肖云鹏：保障灵活调节资源充裕性的容量市场机制

保障灵活调节资源充裕性的容量市场机制西安交通大学电气工程学院肖云鹏2023年9月标题目录CONTENTS01容量市场的作用及问题P

A

R

T保障灵活调节资源充裕性的容量市场出清模型保障灵活调节资源充裕性的容量市场定价与结算机制保障灵活调节资源充裕性的容量市场仿真测算结论与展望2/30Part1

作用与问题ü

容量市场的建设意义Ø

容量市场的主要目的是保障系统充裕度。首要目标是确保电力系统拥有足够的发电能力来满足电力需求，在高峰期或突发情况下保障系统安全运行。电力市场特征容量市场建设意义•

新型电力系统不确定性极强可靠性容量保障可再生能源波动性、高峰期电力需求或突发情况威胁系统供电可靠性•

火电机组投资成本回收困难火电固定成本回收利用小时数较低的传统机组无法在电能量市场中获得持久稳定的收益•

市场多样性创造长期价格信号引导资源投资市场主体增多，电源/负荷结构变化较快，导致多样化的能源需求•

竞争性定价提供更稳定的定价机制电价由市场供需关系决定，系统容量不足时电价高涨，用户用电成本大大提高3/30Part1

作用与问题ü

PJM容量市场的发展l

容量市场发展历程PJM容量市场建立改革前199920072015容量义务分配模式容量信用市场模式（CCM）可靠性定价市场模式（RPM）容量表现市场阶段•

LSE承担容量责任•

LSE通过场内集中、自供给、•

LSE通过PJM从拍卖市场分配双边协商方式实现对原有容量市场资源做了进一步改善•

LSE承担容量责任•

LSE通过自供给或双边协商方式实现•

PJM通过拍卖市场购买后分配或自供给、双边协商方式实现基本容量Base容量表现CP4/30Part1

作用与问题ü

PJM容量市场的发展l

RPM市场架构供给侧资源出售容量购买容量P

JM拍卖市场发电资源容量购买费用分摊需求侧资源能效资源基础拍卖（BRA）追加拍卖（IA）负荷供应商LSE1负荷供应商LSE2负荷供应商LSE3在BRA中申报自供给聚合资源规划中的资源输电升级项目双边合同双边合同双边交易……5/30Part1

作用与问题ü

PJM容量市场的发展三年l

RPM市场交易时序20个月10个月3个月持续开展的双边市场PJM市场交易时序次年六月五月九月七月二月六月容量交付年第一次追加拍卖第二次追加拍卖第三次追加拍卖基本拍卖市场采购LDA的额外容量，以解决由骨干传输线延迟引起的可靠性问题条件追加拍卖6/30Part1

作用与问题对于存在区域输电约束的地区,每个区域（LDA）可以有单独的需求曲线。ü

PJM容量市场的发展l

RPM模式需求曲线制定——可变容量需求曲线（Variable

Resource

Requirement

,VRR）曲线取决于系统可靠性需求和新建机组的净成本,对市场出清价格有重要影响。1.5

Net

Cone价格上限:联合循环燃气轮机新进入成本净额的150%A(0.998IRM,

1.5

Net

Cone）需求曲线与价格上限的交叉点B(1.029IRM,

0.75

Net

Cone)C(1.088IRM,0)容量需求——根据资源充裕性目标设定,即峰值负荷加上所需的装机备用裕度(IRM)0.75

Net

Cone根据十年一遇失负荷期望(LOLE)要求计算得出。IRM-0.2%

IRM

IRM+2.9%IRM+8.8%7/30Part1

作用与问题ü

PJM容量市场的发展需求l

RPM市场出清流程：基本拍卖市场中各LDA的VRR出清结果供给容量资源供给容量和报价求解优化算法•

区域出清容量•

区域容量价格•

容量输送权（CTR）价格约束区域限制约束出清容量约束8/30Part1

作用与问题ü

PJM容量市场的发展l

不同市场模式对比：容量信用市场（CCM模式）可靠性定价市场（RPM模式）提前1年的容量拍卖市场持续时间提前3年的前瞻性容量拍卖市场采用倾斜的容量需求曲线开展日前、月度和多月容量市场采用垂直的容量需求曲线需求曲线制定所有价格下的容量需求都固定在资源充裕性目标上，导致价格剧烈波动允许需求侧资源、输电升级项目、聚合资源、能效资源以及规划中的资源参与市场竞争仅限在役发电机组供给侧资源定价模式资源利用不充分全区域统一定价不考虑区域间传输约束区域内部受约束地区产生可靠性问题考虑传输约束的分区定价9/30Part1

