distribution of total DR potential. ... 93

Table 5-10. Activation cost for Scenario-07, Scenario-08, and Scenario-09 ... 94

Table 5-11. Theoretical parameters of the DR in Scenario-10. ... 94

Table 5-12. Sectoral shares in average potential load increase and load reduction refer to maximum capacity. Source: Gils[31]. ... 97

Table 5-13. Allocation of real pricing DR in the Scenario-11. ... 97

Table 5-14. Parameters for the simulation in Promed Grid of EVs batteries for V2G. .... 100

Table 5-15. Allocation of new PHS and BESS capacity in Scenario-14. ... 102

Table 6-1. BESS and PHS operation in Scenarios 01, 02 ,03, 04, and 05... 108

Table 6-2. BESS and PHS operation in SC-01 to SC-05. ... 109

Table 6-3. Utilization factor of BESS and PHS in Scenario-05. ... 111

Table 6-4. Results of the simulations of peak shedding DR. ... 119

Table 6-5. Benefit/cost analysis by perspective. ... 124

Table 6-6. Main KPIs for the Base Scenario and the Scenario-14 (all flexibility sources) ... 126

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Nomenclature

AMS: Ancillary services market

BESS: Battery energy storage system

DAM: Day-ahead market

DR: Demand response

DSM: Demand Side Management

DSO: Distribution system operator

EC: European Commission

EU: European Union

GHG: Greenhouse Gas

ISO: Independent system operator

ITcn: Italy Central north

ITcs: Italy Central south

ITn: Italy North

ITs: Italy South

ITsar: Italy Sardinia

ITsic: Italy Sicily

LCOE: Levelized cost of electricity

LCOS: Levelized cost of storage

MS: Member State

NECP: National Energy and Climate Plan

PHS: Pumping hydro system

RES: Renewable Energy Source

RES-E: Renewable Energy Sources for Electricity

RL: Residual Load

SWF: Social welfare

TSO: Transmission system operator

V2G: Vehicle to Grid

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Executive Summary

I. Introduction

In 2014 the European Union (EU) introduced new targets for 2030 in terms of share of RES at European level to continue with the 20-20-20 plan. This increase RES is strongly linked to the greenhouse gas reduction target of 40%. The specific target for RES in the electricity sector (RES-E) is to reach at least 45% share by 2030 [1]_.

Under the new framework, Member States (MS) have greater flexibility and autonomy to set and meet their targets in the most cost-effective manner in accordance with their energy mixes and available renewable energy sources, this information must be included in the National Energy and Climate Plan (NECP) of each MS.

The European Commission (EC) reached in June 2018 an ambitious political agreement on increasing RES use within the EU, introducing thus the “Clean Energy for All Europeans” package as part of the 2050 energy Roadmap and Energy Union initiative. The new RES target for 2030 was set at 32%, with an additional revision in 2023[2]_{, as}

result of the outcomes of the Paris Agreement and the downward trend of the cost of RES.

The rapid deployment of variable RES (VRES) has brought several concerns regarding the flexibility and resilience of the power grid. In this matter, a more ambitious penetration of RES-E should be sustained by infrastructure, as well as market changes and improvements.

Under the degree of freedom granted to MSs, some questions arise. Are the targets set by MSs ambitious enough to reach the European goals? Have some countries committed to attaining too demanding, and almost unreachable, targets? How do the NECPs vary from country to country in terms of goals, policies and measures to overcome the issues implicit in the high adoption of VRES in the national power system?

This thesis aims to answer the previous questions in the specific case of Italy, especially for the share of RES-E. In the first part, it establishes whether the Italian RES-E target is more challenging than the one of other comparable MSs. Equally important is to assess the future operational flexibility needs of the Italian power system and

how they can be covered in an optimal techno-economic manner.

The thesis starts by identifying the European energy roadmap. Then, it describes the NECPs of the five biggest electricity markets in Europe, carrying out a comparative analysis to identify how Italy is with respect to comparable MS in terms of targets and policies. Then, since operational flexibility is a concern that is gaining attention due to high penetration of VRES, there is a section dedicated to understanding the assessment of a system’s flexibility and identifying the different flexibility sources in Europe and Italy. Finally, using Promed Grid, a CESI in-house developed software, the Italian 2030 Base Scenario (BS) was simulated along with alternative scenarios to identify the implication of each flexibility source in the power system. Thus, the best practices to cover future flexibility needs were identified and analysed from a techno-economic perspective in a combined scenario.

II. Energy and Climate Targets

The EC differentiated the target for RES-E. The goal at European level is to attain at least 45% in 2030 and 97% by 2050 [1]_{. Contrary to the previous}

energy packages, there are no binding targets. Figure-I presents the RES-E targets of the five biggest power markets in Europe: France, Germany, Italy, Spain and the United Kingdom. These countries accounted for 67% of the overall installed capacity in Europe by 2017[3]_.

Figure-I. RES-E Targets.

