肖云鵬：保障靈活調節(jié)資源充裕性的容量市場機制

保障靈活調節(jié)資源充裕性的容量市場機制西安交通大學電氣工程學院肖云鵬2023年9月標題目錄CONTENTS01容量市場的作用及問題P

A

R

T保障靈活調節(jié)資源充裕性的容量市場出清模型保障靈活調節(jié)資源充裕性的容量市場定價與結算機制保障靈活調節(jié)資源充裕性的容量市場仿真測算結論與展望2/30Part1

作用與問題ü

容量市場的建設意義?

容量市場的主要目的是保障系統(tǒng)充裕度。首要目標是確保電力系統(tǒng)擁有足夠的發(fā)電能力來滿足電力需求，在高峰期或突發(fā)情況下保障系統(tǒng)安全運行。電力市場特征容量市場建設意義?

新型電力系統(tǒng)不確定性極強可靠性容量保障可再生能源波動性、高峰期電力需求或突發(fā)情況威脅系統(tǒng)供電可靠性?

火電機組投資成本回收困難火電固定成本回收利用小時數(shù)較低的傳統(tǒng)機組無法在電能量市場中獲得持久穩(wěn)定的收益?

市場多樣性創(chuàng)造長期價格信號引導資源投資市場主體增多，電源/負荷結構變化較快，導致多樣化的能源需求?

競爭性定價提供更穩(wěn)定的定價機制電價由市場供需關系決定，系統(tǒng)容量不足時電價高漲，用戶用電成本大大提高3/30Part1

作用與問題ü

PJM容量市場的發(fā)展l

容量市場發(fā)展歷程PJM容量市場建立改革前199920072015容量義務分配模式容量信用市場模式（CCM）可靠性定價市場模式（RPM）容量表現(xiàn)市場階段?

LSE承擔容量責任?

LSE通過場內集中、自供給、?

LSE通過PJM從拍賣市場分配雙邊協(xié)商方式實現(xiàn)對原有容量市場資源做了進一步改善?

LSE承擔容量責任?

LSE通過自供給或雙邊協(xié)商方式實現(xiàn)?

PJM通過拍賣市場購買后分配或自供給、雙邊協(xié)商方式實現(xiàn)基本容量Base容量表現(xiàn)CP4/30Part1

作用與問題ü

PJM容量市場的發(fā)展l

RPM市場架構供給側資源出售容量購買容量P

JM拍賣市場發(fā)電資源容量購買費用分攤需求側資源能效資源基礎拍賣（BRA）追加拍賣（IA）負荷供應商LSE1負荷供應商LSE2負荷供應商LSE3在BRA中申報自供給聚合資源規(guī)劃中的資源輸電升級項目雙邊合同雙邊合同雙邊交易……5/30Part1

作用與問題ü

PJM容量市場的發(fā)展三年l

RPM市場交易時序20個月10個月3個月持續(xù)開展的雙邊市場PJM市場交易時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賣第二次追加拍賣第三次追加拍賣基本拍賣市場采購LDA的額外容量，以解決由骨干傳輸線延遲引起的可靠性問題條件追加拍賣6/30Part1

作用與問題對于存在區(qū)域輸電約束的地區(qū),每個區(qū)域（LDA）可以有單獨的需求曲線。ü

PJM容量市場的發(fā)展l

RPM模式需求曲線制定——可變容量需求曲線（Variable

Resource

Requirement

,VRR）曲線取決于系統(tǒng)可靠性需求和新建機組的凈成本,對市場出清價格有重要影響。1.5

Net

Cone價格上限:聯(lián)合循環(huán)燃氣輪機新進入成本凈額的150%A(0.998IRM,

1.5

Net

Cone）需求曲線與價格上限的交叉點B(1.029IRM,

0.75

Net

Cone)C(1.088IRM,0)容量需求——根據(jù)資源充裕性目標設定,即峰值負荷加上所需的裝機備用裕度(IRM)0.75

Net

Cone根據(jù)十年一遇失負荷期望(LOLE)要求計算得出。IRM-0.2%

IRM

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

作用與問題ü

PJM容量市場的發(fā)展需求l

RPM市場出清流程：基本拍賣市場中各LDA的VRR出清結果供給容量資源供給容量和報價求解優(yōu)化算法?

區(qū)域出清容量?

區(qū)域容量價格?

容量輸送權（CTR）價格約束區(qū)域限制約束出清容量約束8/30Part1

作用與問題ü

PJM容量市場的發(fā)展l

不同市場模式對比：容量信用市場（CCM模式）可靠性定價市場（RPM模式）提前1年的容量拍賣市場持續(xù)時間提前3年的前瞻性容量拍賣市場采用傾斜的容量需求曲線開展日前、月度和多月容量市場采用垂直的容量需求曲線需求曲線制定所有價格下的容量需求都固定在資源充裕性目標上，導致價格劇烈波動允許需求側資源、輸電升級項目、聚合資源、能效資源以及規(guī)劃中的資源參與市場競爭僅限在役發(fā)電機組供給側資源定價模式資源利用不充分全區(qū)域統(tǒng)一定價不考慮區(qū)域間傳輸約束區(qū)域內部受約束地區(qū)產生可靠性問題考慮傳輸約束的分區(qū)定價9/30Part1

