Price Regulation and the Incentives to Pursue Energy Efficiency by - - PowerPoint PPT Presentation

Price Regulation and the Incentives to Pursue Energy Efficiency by Minimizing Network Losses

Joisa Dutra, Flavio Menezes, Xuemei Zheng

Centre for Regulation, FGV; The University of Queensland

June 2014

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 1 / 11

Introduction

Energy efficiency is usually cost-effective. Typical concern is to incentivize consumers to pursue energy efficiency. However, demand side management (DSM) is not always effective.

rebound effect of consumers supplier’s incentive to maximize sales under price cap regulation — requires revenue decoupling from sales quantity

Should shift the focus on the supply (e.g. transmission and distribution) side energy efficiency.

• verall losses between the power plant and final consumers: 8 to 15%

(International Electrotechnical Commission, 2007).

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 2 / 11

Our Contribution

Analyse the incentives for supply side energy efficiency embedded in existing regulatory regimes. Key results:

Which regime yields the highest expected welfare depends on demand, costs and the weight assigned by the regulator to the monopolist’s profits in total surplus.The comparison driven by the size of the cost of effort is complex. Policies that encourage utilities to promote end-user energy conservation may reduce the incentives that electricity suppliers face for internal energy efficiency.

Add to the literature on DSM (Wirl,1995) and on incentive mechanism design (Eom, 2009; Chu & Sappington,2012; Chu & Sappington,2013).

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 3 / 11

Model Setup

Inverse demand function: P(Q) = a − bQ, with a > 0, b > 0 Q : the amount of electricity that could be consumed by end users, P : unit price of electricity paid by consumers.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 4 / 11

Model Setup

Inverse demand function: P(Q) = a − bQ, with a > 0, b > 0 Q : the amount of electricity that could be consumed by end users, P : unit price of electricity paid by consumers. Energy efficiency: Φ(·) = Q

QS

Qs : units purchased in the wholesale market, Q < Qs: due to network losses ⇒ 0 < Φ < 1.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 4 / 11

Model Setup

Inverse demand function: P(Q) = a − bQ, with a > 0, b > 0 Q : the amount of electricity that could be consumed by end users, P : unit price of electricity paid by consumers. Energy efficiency: Φ(·) = Q

QS

Qs : units purchased in the wholesale market, Q < Qs: due to network losses ⇒ 0 < Φ < 1. Cost function: C(Qs, E) = cQs + E C : the total cost, c > 0: fixed unit cost of electricity, E: the cost of exerting effort E, E = {0, e} with e > 0: the two possible levels of effort to improve energy efficiency.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 4 / 11

Model Setup

Inverse demand function: P(Q) = a − bQ, with a > 0, b > 0 Q : the amount of electricity that could be consumed by end users, P : unit price of electricity paid by consumers. Energy efficiency: Φ(·) = Q

QS

Qs : units purchased in the wholesale market, Q < Qs: due to network losses ⇒ 0 < Φ < 1. Cost function: C(Qs, E) = cQs + E C : the total cost, c > 0: fixed unit cost of electricity, E: the cost of exerting effort E, E = {0, e} with e > 0: the two possible levels of effort to improve energy efficiency. The monopolist’ profit: π(E, Q) = P(Q)Q −

cQ Φ(E ) − E

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 4 / 11

Model Setup

Relationship between efforts and energy efficiency Φ Φ E = 0 ν 1 − ν E = e 1 − ν ν with ν > 1

2 and 0 < Φ < Φ < 1.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 5 / 11

Model Setup

Relationship between efforts and energy efficiency Φ Φ E = 0 ν 1 − ν E = e 1 − ν ν with ν > 1

2 and 0 < Φ < Φ < 1.

Expected level of energy efficiency: Φ(E) =    Φl =

ΦΦ (1−ν)Φ+νΦ, if E = 0

Φh =

ΦΦ νΦ+(1−ν)Φ, if E = e

, with Φh > Φl by assumption.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 5 / 11

Model Setup

Relationship between efforts and energy efficiency Φ Φ E = 0 ν 1 − ν E = e 1 − ν ν with ν > 1

2 and 0 < Φ < Φ < 1.

Expected level of energy efficiency: Φ(E) =    Φl =

ΦΦ (1−ν)Φ+νΦ, if E = 0

Φh =

ΦΦ νΦ+(1−ν)Φ, if E = e

, with Φh > Φl by assumption. Overall social welfare: W (Q, E) = S(Q) + γπ(E, Q) W : total social welfare, S(Q): consumer’s surplus, γ (0 < γ < 1): the weight assigned on the monopolist’s expected profit.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 5 / 11

Model Setup

Relationship between efforts and energy efficiency Φ Φ E = 0 ν 1 − ν E = e 1 − ν ν with ν > 1

2 and 0 < Φ < Φ < 1.