Each MS had a different starting point; for instance, in 2015, France and the UK had the lowest share of RES-E, partly due to their dependency on nuclear and thermal generations units. Still, the UK

40% 54% 55% 74% 80% 15% 35% 55% 75% 2015 2020 2025 2030 RES-E Trend France Germany Italy Spain UK

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Spain has the second highest 2030 RES-E target, envisioning a rebirth of its RES-E market after the economic crisis in 2008. In the case of Italy and Germany, the share of RES-E has increased steadily, and their targets are similar, 55% and 54% respectively. To achieve the RES-E targets, the policies and measures described by each MS will play a decisive role in affecting the way in which the targets are achieved. These actions are to some extent similar in all five NECPs. All things considered, the Italian RES-E policies and RES-E target (55%) seem to be in line with the trends and perspective of other MSs and do not represent an extremely demanding commitment. However, Italy did not specify how the targeted RES installed capacity will be reached, as Germany and France did, indicating the year and the capacity to be allocated through specific tenders. Italy’s targets in terms of installed capacity and energy by technology are presented in Table-I. Other non-RES technologies installed capacity were assumed to remain at 2017 values with exception of coal-fired power plants, which are expected to completely phase-out by 2025.

Table-I. Italian targets included in the NECP[4]_.

Capacity [MW] Energy [TWh] 2017 2030 CAR G 2017 2030 CAR G Hydro 22 19 -1% 46 49 1% Wind 10 18 5% 17 40 7% Solar 20 51 8% 22 75 10% Bioenerg y 4 4 -1% 19 16 -2% Others 1 1 1% 6 7 1% RES-E Share 49 % 67 % 2% 34 % 55 % 4%

CARG: Compound annual growth rate

III. Operational Flexibility

Operational flexibility is the capability to maintain the system reliability, while coping with uncertainty and variability in generation and demand, at an acceptable additional cost [5]_{. In the}

case of RES, these imbalances come from the dependency of the produced energy on weather conditions, while the traditional imbalances are due to technical constraints and variations in demand, which traditionally was dealt with by the use of operating reserve [6]_.

In 2030, some MS may face insufficient capacity under current market regulation as a result of the absence of investment in new programmable capacity and the dismantle of some plants under the broad deployment of variable RES across Europe

[7]_{. Failure to increment flexibility will lead to a}

significant curtailment of RES and increased the system’s operating costs, as well as diminish of system reliability and security of supply. All this

expansion, cross-border market coupling, and local flexibility solutions such as storage systems or demand response.

Flexibility needs are divided into different time resolutions (annual, weekly, and daily) to consider the effects of seasonality, maintenance, demand patterns, weather forecast error, and technical contingencies[8]_{. This timescale segmentation aims}

to capture and isolate the influence of different phenomena affecting each timescale[9]_(see

Table-II).

Table-II. Influence of different phenomena on flexibility needs.

Phenomenon Year Week Day

Solar power x x

Dynamics of the demand* x

Wind power x x

Weekday/weekend

demand pattern x

Electrification of heat x

There are several approaches to assess the flexibility needs of a particular power system. Methodologies range from simple ones as the Flexibility Chart [10]_{, which evaluates the diversity}

of flexibility sources and illustrates them in a polygonal radar chart to the Annual Flexible Power and Energy Requirement (AFPER) [11]_{, which}

analyses the flexibility needs for each hour considering the Residua Load (RL). Other approaches considered in this thesis are the Flexibility Assessment Tool (FAST2), the Insufficient Ramping Resource Expectation (IRRE) [12]_{, and the Fast Fourier Transform method,}

which is a sophisticated method that identifies the frequencies that create most flexibility needs and thus determine the actual value to cover, expressed in TWh/year.

Once the flexibility needs are determined, the flexibility sources can be identified and evaluated. There are several flexibility sources located in both the demand and the supply side[13]_{(see Figure-II).}

This thesis focuses on energy storage systems (BESS and PHS), Demand Response, and EVs (V2G).

Figure-II. Flexibility sources classification[13].

Conventional power units Energy S torage S ystems Demand S ide Management

Wind/solar power plants Interconnections Available

operational flexibility

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In 2017 the EC published a document, called “Mainstreaming RES Flexibility portfolios” as a reference framework for MS to evaluate the flexibility requirements of their electric power system considering the commitments set in their NECPs. The framework is based on the RL upward needs [14]_{, which is similar to the AFPER method.}

Figure-III shows the RL that should be covered by traditional plants, storage systems, or imports. The focus is on the positive RL values as supply must meet demand at any time.

Figure-III. Residual load. In blue, the positive flexibility needs. In yellow, storage needs.

In the case of Italy, the EC found out that daily flexibility needs in 2030 will increase 4 TWh/year from 21 TWh/year in 2020 [14]_{. While weekly and}

annual needs will remain almost the same, 15 TWh/year and 11 TWh/year respectively[14]_.