作用与问题ü

当前容量市场存在的问题l

新型电力系统对充裕性需求多样化。l

新能源、储能等新兴市场主体的有效容量评估困难。问题10/30PromotionalArticleaddedbytheECE,notincludedintheoriginalslidesEnergy

Conversion

and

EconomicsDOI:

10.1049/enc2.12050ORIGINAL

RESEARCH

PAPERDistributed

control

strategy

for

transactive

energy

prosumers

inreal-time

marketsChen

Yin1Ran

Ding2Haixiang

Xu2Gengyin

Li1Xiupeng

Chen3Ming

Zhou11

State

Key

Laboratory

of

Alternate

Electrical

PowerSystem

with

Renewable

Energy

Sources,

School

ofAbstractThe

increasing

penetration

of

distributed

energy

resources

(DERs)

has

led

to

increasingresearch

interest

in

the

cooperative

control

of

multi-prosumers

in

a

transactive

energy

(TE)paradigm.

While

the

existing

literature

shows

that

TE

offers

significant

grid

flexibility

andeconomic

benefits,

few

studies

have

addressed

the

incorporation

of

security

constraints

inTE.

Herein,

a

market-based

control

mechanism

in

real-time

markets

is

proposed

to

eco-nomically

coordinate

the

TE

among

prosumers

while

ensuring

secure

system

operation.Considering

the

dynamic

characteristics

of

batteries

and

responsive

demands,

a

model

pre-dictive

control

(MPC)

method

is

used

to

handle

the

constraints

between

different

timeintervals

and

incorporate

the

following

generation

and

consumption

predictions.

Owing

tothe

computational

burden

and

individual

privacy

issues,

an

efficient

distributed

algorithmis

developed

to

solve

the

optimal

power

flow

problem.

The

strong

coupling

between

pro-sumers

through

power

networks

is

removed

by

introducing

auxiliary

variables

to

acquirelocational

marginal

prices

(LMPs)

covering

energy,

congestion,

and

loss

components.

Casestudies

based

on

the

IEEE

33-bus

system

demonstrated

the

efficiency

and

effectiveness

ofthe

proposed

method

and

model.Electrical

and

Electronic

Engineering,

North

ChinaElectric

Power

University,

Beijing,

China2

State

Grid

Jibei

Electric

Power

Co.,

Ltd.,

Beijing,China3

Engineering

and

Technology

Institute

Groningen,University

of

Groningen,

Groningen,

TheNetherlands标题目录CONTENTS01容量市场的作用及问题P

A

R

T02保障灵活调节资源充裕性的容量市场出清模型P

A

R

T保障灵活调节资源充裕性的容量市场定价与结算机制保障灵活调节资源充裕性的容量市场仿真测算结论与展望Part2出清模型ü

容量市场出清模型构建传统容量市场只考虑保障负荷峰值时段系统充裕度，未来在高比例新能源接入的新型电力系统场景下，新能源的波动性和不确定性将对电力系统的调峰能力、灵活爬坡调节能力提出了更高的要求。根据各类型资源有效容量评估方法、系统容量充裕度评估方法、关键断面约束辨识技术，构建充分考虑长期有效容量和煤电深调容量的容量市场出清模型。容量市场需求曲线资源供应曲线Ø

目前考虑保障负荷峰值时段系统充裕度、灵活爬坡能力出清价格充裕度。下一步计划将类似考虑调峰能力充裕度。l

目标函数：社会福利最大化

max

SW=

d

Pd

(

cgiPig

cwhPwh

cskPsk

cemP

)mel,n

l,nl,nihkm负荷火电风电光伏储能容量/MW12/30Part2出清模型ü

容量市场出清模型构建根据各类型资源有效容量评估方法、系统容量充裕度评估方法、关键断面约束辨识技术，构建充分考虑长期有效容量和煤电深调容量的容量市场出清模型。l

约束条件：•

供需平衡约束满足系统灵活爬坡调节需求的容量供需平衡约束•

各类型机组中标容量约束、容量需求约束•

火电机组、储能提供灵活爬坡调节容量•

采用嵌入式优化考虑新能源不确定性波动保障负荷峰值时段系统充裕度的容量供需平衡约束

Fg

Fem

FCIOminisn

D

wD

sD

i

m

s

D

,

P

,

PPg

Pwh

Psk

Pemnhknnni

FnR:

FRupn,

n系统灵活爬坡调节容量需求考虑负荷、新能源出力的波动量的不确定性偏差i

h

k

m

nnnn

PCIO

=

Pd:

nCap

,

nl,nsn(

Fg

Fem

FCIO

(

FnR))mins

lisnn

D

,

P

,

PD

wD

sD

i

m

s

nnhknn区域s向区域n传输的容量

0:

nFRdn

,

n13/30Part2出清模型ü

容量市场出清模型构建储能0

Pem

CPe,max

:

e,min

,

e,max,

ml

约束条件：mmm0

Fme

RemPe,max

:

mfce,min

,

mfce,max

,

mm•

供需平衡约束•

各类型机组中标容量约束、容量需求约束

0

P

,

ep,max

,

mmemFemPe,maxm:ep,minm火电0

Pem

Fme

Pe,max

:

men,min

,

en,max

,

mmm0

Pg

CPg,max

:

ig,min

,

ig,max

,

i由边际带负荷能力的有效容量评估方法得到的，火电资源参与容量市场可提供的有效容量ii区域间传输容量0

Fg

RigPg,max

:

ifcg,min

,

ifcg,max

,

iii

Lmax

PCIO

Lmax

:

CIO,min

,

CIO,max0

Pgg

Fg

Pg,max

:

igp,min

,

igp,max

,

isnsnsnsnsniii火电资源所能提供的最大灵活爬坡调节容量CIO

PCIO

0

:

CIOPsn

ns

sn0

P

Fg

Pg,max

:

ign,min

,

ign,max

,

iiii

Lmax

FsCnIO

Lmax

:

FCIO,min

,

FCIO,maxsnsnsnsn新能源容量需求CIO

FnCsIO

0

:

sFnCIOFsnCIO0

Pd

Pd

,max0

Pws

CPw,max

:

hw,min

,

hw,max

,

h

Lmax

Psn

FCIO

L,min

,

L,maxL

:maxl,nl,nhhsnsnsn

sn

sn:

d,min

,

d,max

,

l,n0

P

CPs,max

:

ks,min

,

ks,max

,

k,

n,

s

nl,nl,nkk14/30标题目录CONTENTS01容量市场的作用及问题P

A

R

T02保障灵活调节资源充裕性的容量市场出清模型P

A

R

T03

保障灵活调节资源充裕性的容量市场定价与结算机制P

A

R

T保障灵活调节资源充裕性的容量市场仿真测算结论与展望Part3

定价与结算机制ü

容量市场机制与规则设计l

容量市场定价机制：保障负荷峰值时段系

nCap统容量充裕度的容量价格容量市场出清价格灵活爬坡调节预测需求

EFR价格n

UFRINn灵活爬坡调节向上偏差需求价格满足系统灵活爬坡调节需求的容量价格灵活爬坡调节不确定性偏差需求价格灵活爬坡调节向

nUFRDN下偏差需求价格16/30Part3

定价与结算机制ü

容量市场机制与规则设计l

容量市场定价机制：p

保障系统灵活性的容量价格

L灵活爬坡调节需求向上偏差价格

UFRIN

FRup

(uFRup

)

FRdn

(uFRdn

)n

n

(h)

n

n

(h)n

(

PwD,min

)①灵活爬坡调节预测需求价格h

n

L

FRup

uFRup()(

)

FRdn

uFRdnH

k)

n

n

(②灵活爬坡调节不确定性偏差需求价格nn(H

k

)

PsD(,min

)k

n

L

FRup

(uFRup

)

FRdn

(uFRdn

)

,

n(2(H

K)

N

n))灵活爬坡调节不确定性偏差需求价格与负荷、风电、光伏出力波动量的不确定性偏差值有关。

nn(2(H

K)

N

n))nn(

DD,max

)n

L灵活爬坡调节需求向下偏差价格

UFRDNn

nFRup(u

)(H

K

N

h)FRupn

nFRdn(u

)(H

K

N

h)nFRdn

PwD,maxh

n

L

nFRup

(unFRup

)(2H

nFRdn

(unFRdn(2H

K

N

k

))

K

N

k

)

PsD,maxk

n

L

nFRup

(unFRup)

nFRdn

(unFRdn

)(H

K

n)