Expected level of energy efficiency: Φ(E) =    Φl =

ΦΦ (1−ν)Φ+νΦ, if E = 0

Φh =

ΦΦ νΦ+(1−ν)Φ, if E = e

, with Φh > Φl by assumption. Overall social welfare: W (Q, E) = S(Q) + γπ(E, Q) W : total social welfare, S(Q): consumer’s surplus, γ (0 < γ < 1): the weight assigned on the monopolist’s expected profit. Objective of the regulator: to maximize overall social welfare

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 5 / 11

The Unregulated Monopolist

Lemma 1 The unregulated monopolist’s optimal choice of effort is given as follows:

Effort Cost Optimal Effort Optimal Price Expected Social Welfare

• e1 e

E = 0

aΦl +c 2Φl (2γ+1)(aΦl −c)2 8bΦ2

l

0 < e < e1 E = e

aΦh+c 2Φh (2γ+1)(aΦh−c)2 8bΦ2

h

where e1 = c(2ν−1)(Φ−Φ)[2aΦΦ−c(Φ+Φ)]

4bΦ2Φ

2

LHS: effort cost; RHS: difference of benefit obtained from positive effort and zero effort

• e1 increases when a increases (i.e., the demand shifts outward),
• e1 increases when b decreases (i.e., flatter/ less steep),
• e1 increases with c for sufficiently low values of c and decreases with

c for sufficient high values of c (i.e., non-monotonic with c).

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 6 / 11

Rate of Return Regulation

Lemma 2 Under rate-of-return regulation, the monopolist chooses E = 0, and regulated prices are given by: PROR∗ =

• PROR∗ = c

Φ, Φ = Φ

P

ROR∗ = c Φ, Φ = Φ

, and expected social welfare is given by: W ror∗ = ν(aΦ − c)2 2bΦ2 + (1 − ν)(aΦ − c)2 2bΦ

2

, where (aΦ−c)2

2bΦ2

refers to the ex post welfare with Φ = Φ, while (aΦ−c)2

2bΦ

2

is the ex post welfare with Φ = Φ.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 7 / 11

Price Cap Regulation and Revenue Cap Regulation

Lemma 3 The optimal price cap, level of effort and expected social welfare are fully characterized as

Effort Cost Effort Level Optimal Price Cap Expected Social Welfare e e2 E = 0

c Φl (aΦl −c)2 2bΦ2 l

0 < e < e2 E = e

aΦh +c−

• (aΦh −c)2−4bΦ2

he 2Φh (aΦh −c)2+(aΦh −c)

• (aΦh −c)2−4bΦ2

he) 4bΦ2 h

− e

2

where e2 = c(aΦl−c)(Φh−Φl)

bΦhΦ2

l

LHS: effort cost; RHS: difference of benefits obtained from positive effort and zero effort. In our setting there is no meaningful distinction between price cap and revenue cap regulation.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 8 / 11

Mandated Target Regulation

Lemma 4 The optimal mandated target for energy efficiency is Φmt = Φ. The characterization of mandated target regulation is summarized as

Mandated Target Effort Cost Effort Level Expected Social Welfare Φmt = Φ e e4 E = 0

(2γ+1)(aΦl −c)2 8bΦ2

l

+ (1 − γ)νδ 0 < e < e4 E = e

(2γ+1)(aΦh−c)2 8bΦ2

h

− γe + (1 − γ)(1 − ν)δ

where e4 = (aΦh−c)2

4bΦ2

h

− (aΦl−c)2

4bΦ2

l

+ (2ν − 1)δ LHS: effort cost; RHS: difference of benefits obtained from positive effort and zero effort, with (2ν − 1)δ as the difference of penalization avoided for not meeting targets.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 9 / 11

Expected Welfare under Different Regulatory Regimes

Lemma 5 According to the comparison of expected social welfare under different regulatory regimes, the optimal regulation is summarized as:

Levels of Effort Level Social Welfare Effort Cost Unreg ROR PC MT Comparison e e2 E = 0 E = 0 E = 0 E = 0 W ror∗ > W pc∗

l

> W mt∗

l

> W ∗

l

• e3 e <

e2 E = 0 E = 0 E = e E = 0 W ror∗ W pc∗

h

> W mt∗

l

> W ∗

l

• e4 e <

e3 E = 0 E = 0 E = e E = 0 W pc∗

h

> W ror∗ > W mt∗

l

> W ∗

l

• e1 e <

e4 E = 0 E = 0 E = e E = e W pc∗

h

> W ror∗ > W mt∗

h

> W ∗

l

0 < e < e1 E = e E = 0 E = e E = e W pc∗

h

> W ror∗ > W mt∗

h

> W ∗

h

Price cap regulation dominates an unregulated monopolist. The comparison between rate of return regulation and price cap regulation is ambiguous and complex. Mandated target regulation is always dominated by both price cap and rate of return regulation although it can do better than an unregulated monopolist.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 10 / 11

Conclusions

Rate of return, price cap and mandated targets embed different incentives for the regulated firm to pursue energy efficiency at the network end.

Rate of return regulation provides no incentive. Price cap regulation provides incentives but creates rents to the monopolist. Mandated target regulation also provides incentives but they are too coarse.

Policies that are designed to encourage utilities to promote end-user energy efficiency (such as revenue decoupling) may reduce the incentives for utilities to pursue internal energy conservation.

Joisa Dutra, Flavio Menezes, Xuemei Zheng (Centre for Regulation, FGV; The University of Queensland) June 2014 11 / 11