Moreover, the report identified CCGT and existing PHS as the primary flexibility sources in all the three timescales. Nevertheless, it also mentioned the potential of DR (2.1 GW), interconnections (2.2 GW), and low-cost PHS (4.4 GW) [14]_{. The report}

had some differences with respect to the Italian NECP estimations, such as lower wind and solar power generation, as well as the fact that the EC report did not completely consider the phasing out of coal power plants.

IV. Italian 2030 Base Scenario

With the aim of evaluating the flexibility of the Italian power system in line with the goals of the Italian government, the 2030 base scenario (BS) was built, using six bidding zones (see Figure-IV), under the following considerations:

• Demand by 2030 is the one provided in the Italian NECP, 337 TWh [15]_.

• Future installed capacity: increase of RES capacity and coal phasing out (8700 MW [16]_).

• Wind, solar, hydro, geothermal and biomass power generation as indicated in the Italian NECP. The hourly profiles were based on the TYNDP 2018 database provided by ENTSO-E.

• The national grid and the interconnection capacity were defined in accordance with TERNA and ENTSO-E reports [17][18][19][20][21]_.

• Net import-export balance at 32 TWh, as described in SEN-2017[22]_.

• Fossil fuels and GHG emissions prices as forecasted in the EU reference scenario PRIMES-16[23]_.

• Storage systems: installed capacity in 2018 (7.6 GW of PHS [24]_{). Minor BESS pilot}

projects were neglected.

Figure-IV. Transmission and generation capacity used in the 2030 Base Scenario. Values in brackets correspond to the

capacity in 2018. All values are expressed in GW.

V. Alternative Scenarios

Alternative scenarios were defined to understand how flexibility sources performed in the Italian power system with high penetration of RES. Thus, local sensitivity analyses were used to find out how the modification of one parameter in the model alters the output, thus isolating the influence of each flexibility source. Table-III presents the variation of the characteristics of the flexibility source in each scenario. While Table IV shows the allocation of BESS and PHS in the first five alternative scenarios.

In the first five alternative scenarios, the installation of new storage capacity was assumed to -4 -3 -2 -1 0 1 2 3 1 3 5 7 9 11 13 15 17 19 21 23 GW h

Hour of the day Residual Load 19.7 9.7 14.7 7.6 3.2 0.8 59.3 50.9 18.4 15.3 7.6 3.2 0.8 49.9 Solar Wind Hydro PHS Bio O. RES Thermal 2017 2030 2.5 (1.3) 5.0 (4.0) 3.8 (2.7) 2.4 (1.3) 5.7 (4.6) 5.7 (4.6) 1.6 (0.7) 1.9 (0.7) 0.8 (0.1) 1.0 (0.3) 3.7 (1.8) 6.0 (4.2) 2.2 (1.0) 4.4 (3.2) 0.2 (0.2) 0.2 (0.2) 0.9 (0.9) 0.7 (0.7) 1.5 (1.2) 1.5 (1.1) 0.6 (0) 0.6 (0) 0.4 (0.3) 0.4 (0.3) 0.5(0.5) 0.5(0.5) 1.2 (0) 1.2 (0) 0.4 (0.3) 0.4 (0.3) ITn ITcn ITcs ITs ITsic ITsar GR MT SL AT CH TN FCO ME FR

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The economic analysis was done utilizing the Levelized Cost of Storage (LCOS), which was, on average, 186 €/MWh in the case of PHS and 217 €/MWh for BESS[25]_{. Nevertheless, it is expected}

that the LCOS of BESS will decrease as this technology has still room for improvement, while PHS is a mature technology.

Table. III. Alternative Scenarios modifications.

SC Variation Observations

1 3 GW BESS Allocation of capacity based on energy spills. 6 GW was defined by TERNA as the needed capacity by 2030 [26]_.

2 6 GW BESS

3 3 GW PHS Allocation of capacity based on potential defined by TERNA and ESTMAP [27]_.

4 6 GW PHS

5 3 GW PHS + 3 _{GW BESS} PHS as in Scenario-03 and allocation of _{BESS based on energy spills.}

6 RES Generation (+10%)

Increase of initial wind and solar power generation hourly profiles to identify how much an increase in RES penetration will demand a much higher flexibility form the system

7 DR:(225€/MWh) Theoretical DR capacity of 5 GW

[28]_.

DR Peak Shedding Scenarios 07, 08, and 09 based on the activation cost defined by Gils and Norden. Activation cost in Scenario-10 set to allow DR to operate for more than 1460 hours during the year. Capacity allocated to the zones with high industry concentration: ITn, ITcn, and ITcs. Variations to SC-10 to perform a further sensitivity analysis. 8 DR (100 €/MWh)

9 DR (70 €/MWh)

10 DR (48 €/MWh)

11 DR: Energy _shifting Price elasticity DR model

12 EVs and V2G 1 million EVs and 20% engaged in V2G.

13 Import-Export No limit to net balance of 32 TWh.

14

4 GW PHS + 2 GW BESS | DR | EVs

BESS and PHS allocated in the same way as in Scenario-5. EVs parameters as in Scenario-12. DR same criterion as in Scenario-10. 15 4 GW PHS + 2 GW BESS | DR | EVs | 2018 Grid

Same as Scenario-14 with transmission capacity set at 2018 values to understand the influence of the delay of any transmission project.