,

nH

K

n)(

DnD,min()17/30Part3

定价与结算机制ü

容量市场机制与规则设计火电机组、储能电站•

保障负荷峰值时段系统充裕性的容量收益l

容量市场结算机制：+保障系统灵活性的容量收益

g

Cap

Pg

(

FRup

FRdn

)F

g

,

iin:i

nin:i

nn:i

ni•

给出火电、新能源、储能等不

e

Cap

Pe

(

FRup

FRdn

)F

e

,

m同类型资源相应的结算规则。•

有效区分不同类型资源的对于保障负荷峰值时段系统充裕度、灵活爬坡调节能力充裕度的有效容量贡献与引起灵活爬坡调节需求的责任。n:m

nn:m

nn:m

nmmm风电场、光伏电站•

提供容量保障负荷峰值时段系统充裕性的收益-分摊由于自身出力波动造成的灵活调节需求成本

EFR

(

PwD,exp

)

UFRDN

PwD,max

n:h

nhn:h

nh

wj

CapPwh

,

hn:h

n

UFRIN(

PwD,min)

n:h

nh

EFR

(

PsD,exp

)

UFRDN

PsD,max

n:k

nkn:k

nk

ks

CapPsk

,

kn:k

n

UFRIN(

PsD,min)

n:k

nk18/30Part3

定价与结算机制ü

容量市场机制与规则设计区域间传输容量l

容量市场结算机制：•

考虑了区域之间的价格差异，当区域间传输通道发生阻塞时会产生阻塞盈余，应分配给对应输电权所有者。•

给出火电、新能源、储能等不同类型资源相应的结算规则。•

有效区分不同类型资源的对于保障负荷峰值时段系统充裕度、灵活爬坡调节能力充裕度的有效容量贡献与引起灵活爬坡调节需求的责任。

CapPCIO

(

nFRup

nFRdn

)FCIOsn

snsnn负荷•

向容量市场支付保障负荷峰值时段系统充裕性+保障系统灵活性的容量费用

EFR

DD,exp

UFRDN

(

DD,min

)

d

CapPd

l,nnnnn

n,

nn

UFRIN

DD,max

lnn19/30Part3

定价与结算机制ü

容量市场机制性质验证•

良好的市场机制应满足社会效率、收支平衡、个体理性和激励相容等性质，激励市场主体主动参与，促进资源优化配置。社会效率(SocialEfficiency)所提出的容量市场鲁棒优化出清模型的目标函数为最大化社会福利，即出清结果能够在应对负荷、风电、光伏的任何不确定波动情况下实现尽可能大的社会福利，因此可以满足社会效率性质。20/30Part3

定价与结算机制ü

容量市场机制性质验证收支平衡(Budget

Balance)•

市场运营机构应为非盈利机构，市场的流入和流出资金应相等，即收支平衡。•容量市场流出资金：•容量市场流入资金：

(

Cap

Pg

(

FRup

FRdn

)Fg)

ninni

IN

CapnPdl,n

EFRnDexpn

(

UFRDN

(

DnD,min

)

nUFRIN

DnD,max)

n

OT

i

nlnn

(

Cap

Pe(FRupnFRdn

)Fe

)nmnm负荷为引起峰值时段需求、引起灵活调节需求所支付的费用

m

EFR

(

PwD,exp

)

(

UFRDN

PwD,max

nUFRIN

(

PwD,min))nhnhh支付给火电、储能保障负荷峰值时段

hh系统充裕度、满足系统灵活调节需求的费用风电为引起灵活调节需求所支付的费用

n

Cap

Pw

Cap

Psk

EFR

(

PsD,exp

)

(

UFRDN

PsD,max

nUFRIN

(

PsD,min

))nh

nknkk

hkkk支付给风电和光伏保障负荷峰值时段系统充裕度的费用光伏为引起灵活调节需求所支付的费用

(

Cap

PCIO

(

FRup

FRdn

)FCIO

)nsnnnsn

IN

OT

根据供需平衡约束和KKT条件，可以推导出sn区域传输容量阻塞盈余21/30Part3

定价与结算机制ü

容量市场机制性质验证个体理性(Individual

Rationality)•

个体理性指市场成员愿意主动参与市场，即各市场成员的净利润非负。以火电机组为例

ig

capPig

(

FRup

FRdn

)Fg

cigPig火电机组利润为：n:i

nn:i

nn:i

ni

(

cap

cig)Pg

(

FRup

FRdn

)Fig根据KKT条件，可以推导出n:i

nin:i

nn:i

n

(

ig,max

ig,min

igp,max

igp,min

ign,max

ign,min

)Pgi

(

igp,max

igp,min

ign,max

ign,min

ifcg,max

ifcg,min

)Fgi

Pg,max

(

ig,max

igp,max

ign,max

)

Rigg,max

ifcg,maxPii

022/30Part3

定价与结算机制ü

容量市场机制性质验证激励相容(Incentive

Compatibility)•

激励相容是指市场成员追求自身利润最大的结果与市场整体实现社会福利最大化的结果一致，即市场成员根据市场出清价格计算使得自身利润最大化的出力计划与市场根据成员报价出清的出力计划一致。•

容量市场出清模型•市场成员根据市场出清价格以自身利润最大化为目标进行优化的模型

cr

xrTmin

x,y,z

rRrmax

s.t.

