Motivation Detailed VOLL data Theory Illustration Conclusions References How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity Marten Ovaere, Evelyn Heylen, Stef Proost, Geert Deconinck, Dirk Van Hertem KU Leuven, Department of Economics September 6, 2017 Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Motivation ◮ Value of lost load (VOLL) = cost of unserved energy. ◮ On average: VOLL = 10 e /kWh ← → price = 0.2 e /kWh But VOLL differs widely among consumers and over time ◮ Various empirical studies have estimated VOLL for different countries and for different interruption characteristics. ◮ This paper analyses the efficiency gains of using more detailed VOLL data in ex-ante decision making. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Interruption costs Table: Studies that estimate VOLL as a function of different interruption characteristics. Consumer Advance Country Time Duration Location Source type notification Australia x x (CRA International, 2008) Austria x x x (Reichl et al., 2013) Cyprus x x (Zachariadis and Poullikkas, 2012) Germany x x (Growitsch et al., 2013) Great Britain x x (London Economics, 2013) Ireland x x x (Leahy and Tol, 2011) Netherlands x x x (de Nooij et al., 2007) New Zealand x x x x (Electricity Authority, 2013) Norway x x x x (EnergiNorge, 2012) Portugal x x (Castro et al., 2016) Spain x x (Linares and Rey, 2013) Sweden x x (Carlsson and Martinsson, 2008) United States x x x x x (Sullivan et al., 2009) Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Interruption costs Table: Great Britain VOLL as a function of time characteristics and consumer groups (London Economics, 2013, Table 1 and Table 2). Expressed in [2015 e /MWh]. Not winter Winter Weekday Weekend Weekday Weekend Peak Not peak Peak Not peak Peak Not peak Peak Not peak Residential 11,093 8,081 10,753 12,946 12,757 10,571 11,952 13,730 SMEs 44,077 42,849 38,749 39,722 51,284 45,551 41,224 46,306 Estimating VOLL Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Interruption costs Table: United States VOLL as a function of time characteristics and consumer groups ((Sullivan et al., 2009, Table 3-10, Table 4-10 and Table 5-11)). Expressed in [2015 e /MWh]. Summer Weekday Weekend Morning Afternoon Evening Night Morning Afternoon Evening Night Residential 2,947 2,210 2,097 2,097 3,457 2,607 2,493 2,493 Small C&I 265,004 322,100 169,713 169,319 163,019 204,364 96,866 95,291 Large C&I 15,351 21,573 18,184 13,550 11,030 15,711 12,831 9,576 Winter Weekday Weekend Morning Afternoon Evening Night Morning Afternoon Evening Night Residential 2,097 1,473 1,190 1,190 2,437 1,757 1,417 1,417 Small C&I 365,415 458,343 214,996 211,452 216,177 279,967 117,342 113,798 Large C&I 12,557 18,448 14,019 10,503 8,667 12,948 9,468 7,109 Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Interruption costs Table: Norwegian VOLL as a function of time characteristics and consumer groups (EnergiNorge, 2012, Table A and Table B). Residential Industry Commercial Public VOLL [2015 e /MWh] 469 10,926 17,984 15,888 Winter 1 1 1 1 Spring 0.57 0.87 1 0.67 Season f y ( c , y ) Summer 0.44 0.86 1.02 0.51 Autumn 0.75 0.88 1.06 0.58 Weekday 1 1 1 1 Day f d ( c , d ) Saturday 1.07 0.13 0.45 0.3 Sunday 1.07 0.14 0.11 0.29 2 AM 0.4 0.12 0.11 0.43 Time f h ( c , h ) 8 AM 0.69 1 1 1 6 PM 1 0.14 0.29 0.31 V ( c , t ( h , d , y )) = V ( c ) f h ( c , h ) f d ( c , d ) f y ( c , y ) (1) Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level C ′ ( ρ ) [ e /MWh] ¯ V ρ ¯ 1 ρ Figure: Efficiency gains if VOLL differs over time. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level over time [ e /MWh] C ′ ( ρ ) V w ¯ V V s ρ ¯ 1 ρ ρ w ρ s Figure: Efficiency gains if VOLL differs over time. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level between consumers [ e /MWh] C ′ ( ρ ) ¯ V ρ 1 ¯ ρ Figure: Efficiency gains if VOLL differs between consumers. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level between consumers [ e /MWh] C ′ ( ρ ) V max Perfect Random ¯ V V min ρ 1 ¯ ρ p ρ Figure: Efficiency gains if VOLL differs between consumers. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level between consumers [ e /MWh] C ′ ( ρ ) V max Perfect Spatial Random V 2 ¯ V V 1 V min ρ 1 ρ s ¯ ρ p ρ Figure: Efficiency gains if VOLL differs between consumers. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Optimal reliability level between consumers [ e /MWh] C ′ ( ρ ) V max Perfect Spatial Random V 2 ¯ V V 1 V min ρ 1 ρ s ¯ ρ p ρ Figure: Efficiency gains if VOLL differs between consumers. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Data Network: 5-node version of the RBTS 1 2 3 4 5 Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Data Main assumptions ◮ Operational planning + real-time operation stage More details ◮ Conventional and wind generation ◮ Dispatch, preventive redispatch and corrective redispatch costs ◮ A year = 72 time instants (6x3x4), each with its probability of occurrence. ◮ Each consumer group has a different VOLL for each time instant. ◮ Demand shares of different consumer groups change over time. Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Evaluation of short-term reliability management Evaluation according to three criteria (1) Expected total costs � � C corr ( a rt � ETC( v ) = [ C prev ( a p ( v , t )) + c ( v , t )) π rt t ∈ T rt ∈ RT + P rt � curt ( c , v , t ) · V(c,t) ] ∀ t (2) (2) Average interruption time [min/year] (3) Inequality between consumers � G = | 1 − ( ( X k − X k − 1 ) · ( Y k + Y k − 1 ) | (3) k A = (4) A + B Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
Motivation Detailed VOLL data Theory Illustration Conclusions References Evaluation of short-term reliability management Inequality index Cumulative relative energy not supplied ( E k ) 0 0.2 0.4 0.6 0.8 1 E 5 1 0 . 8 E 4 0 . 6 A 0 . 4 E 3 B 0 . 2 E 2 E 1 0 D 1 D 2 D 3 D 4 D 5 Cumulative relative demand ( D k ) Marten Ovaere KU Leuven, Department of Economics How detailed value of lost load data impact power system reliability decisions: a trade-off between efficiency and equity
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