Table. IV. Allocation of storage capacity.

Scenario SC-01 SC-02 SC-03 SC-04 SC-05 ITcn BESS 217 460 - - 230 PHS - - 443 885 443 ITcs BESS 489 970 - - 485 PHS - - 1180 2361 1180 ITn BESS 0 0 - - 0 PHS - - 0 0 0 ITs BESS 969 1918 - - 958 PHS - - 590 1179 590 ITsar BESS 525 525 - - 265 PHS - - 492 590 295 ITsic BESS 799 2127 - - 1063 PHS - - 295 985 492 Italy BESS 3000 6000 - - 3000 PHS - - 3000 6000 3000

Real pricing DR was modelled using a simplified ideal price elasticity approach, owing to the complexity of available models in the literature and the considerable amount of data and computational resources needed. Figure-V depicts the demand

percentage variation.

The effects of seasonality were considered in the simulation of DR as well as load increase and load reduction by sector (see Table V). Moreover, since priced-based DR is concentrated in the residential sector, the theoretical capacity was distributed among all the bidding zones according to the demand defined in the BS.

Figure-V. Average price elasticity demand in the BS.

Finally, this thesis studied two aspects related to the potential increase of EVs: the modification of the demand load profile and the use of the storage capacity of EVs (V2G). By 2030 ENEL estimated 2 million or more EVs in Italy[29]_{. However, in this}

thesis, it was assumed 1 million EVs as a more conservative forecast. The distribution of EVs was based on the existing charging infrastructure, being ITn and ITcn the zones with the highest concentration[30]_.

Table-V. Weighted capacity to modify load by sector, Gils[31]_.

Residential Tertiary Industry

Increase 40% 35% 25%

Reduction 88% 10% 2%

VI. Performance Indicators

Technical and economic indicators were used in order to assess the effect of the different flexibility sources on the performance of the power system. Table-VI shows the 10 most relevant out of the 23 performance indicators used in this thesis.

Table. VI. Alternative Scenarios modifications.

Type Indicator Metric

Economic

Benefit/Cost Ratio

∆ SEW [M€/y]

Action cost [M€/y]

Average price [€/MWh]

Operational RES curtailments [TWh/y]

Thermal dependency [%]

Flexibility

Daily flexibility needs [TWh/y] Weekly flexibility needs [TWh/y] Annual flexibility needs [TWh/y]

Environmental GHG Emissions [tons]

-2 -1 0 1 3 5 7 9 11 13 15 17 19 21 23 P ri ce e la st ic it y o f d eman d

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VII. Findings

A. Storage Systems

From the results of the first five scenarios, it was possible to identify that BESS operated more hours than PHS and reduce more the daily flexibility needs (see Figure-VI). There is a complementarity between BESS and PHS; the former addresses daily flexibility needs while the latter does the same in the weekly timescale. The capacity distribution that seemed optimal from a technical and economic perspective was the installation of 2 GW of BESS and 4 GW of PHS (see Figure-VII). This combination decreased energy spills by 8.6 TWh compared with the BS (10.1 TWh) and reduced thermal generation in 8.3 TWh, which represented a reduction of €114 million in fuel cost, and more than 2 Gg of CO2 emissions.

Figure-VI. Capacity factor duration curve of BESS and PHS.

BESS performed on average 335 cycles per year, while PHS worked 170 cycles. The difference is due to the fact that BESS energy storage is smaller and therefore it is used almost once a day, harnessing solar power generation. Moreover, BESS can be located where needed, which gives them an absolute advantage over PHS. For instance, storage systems will be essential to cover flexibility needs and harness the potential of RES in the south of Italy. The behaviour throughout the year is similar for both technologies, but BESS showed always higher capacity factors (see Figure-VIII). Energy Spills [TWh] 10 10 B E SS [ G W ] 6 3.43 9 5 3.98 2.65 7 4 4.53 2.63 1.74 6 3 5.08 4.18 3.29 2.39 5 2 6.76 5.86 4.97 2.89 0.94 3 1 8.44 7.55 5.47 3.38 2.12 0.85 2 0 10.12 8.04 5.96 3.88 3.30 2.71 2.13 1 1 0 1 2 3 4 5 6 PHS [GW]

Figure-VII. Energy spills for different BESS and PHS combinations.

Currently, in Italy, storage systems are allowed to be part only of the day-ahead market (DAM), where prices are low compared with the LCOS of both technologies. For instance, doing energy arbitrage, the average selling price was 43€/MWh, which was lower than the LCOS, resulting in a nett loss position of nearly 150€/MWh. To overcome this barrier, storage systems should be allowed to participate in energy and capacity markets that show high prices (see Figure-IX).