ACap

x

AFR

y

AUFR

z

B

:

τ

,

nrrrrrrnn

Capn:r

n

FRn:r

n

UFRn:r

n

Tr

r

r

r

nmax

ρx

ρy

ρz

c

x

nnrrrr

r

(x

,

y

,z

)

Χ

,

r

x

,y

,zrrrrrr

s.t.(x

,y

,z

)

Χ

,

r

rrrr对偶转换由KKT可得，目标函数满足

Rrmax

(ρCap

,

ρFR

,

ρUFR

)

(ρCap

*r

FRn:r

n*r

ρUFRn:r

n*r

Tr*rxρyzcx)n:r

nn:r

nn:r

nn:r

n

[Rrmax

(ρCap*

,

ρFR*

,

ρUFR*

)

(ρCap*x*r

ρFR*y*r

ρUFR*z*r

crTx*r)]rr

n:r

nn:r

nn:r

nn:r

nn:r

nn:r

n

minn

τ(

)T(

ArCap

x

AFR

y

AUFR

z

B*r*r*r)rnrrn

0τ

0

r

nnrrnn

(x

,

y

,z

)

Χ

,

rx

x*r,

y

y*r,

z

zr*r当上式等号成立，即市场成员使得自身利rrrrrr润最大化的容量策略与容量市场出清的中标容量一致23/30PromotionalArticleaddedbytheECE,notincludedintheoriginalslidesReceived:

16

December

2020Revised:

11

April

2021Accepted:

17

April

2021Energy

Conversion

and

EconomicsDOI:

10.1049/enc2.12036ORIGINAL

RESEARCH

PAPEROption-based

portfolio

risk

hedging

strategy

for

gas

generatorbased

on

mean-variance

utility

modelShuying

LaiJing

QiuYuechuan

TaoSchool

of

Electrical

and

Information

Engineering,The

University

of

Sydney,

Sydney,

AustraliaAbstractNatural

gas

generators

are

promising

devices

for

reducing

greenhouse

gas

emissions.

How-ever,

gas

generators

encounter

difficulties

in

the

bid-to-sell

process

based

on

a

relativelyhigh

levelised

cost

of

energy

for

power

generation.

Therefore,

a

novel

risk

hedging

strategyis

developed

based

on

the

mean-variance

portfolio

theory

to

reduce

the

operational

risksof

gas

generators

and

enhance

their

profits.

Three

types

of

options

are

utilised

and

com-bined

to

form

a

portfolio

of

financial

hedges:

the

short

put

option,

long

put

option,

andshort

call

option.

Two

types

of

energy

storage

devices

are

used

to

facilitate

the

risk

hedgingprocess,

namely

power-to-gas

and

battery

devices.

Simulation

results

demonstrate

that

theproposed

risk

hedging

model

can

ensure

higher

profits

for

gas

generators

with

reducedrisk

compared

to

the

traditional

risk

hedging

model

and

a

model

using

only

one

type

ofoption.

Additionally,

the

varied

risk

preferences

of

gas

generators

lead

to

varied

portfoliocombinations.

The

more

risk

averse

a

gas

generator,

the

more

likely

the

long-put

optionwill

be

utilised.

In

contrast,

the

less

risk

averse

a

gas

generator,

the

more

likely

that

shortcalls

will

be

utilised.标题目录CONTENTS01容量市场的作用及问题P

A

R

T02保障灵活调节资源充裕性的容量市场出清模型P

A

R

T03

保障灵活调节资源充裕性的容量市场定价与结算机制04

保障灵活调节资源充裕性的容量市场仿真测算P

A

R

T结论与展望Part4

仿真测算ü

算例分析Ø

考虑保障负荷峰值时段系统充裕度、灵活爬坡能力充裕度。下一步计划将类似考虑调峰能力充裕度。Ø

选取修正的IEEE-118节点系统进行算例分析，将该系统划分为3个区域，其中区域1新能源机组较为集中，共有2台火电机组、7座风电场、5座光伏电站和1座储能电站；区域2和3则具有较多爬坡性能优异的灵活性资源，区域2共有12台火电机组、4座风电场、2座光伏电站和2座储能电站；区域3共有11台火电机组、3座风电场、2座光伏电站和3座储能电站。25/30Part4

仿真测算各区域出清价格ü

算例分析出清价格/(元/(MW·天))区域

1区域

2区域

3

Cap210180210n

EFR11011008080010100n

Capn

UFRIN•

保障负荷峰值时段系统充裕性的容量价格：n

UFRDN区域2为180元/(MW·天)，低于区域1和3。因为区域1和区域3需要由区域2来提供容量保障各自区域内的负荷峰值时段系统充裕性，区域2和其它区域之间存在阻塞，区域1、3的保障负荷峰值时段系统充裕度的容量出清价格高于区域2。n区域需求容量火电中标容量储能中标容量风电中标容量光伏中标容量1201008060402001

0009008007006005004003002001000区域需求容量火电中标容量储能中标容量906.7595区域需求容量区间710

EFRn•

灵活爬坡调节预测需求价格：区域1为110元545/(MW·天)，高于区域2、3。因为区域1为高比例新能源区域，风电场、光伏电站装机容量占比高达80%，具有较大的灵活调节需求，但其火电机组数量少且爬坡系数小，灵活调节性能较差，需要由其他区域提供容量来保障系统灵活性，传输通道发生阻塞。364.444.5

上范围45上范围4523上范围下范围38.25下范围31.5195下范围178.85231.25区域1区域2区域3区域1区域2区域3(a)各区域保障负荷峰值时段充裕度的中标容量与需求容量(b)各区域满足灵活爬坡调节需求的中标容量与需求容量26/30Part4

仿真测算各区域出清价格出清价格/(元/(MW·天))区域

1区域

2区域

3ü

算例分析

Cap210180210n

EFR11011008080010100n

UFRIN

UFRIN：等于灵活•

灵活爬坡调节需求向上偏差价格nn

UFRDN

EFR爬坡调节预测需求价格

。因为本算例中各区域nn的灵活调节需求大于0，即表示需要向上的满足灵活调节需求的容量。此时算例中的灵活爬坡调节预测需求价格与灵活爬坡调节不确定性偏差需求价格均由灵活爬坡供需约束的对偶变量决定。区域需求容量火电中标容量储能中标容量风电中标容量光伏中标容量1201008060402001

0009008007006005004003002001000区域需求容量火电中标容量储能中标容量906.7595区域需求容量区间710

UFRDNn545•

灵活爬坡调节需求向下偏差价格：等于0。44.5

上范围45上范围下范围4523上范围下范围因为算例中灵活爬坡调节需求大于0，当灵活爬坡调节需求不确定性向下偏差时，即需求减小，原本的出清结果依旧能够满足需求。364.438.25下范围31.5195178.85231.25区域1区域2区域3区域1区域2区域3(a)各区域保障负荷峰值时段充裕度的中标容量与需求容量(b)各区域满足灵活爬坡调节需求的中标容量与需求容量27/30Part4

仿真测算ü

算例分析典型机组收益•

对比火电机组G40和G41，可见二者的报价和装机容量均相同，由于G40爬坡系数大于G41，灵活调节能力更好，能够提供更多的容量来满足系统灵活调节需求，因此G40的收益高于G41。•

对比风电场G28和G29、光伏电站G32和G33、储能电站G34和G35，可见相同报价与装机容量下，可用容量系数越大，即资源参与容量市场的可用容量越大，保障负荷峰值时段系统充裕度的容量中标量越大，收益越多。•

所提出的容量市场机制能够有效区分不同类型资源对于保障负荷峰值时段系统充裕度和满足灵活爬坡调节需求的贡献，并给予相应的奖励。相同条件下，资源的灵活调节能力越好，可用容量系数越大，则所能获得的收益越多。28/30PromotionalArticleaddedbytheECE,notincludedintheoriginalslidesReceived:

9

December

2020Revised:

3

May

2021Accepted:

11

May

2021Energy

Conversion

and

EconomicsDOI:

10.1049/enc2.12037ORIGINAL

RESEARCH

PAPERElectricity

economics

for

ex-ante

double-sided

auctionmechanism

in

restructured

power

marketAruna

KanagarajKumudini

Devi

Raguru

PanduDepartment

of

EEE,

College

of

EngineeringGuindy,

Anna

University,

Chennai,

Tamil

Nadu,IndiaAbstractAuction

mechanism

analysis

provides

favourable

economic

outcomes

for

key

stakeholdersinvolved

in

the

restructured

power

market.