Figure-VIII. BESS and PHS capacity factors.

Figure-IX. Prices in different markets where BESS might be used.

B. Demand Response

Although in Italy there are 4 GW available through interruptible load contracts[28]_{, the capacity}

has seldom been used, and in some cases never, during the recent years. The theoretical potential of DR (5GW[28]_{), is equivalent to have at least 7}

million of Italian households with an engagement level of 25%, who can be represented by an aggregator or a DSO in the DAM. Nevertheless, Peak shedding DR was barely used in scenarios 07, 08, and 09 due to the high activation cost. Conversely, in Scenario-10 (activation cost at 48€/MWh) the DR capacity in the north was used approximately 600 hours, mostly during the winter (see Figure-X). On the other hand, in other regions, DR was activated just two hours throughout the year as a result of low prices in the DAM in those zones. -100% -50% 0% 50% 100% 0 2000 4000 6000 8000 C ap ac ity f ac to r Hours Discharging - Charging Series1 Series2 -100% -50% 0% 50% 100% Ja n F eb M ar Ap r Ma y Ju n Ju l Au g S ep Oc t No v De c Ca p ac it y F ac to r

BESS and PHS annual average operation profile 217 186 43 381 300 LCOS BESS LCOS PHS Energy Arbitrage ASM up movements Interruptible load Energy [€/MWh] 400 1300 Interruptible load Primary control reserve Capacity [€/MW]

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to be complementary with the use of storage systems, which allows the system operator to cover flexibility needs in summer and winter (see Figure-VIII and Figure-X). Simulations showed that through the use of DR, annual flexibility needs were reduced by 5.2%. Finally, the reduction of the peak load during some hours kept storage systems without operating, using than their stored energy in off-peak hours. Besides optimizing the use of storage systems and harnessing RES generation, the acceptance of DR bids in the DAM reduces the electricity prices for all the demand, usually displacing thermal units (See Figure-XI). For instance, a reduction of 1.6 €/MWh in the annual average price was obtained by using an activation cost of 48€/MWh and a capacity of 5 GW in peak shedding DR programmes.

Figure-X. DR activations during the year.

DR response fixed and variable costs are particularly low compared with power generation units and storage systems, which makes it extremely appealing as a flexibility source. Fixed cost, or investment cost, is approximately 0.12€/MW-year for industrial applications and 0.22€/MW-year in the case of residential users[32]_.

While the variable cost is the activation cost that must be paid to the DR provider.

Figure-XI. (a) Bids in the day-ahead market. (b) effect of DR bid in the day-ahead market.

Energy shifting DR is a scheme still under study[31] [33]_{. Therefore, the model used was a}

simplification of the actual effects that price-based programs might have over the power system. Hence, from the simulation, it was possible to identify that the shift of energy from evening to morning or afternoon helped to reduce energy spills (0.8TWh), increase the use of BESS (1.9TWh) and

(0.3€/MWh). However, the use of the thermal fleet remained almost unchanged as electricity demand is advanced or delayed and then final consumption remains almost invariable.

C. Electric Vehicles

Regarding the interaction between EVs and the power system, 1million of EVs will represent an increase of 3.8 TWh (1% of the projected demand by 2030), modifying the load profile due to EVs charging patterns. As an illustration, during one peak hour, there was an increase in the demand of almost 180 MWh, which is roughly equal to the demand of 100.000 households in Italy. Using a 20% engagement level, EVs batteries represented 1.3 GW of available storage capacity, which generated 2 TWh during peak hours, performing between 400 and 500 cycles per year.

Considering the cost difference between charging during the evening and doing it during the afternoon, the potential savings in electricity cost was close to €130/year for a single EV owner. The energy stored during the day could also be used by the EVs owners at their houses to reduce their electricity bill, which indirectly benefits the TSO by reducing the peak load.

Scenario-12 showed that the aggregation of EVs batteries (1.3 GW) could replace part of the 6 GW of BESS and PHS to be installed by 2030, which might lead to substantial investments savings between €250 million and €350 million per year.

D. Combination of flexibility sources

The Scenario-14 combined DR, storage systems and EVs (V2G) to cover part of the flexibility needs for the three timescales. There is a remarkable complementarity among these flexibility sources as flexibility needs decreased in the three timescales (see Figure-XII). In this scenario, thermal power generation was reduced by 8.3TWh, however, the ramp rates were higher due to the entry into operation of storage systems. Energy spills were also reduced significantly, helping to reduce the average national electricity price by 0.77€/MWh compared with the BS price.