Real

power

pricing

based

on

locational

marginalpricing

has

been

implemented

in

the

electricity

market

worldwide.

In

this

study,

the

opti-mal

power

flow

is

considered

to

minimise

the

operating

cost

of

the

active

power

gener-ation

in

the

ex-ante

energy

market

and

an

augmented

optimal

power

flow

in

the

ex-antereserve

market.

The

double-sided

auction

mechanism

has

better

control

over

the

energyand

reserve

markets,

enhancing

social

welfare

in

the

restructured

power

markets.

Single-and

double-sided

auction

mechanisms

are

considered

to

analyse

the

allocation

and

pricingeconomics

in

the

ex-ante

day-ahead

energy

and

ex-ante

day-ahead

reserve

markets.

Loca-tional

marginal

pricing

is

calculated

and

analysed

for

both

the

on-and

off-peak

demandperiods.

The

proposed

auction

model

was

validated

using

an

IEEE

30-bus

power

system.The

benefits

of

the

double-sided

auction

are

assessed

from

technical

and

economic

per-spectives.标题目录CONTENTS01容量市场的作用及问题P

A

R

T02保障灵活调节资源充裕性的容量市场出清模型P

A

R

T03

保障灵活调节资源充裕性的容量市场定价与结算机制04

保障灵活调节资源充裕性的容量市场仿真测算P

A

R

T05结论与展望P

A

R

T29/30Part5

结论与展望l

针对新型电力系统发展下灵活调节资源稀缺性逐渐凸显的问题，提出了保障灵活调节资源充裕性的容量市场机制总结l

采用不确定性定价方法给出了灵活调节容量电价l

所提机制有效保障了系统灵活调节资源的充裕性l

进一步应考虑新型电力系统其他维度的充裕性需求，并与现货电能量与辅助服务市场做好有效衔接30/30谢谢！请批评指正！西安交通大学电气工程学院肖云鹏2023年9月Energy

Conversion

and

EconomicsDOI:

10.1049/enc2.12050ORIGINAL

RESEARCH

PAPERDistributed

control

strategy

for

transactive

energy

prosumers

inreal-time

marketsChen

Yin1Ran

Ding2Haixiang

Xu2Gengyin

Li1Xiupeng

Chen3Ming

Zhou11

State

Key

Laboratory

of

Alternate

Electrical

PowerSystem

with

Renewable

Energy

Sources,

School

ofElectrical

and

Electronic

Engineering,

North

ChinaElectric

Power

University,

Beijing,

ChinaAbstractThe

increasing

penetration

of

distributed

energy

resources

(DERs)

has

led

to

increasingresearch

interest

in

the

cooperative

control

of

multi-prosumers

in

a

transactive

energy

(TE)paradigm.

While

the

existing

literature

shows

that

TE

offers

significant

grid

flexibility

andeconomic

benefits,

few

studies

have

addressed

the

incorporation

of

security

constraints

inTE.

Herein,

a

market-based

control

mechanism

in

real-time

markets

is

proposed

to

eco-nomically

coordinate

the

TE

among

prosumers

while

ensuring

secure

system

operation.Considering

the

dynamic

characteristics

of

batteries

and

responsive

demands,

a

model

pre-dictive

control

(MPC)

method

is

used

to

handle

the

constraints

between

different

timeintervals

and

incorporate

the

following

generation

and

consumption

predictions.

Owing

tothe

computational

burden

and

individual

privacy

issues,

an

efficient

distributed

algorithmis

developed

to

solve

the

optimal

power

flow

problem.

The

strong

coupling

between

pro-sumers

through

power

networks

is

removed

by

introducing

auxiliary

variables

to

acquirelocational

marginal

prices

(LMPs)

covering

energy,

congestion,

and

loss

components.

Casestudies

based

on

the

IEEE

33-bus

system

demonstrated

the

efficiency

and

effectiveness

ofthe

proposed

method

and

model.2

State

Grid

Jibei

Electric

Power

Co.,

Ltd.,

Beijing,China3

Engineering

and

Technology

Institute

Groningen,University

of

Groningen,

Groningen,

TheNetherlandsCorrespondenceXiupengChen,EngineeringandTechnologyInstituteGroningen,UniversityofGroningen,9742AGGroningen,TheNetherlands.Email:a1124756041@163.comFundinginformationStateGridCorporationof

China,Grant/AwardNumber:5201202000161INTRODUCTIONcontrol

actions.