The extreme case of having today’s internal transmission capacity (Scenario-15) increased the congestion hours by 40%, intensifying the use of the thermal generation fleet in the north of Italy (4 TWh). Despite this technical events, the annual average national price varied just by 0.04€/MWh in relation to Scenario-14, owing to the reduction of 3 10 20 30 40 50 60 1 000 2 000 3 000 g en _feb m a r ap r m a g g iu _lug ag o se t o tt n o v d ic MW MW Peak shedding DR ITn Itcn (2nd axis) Itcs (2nd axis) 0 20 40 60 80 100 120 140 0 20 40 60 €/ M W h Supply [MW] Day-ahead bids 0 20 40 60 80 100 120 140 0 20 40 60 €/ M W h Supply [MW] Day-ahead bids DR

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TWh from thermal generation among the other zones, as well as an increment in the use of PHS (0.4 TWh), BESS (1.7 TWh), and DR (0.5 TWh). Thus, the combination of the different flexibility sources compensated the constrains for internal electricity flow. ITn was the most affected zone since imports from ITcn decreased by 5.4TWh

Figure-XII. Comparison of flexibility needs between the Base Scenario and the Scenario-14.

E. Economic analysis

The economic analysis included direct and indirect benefits resulting from the implementation of each flexibility source. Therefore, a multi-stakeholder approach was used to consider various possible benefits that can be obtained by implementing these measures in the Italian market. The benefits were classified into three tiers: the power system, externalities, and macroeconomic effects. Table VII presents the summary of the benefit-cost analysis on an annual basis.

BESS and PHS costs were computed as the LCOS multiplied by the energy produced during the year. The cost of EVs engaged in V2G schemes was assumed to be the increase of the provision needed to replace the battery in less time due to the degradation caused by the operation in V2G. Finally, DR and the TSO/ISO perspective considered the estimated investment cost in infrastructure and operations.

Table-VII. Benefit/cost analysis by perspective.

Perspective Costs _[M€] Benefits* _[M€] Ratio (Bnf/cost) BESS owner 553 122 0.2 PHS owner 652 420 0.6 DR provider (peak shedding) 1 43 >>1 DR provider (energy shifting) 1 24 >>1 EVs owners 46 24 0.5 Demand, Society, ISO, DSOs and TSOs

12 1170 NA

Holistic 1264 1780 1.4

*The detailed benefit analysis is included in Appendix B.

From this analysis it was possible to identify the following aspects:

• BESS remains not economically viable from the owner perspective.

• The degradation of the batteries strongly affects the feasibility of V2G from the owner perspective.

• DR is the action with a better benefit/cost ratio. Nevertheless, to exploit its potential the focus should be on regulation changes and the design of attractive and novel business models.

Since Scenario-14 is the most likely to be close to reality (mix of different flexibility sources), it was contrasted with the Base Scenario (see Table-VIII). RES curtailments were reduced by means of storage systems and DR. This made possible to reduce the system’s flexibility needs, in particular, the daily ones, and diminish the use of thermal units by the use of DR during peak hours, among other reasons. All in all, more than 2 Mton of CO2 were

reduced. Finally, from an economic perspective, and a holistic point of view, the implementation of a proper flexibility portfolio generates several benefits that affect several stakeholders directly and indirectly.

Table-VIII. Main KPIs for BS and SC-14

KPI Bnf-cost RES curtailment s Daily flexibilit y needs CO2 emissions [ratio ] [TWh/y] [TWh/y] [Mton/year ] BS NA 10.1 32.6 46.4 SC-14 1.4* 1.5 30.6 44.1

VIII. Conclusions and future work

This thesis has reviewed the techno-economic implications of utilizing several flexibility sources in the Italian power system, which will be essential to cope with the expected high RES-E penetration to be reached by 2030, conforming to the Italian NECP. The design of a proper flexibility portfolio includes the capacity (storage and DR), the location among the Italian bidding zones, the markets in which they can participate, and regulation changes, among other aspects. Defining the optimal flexibility portfolio could increase the benefits not just for storage systems owners and demand response providers but for the TSO, DSOs, end-users and society. In fact, the overall benefits cost ratio (1.4) could be higher if other benefits are quantified and monetized, making flexibility sources not just technical and environmentally feasible but economically too

-6.2% -4.7% -3.8% 5 15 25 35

Daily Weekly Annual

[T W h /y ea r] Flexibility Needs BS SC-14 Variation

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source that can be employed in Italy, firstly by using the potential for new PHS in southern zones and then by the allocation of BESS capacity where it is more needed. Their use is vital to exploit RES energy and reduce electricity prices. However, their LCOS are still high, which hampers their installation. To overcome this economic barrier, technical and operational benefits of other stakeholders must be considered and translated into revenue streams for BESS and PHS owners in order to boost their business models.

DR has enormous potential due to its low investment cost and minimal technical requirement, as technology breakthroughs accelerate its implementation. In this regard, the role of DR aggregators will be essential to open up the DR market and exploit its potential. Aggregators have an appealing business opportunity as the activation cost is usually lower than price in the DAM, and they can obtain part of their revenues from such differences. In that case, regulation and incentives schemes are the main drivers to use the potential of DR while benefiting DR providers, system operators, and all consumers. Nevertheless, it should be deemed that the use of thermal units might be moved from peak to off-peak hours, reducing the benefits, and even creating new load peaks.

Smart EV charging behaviour is critical to prevent the increase in the use of peaking power plants. The use of V2G schemes is another manner to reduce the impact of EVs while generating benefits for both EV owners and the power system, representing saving for EV owners and an additional flexibility source for the system operator. Moreover, if the regulation allows EVs to participate in the ancillary services markets, the benefits for EVs owners would be much higher. To do so, the role of aggregators and the definition of the rules are vital. The use of EVs batteries by the system operator could reduce RES curtailments and daily flexibility needs, in the same way, that BESSs do. Then, from the power system perspective, the use of EVs as energy storage on the grid has an advantage over BESS, as the investment cost is born by the EV owners, and the system operator only pays for the service.

A proper flexibility portfolio is able to provide the system with enough flexibility to prevent price spikes, reduce congestion hours and defer transmission and distribution investments. Additionally, such a portfolio could help in reducing GHG emissions, harnessing RES power

of Italy.

Even though the technical aspects of storage systems are promising, their economic feasibility might hold back their development. Conversely, DR is able to produce benefits not just for DR providers but for the whole demand, making it the most cost-effective alternative. Benefits from the analysed flexibility sources are not only for the storage system owner or the DR provider, TSOs and consumers also benefit from these measures. Therefore, the actors that indirectly benefit from the use of flexibility sources should push and back their integration into the power system.

Bearing in mind that the holistic benefit-cost ratio is higher than one, a redistribution of the benefits (e.g.: reduction of GHG emissions) is needed to improve the economic perspective of those that are bearing the highest costs (e.g. storage owners).

The governance framework for flexibility providers will be the main enabler for the Italian power system to exploit the potential of storage systems and demand participation. For instance, allowing BESS, PHS and DR to participate in the balancing and the frequency response markets could incentivize investors and end-users. Direct incentives are another way to encourage investments in storage systems, considering that several stakeholders benefit from their installation.

A limitation for the analyses carried out in this thesis was the lack of some functionalities in the simulation software (e.g. reliability analysis, BESS simulation model). These limitations restricted the evaluation of further technical and economic benefits associated with each flexibility source. Lastly, even though there was not a thorough and complex analysis of each one of the flexibility sources, this thesis covered several aspects which give an overview of the importance of the flexibility for the Italian power system and the sources to cover such needs.

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1 Introduction

The European Commission has been working towards a nearly zero carbon economy within the bloc. Following the Kyoto protocol adoption in 1997, the European Commission introduced the first Energy and Climate package, which later was back by the Directive 2009/28/EC, also known as the 20-20-20 plan. To continue with the European directives

the next milestones are the targets for the period 2021-2030. For this period, the

Commission published the “Clean Energy for All Europeans” package, as part of the

2050 Energy roadmap, which set energy targets at European level and grant, therefore,

certain degree of freedom to the Member States to define their own targets, as long as the attainment of the European targets is guaranteed.

To comply with the European guidelines, Italy developed the draft of the National Energy and Climate Plan (NECP) in 2018. In the NECP, the Italian government set specific goals

in terms of Renewable Energy Sources for Electricity (RES-E) penetration, reduction

of Greenhouse Gas emissions (GHG), and energy efficiency. With regards to RES-E, the

Italian government committed itself to reach 55.4% RES-E share of the expected demand (337TWh) by 2030[4], since the new European directive did not include binding targets. Other Member States committed themselves to different RES-E penetration levels, among them, the five biggest power markets in Europe pushed for very demanding targets, as it is the case of Spain, the UK, France and Germany. In terms of RES-E penetration trajectory and policies to back such RES-E adoption, Italy is in line with these four Member States.

Although the operational flexibility of a power system is not a new term, it has gained exceptional attention as flexibility concerns rise with the increase of RES-E share. The need for operational flexibility is aggravated since RES-E are not dispatchable and are strongly exposed to weather changes, mainly solar and wind power plants. Operational flexibility is defined as the capability to maintain the system reliability, while coping with

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uncertainty and variability in generation and demand, at an acceptable additional cost [5]_.

The variability of such RES-E generation represents a challenge for the power system form both technical and economic perspectives.

Several flexibility sources have been identified in both demand and supply sides. Such sources are commonly classified into five groups: Demand Side Management (DSM), Conventional power units, Energy Storage Systems (ESS), Interconnections, and Solar and Wind curtailments[34]. This thesis focuses on Energy Storage Systems (Batteries and Pumping Hydro), Demand Response and interconnections as flexibility sources in the Italian power system.

The current energy storage capacity in Italy is about 7.6 GW of PHS, which are concentrated in the north. Kougias et al.[35] argue that the underused of this storage capacity in recent years could obey to owners’ strategies in the power market and the low level of RES-E overgeneration in the market zone. Furthermore, TERNA is nowadays carrying out minor BESS pilot projects, which still do not represent a considerable flexibility source for the power system, and therefore were not included in the analysis in this thesis. In terms of Demand Response, TSOs and DSOs have interruptible load contracts accounting for 4 GW [28], which are mainly oriented to handle severe contingencies. Nevertheless, such capacity has been rarely used since the program started[28]_{. Italy is a net electricity} importer, the majority of imports come from the interconnections in the north with France,

Switzerland, Austria, and Slovenia. Still, Italy’s special geographical location hinders the increase of its interconnection level to the European expected level. Therefore, the increase of the interconnection capacity with those countries is of particular interest towards the full market coupling, also known as the Energy Union, as proposed by the European Commission.