However,

this

centralized

network

architectureis

of

great

concern,

because

sending

all

this

information

to

aDriven

by

growing

environmental

and

climate

concerns,

dis-tributed

energy

resources

are

increasing

in

the

penetration

rateof

distribution

networks,

and

distribution

power

networks

areundergoing

a

fundamental

transition.

In

traditional

power

grids,users

only

have

load

characteristics,

but

with

the

rapid

develop-ment

of

distributed

power

generation

technology

and

Internettechnology,

users

can

gradually

manage

internal

power

genera-tion

and

storage

resources,

and

deliver

electrical

energy,

namelyprosumers.

Prosumers

are

end-use

consumers

with

local

genera-tion

sources,

for

example,

photovoltaic

(PV)

panels

and/or

bat-tery,

and

are

able

to

manage

their

consumption

and

productionof

energy

actively.

Under

the

promotion

of

the

market-basedtrading,

these

prosumers

are

held

as

independent

stakeholdersto

participate

in

power

market

operation

[1].

Traditionally,

distri-bution

power

networks

are

kept

stable

and

secure

by

centralizedsystem

operator

introduces

scalability,

complexity

and

privacyissues

[2].

Consequently,

more

decentralized

network

controland

optimization

techniques

are

required

to

support

the

energyamong

large

numbers

of

prosumers

[3].It

is

necessary

to

coordinate

the

market

and

control

and

man-age

the

system

through

economic

value

to

ensure

that

pro-sumers

participate

in

market

transactions

and

the

safe

and

flex-ible

operation

of

the

system,

the

existing

research

about

mech-anism

design

for

prosumers

can

be

classified

into

two

cate-gories:

distributed

optimization-based

method

[4]

and

gametheory

based

method

[5].

In

the

former

approach,

all

prosumersare

willing

to

collaborate

to

achieve

a

certain

goal,

for

example,maximizing

social

welfare.

A

non-profit

agent,

for

example,

sys-tem

operator

(SO),

is

programmed

to

set

prices

and

individualprosumers

choose

their

corresponding

strategies

as

price

takes.This

is

an

open

access

article

under

the

terms

of

the

Creative

Commons

Attribution

License,

which

permits

use,

distribution

and

reproduction

in

any

medium,

provided

the

original

work

isproperly

cited.©

2022

The

Authors.

Energy

Conversion

and

Economics

published

by

John

Wiley

&

Sons

Ltd

on

behalf

of

The

Institution

of

Engineering

and

Technology

and

the

State

Grid

Economic

&Technological

Research

Institute

Co.,

Ltd.Energy

Convers.

Econ.

2022;3:1–10./iet-ece12YIN

ET

AL.The

interaction

between

prosumers

with

the

SO

is

privacy

pre-serving

as

only

energy

preferences

are

communicated.

A

dis-tributed

price-based

optimization

mechanism

for

prosumers’energy

management

is

proposed

in

[6]

based

on

the

alternat-ing

direction

method

of

multipliers

(ADMM)

method.

In

[7],a

relaxed

consensus

innovation

(RCI)

approach

is

described

tosolve

multi-bilateral

economic

dispatch

problem

in

fully

decen-tralized

manner.

A

distributed

generation

and

demand

controlschemes

for

secondary

frequency

regulation

in

power

networksis

presented

to

guarantee

system

stability

and

economic

optimal-ity

simultaneously

[8].

In

the

latter

approach,

conflicting

inter-ests

of

prosumers

are

characterized.

The

key

point

here

is

tomodel

the

decision-making

processes

of

prosumers

and

find

theNash

equilibrium

so

that

each

prosumer

maximizes

their

prof-its

while

ensuring

the

system

supply

and

demand

balance.

Ref.[9]

systematically

clarifies

various

game-

and

auction-theoreticmethods

used

for

peer-to-peer

(P2P)

energy

trading

among

pro-sumers.

an

incentive-compatible

mechanism

is

proposed

in

[10]to

elicit

truthful

bids

of

generators

and

coordinate

the

economicoperation.

An

optimal

bidding

framework

is

proposed

in

[11]for

a

regional

energy

internet

to

participate

in

day-ahead

marketsconsidering

carbon

trading.

An

auction-theoretic

scheme

is

pre-sented

for

prosumer

models

and

resource

constraints

in

[12].

Anenergy

sharing

mechanism

is

proposed

in

[13]

to

accommodateprosumers’

strategic

decision-making

on

their

self-productionand

demand

in

the

presence

of

capacity

constraints.

In

thispaper,

a

distributed

optimization

algorithm

is

applied

consid-ering

the

fact

that

there

are

sufficien