Several analyses have been carried out to understand how the European Union will cope with the flexibility issue [36] [37] [38]_{. In the 2030 scenario, some Member States’ power}

markets may have insufficient power generation capacity under current market regulation as a result of the absence of investment in new programmable capacity and plant

dismantle under the wide deployment of variable RES across Europe [7]. The European Commission stressed the importance of enhancing the flexibility of the Member States’ power systems since the introduction of the “2030 Clean energy for all Europeans” package

[39]_{. Failure to increment flexibility for the crucial 2021–2030 period could lead to a} significant curtailment of RES and increased system’s operating costs[40]. Moreover, multiple European interconnection lines could operate under high levels of congestion by 2030 regardless of the RES-E penetration level [40]. All this implies the need for

interconnection capacity expansion, cross-border market coupling, and local flexibility solutions such as storage systems or demand response.

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Some studies have covered the flexibility needs at European level, providing a brief insight into the flexibility needs and current situation at country level [36][37][38]_{. One of the}

most notable reports for Italy could be the assessment of flexibility needs framework provided by the European Commission, which identified CCGT and PHS as the main flexibility sources for Italy by 2030. The framework was intended to help the Member States to assess flexibility needs and design optimal flexibility portfolios adjusted to the NECPs targets. Nevertheless, there are no studies dedicated to the Italian situation considering the local specificities. For this reason, tailored analyses are required to better understand

how Italy could cope with the variable RES-E increase while keeping the reliability of the power system at minimum cost. Since there is not a “one-fits-all” solution,

understanding how the Italian power system responds to each flexibility source and the local resources is vital in the flexibility portfolio designing process to guarantee the adequacy of the power market and the power system.

Under the degree of freedom granted to the Member States by the European Commission, some questions arise. How does the Italian NECP vary while compared with other Member States’ NECPs in terms of goals, policies and measures? In Italy, which type of flexibility sources should be firstly exploited? Where should the energy storage systems be installed? How to maximise the use of RES-E generation using storage systems? How to exploit the potential of Demand Response in Italy? Should storage systems and Demand Response be allowed to participate in different markets such as the Ancillary Services Market and the Balancing Market to improve their economic feasibility?

Answering these questions appropriately will be useful for multiple stakeholders like policy makers, investors, the Independent System Operator (ISO), the Transmission System Operator (TSO), and regulatory bodies, among others. Therefore, this thesis aimed to enlarge the knowledge about the flexibility of the Italian system under a high RES-E penetration by 2030 from a techno-economic perspective.

The thesis starts by describing in Chapter 2 the 2050 European energy roadmap, with an emphasis on the 2030 targets set in the “Clean Energy for all Europeans” package. Hence, a thorough description of the NECPs of the five biggest electricity markets in Europe is presented, with a particular focus on the RES-E targets. Then, a comparative analysis is carried out with the aim to identify how Italy is with respect to comparable Member States in terms of RES-E targets and the policies defined to reach such targets. Since operational flexibility is a concern that is gaining attention due to high penetration of variable RES-E, Chapter 3 is dedicated to understanding the assessment of a system’s flexibility and

identifying the different flexibility sources in Europe and Italy. Finally, using Promed

Grid, a CESI in-house developed software, the Italian 2030 base scenario was characterized and simulated (Chapter 4). In addition, Chapter 5, describes the alternative scenarios used to identify the implication of each flexibility source in the Italian power system. Thus,

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the best practices to cover future flexibility needs were identified and analysed from a techno-economic perspective in a combined scenario. Then, all results from the simulation of the 15 alternative scenarios are discussed in Chapter 6.

The information about the installed power capacity, transmission capacity, storage systems, electricity demand and RES-E generation by technology was gathered from

publicly available sources. The main references were the Italian NECP [4] and TERNA reports[17][18][19]. The simulations of the 2030 Base Scenario and all the alternative scenarios were conducted in Promed Grid, a software developed by CESI. To understand how each flexibility source affects the Italian power system, a local sensitivity approach was used. In other words, the flexibility sources were simulated in separate scenarios, and any modification in terms of location and capacity was considered in a different scenario. After performing independent simulations, the adequate BESS, PHS and Demand Response capacities were identified, which were used, then, to simulate a combined scenario. Finally, other scenarios covered additional aspects such as transmission capacity, electric vehicles, and delay in transmission reinforcement. In order to complete the analysis from an economic perspective, a benefit-cost analysis was completed form an owner as well as from a holistic

Analysis of the operational flexibility of the Italian power system in 2030