作用與問題ü

當前容量市場存在的問題l

新型電力系統(tǒng)對充裕性需求多樣化。l

新能源、儲能等新興市場主體的有效容量評估困難。問題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

signi?cant

grid

?exibility

andeconomic

bene?ts,

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

ef?cient

distributed

algorithmis

developed

to

solve

the

optimal

power

?ow

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

ef?ciency

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標題目錄CONTENTS01容量市場的作用及問題P

A

R

T02保障靈活調節(jié)資源充裕性的容量市場出清模型P

A

R

T保障靈活調節(jié)資源充裕性的容量市場定價與結算機制保障靈活調節(jié)資源充裕性的容量市場仿真測算結論與展望Part2出清模型ü

容量市場出清模型構建傳統(tǒng)容量市場只考慮保障負荷峰值時段系統(tǒng)充裕度，未來在高比例新能源接入的新型電力系統(tǒng)場景下，新能源的波動性和不確定性將對電力系統(tǒng)的調峰能力、靈活爬坡調節(jié)能力提出了更高的要求。根據(jù)各類型資源有效容量評估方法、系統(tǒng)容量充裕度評估方法、關鍵斷面約束辨識技術，構建充分考慮長期有效容量和煤電深調容量的容量市場出清模型。容量市場需求曲線資源供應曲線?

目前考慮保障負荷峰值時段系統(tǒng)充裕度、靈活爬坡能力出清價格充裕度。下一步計劃將類似考慮調峰能力充裕度。l

目標函數(shù)：社會福利最大化

max

SW=

d

Pd

(

cgiPig

cwhPwh

cskPsk

cemP

)mel,n

l,nl,nihkm負荷火電風電光伏儲能容量/MW12/30Part2出清模型ü

容量市場出清模型構建根據(jù)各類型資源有效容量評估方法、系統(tǒng)容量充裕度評估方法、關鍵斷面約束辨識技術，構建充分考慮長期有效容量和煤電深調容量的容量市場出清模型。l

約束條件：?

供需平衡約束滿足系統(tǒng)靈活爬坡調節(jié)需求的容量供需平衡約束?

各類型機組中標容量約束、容量需求約束?

火電機組、儲能提供靈活爬坡調節(jié)容量?

采用嵌入式優(yōu)化考慮新能源不確定性波動保障負荷峰值時段系統(tǒng)充裕度的容量供需平衡約束

Fg

Fem

FCIOminisn

D

wD

sD

i

m

s

D

,

P

,

PPg

Pwh

Psk

Pemnhknnni

FnR:

FRupn,

n系統(tǒng)靈活爬坡調節(jié)容量需求考慮負荷、新能源出力的波動量的不確定性偏差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區(qū)域s向區(qū)域n傳輸?shù)娜萘?/p>

0:

nFRdn

,

n13/30Part2出清模型ü

容量市場出清模型構建儲能0

Pem

CPe,max

:

e,min

,

e,max,

ml

約束條件：mmm0

Fme

RemPe,max

:

mfce,min

,

mfce,max

,

mm?

供需平衡約束?

各類型機組中標容量約束、容量需求約束

0

P

,

ep,max

,

mmemFemPe,maxm:ep,minm火電0

Pem

Fme

Pe,max

:

men,min

,

en,max

,

mmm0

Pg

CPg,max

:

ig,min

,

ig,max

,

i由邊際帶負荷能力的有效容量評估方法得到的，火電資源參與容量市場可提供的有效容量ii區(qū)域間傳輸容量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火電資源所能提供的最大靈活爬坡調節(jié)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標題目錄CONTENTS01容量市場的作用及問題P

A

R

T02保障靈活調節(jié)資源充裕性的容量市場出清模型P

A

R

T03

保障靈活調節(jié)資源充裕性的容量市場定價與結算機制P

A

R

T保障靈活調節(jié)資源充裕性的容量市場仿真測算結論與展望Part3

定價與結算機制ü

容量市場機制與規(guī)則設計l

容量市場定價機制：保障負荷峰值時段系

nCap統(tǒng)容量充裕度的容量價格容量市場出清價格靈活爬坡調節(jié)預測需求

EFR價格n

UFRINn靈活爬坡調節(jié)向上偏差需求價格滿足系統(tǒng)靈活爬坡調節(jié)需求的容量價格靈活爬坡調節(jié)不確定性偏差需求價格靈活爬坡調節(jié)向

nUFRDN下偏差需求價格16/30Part3

定價與結算機制ü

容量市場機制與規(guī)則設計l

容量市場定價機制：p

保障系統(tǒng)靈活性的容量價格

L靈活爬坡調節(jié)需求向上偏差價格

UFRIN

FRup

(uFRup

)

FRdn

(uFRdn

)n

n

(h)

n

n

(h)n

(

PwD,min

)①靈活爬坡調節(jié)預測需求價格h

n

L

FRup

uFRup()(

)

FRdn

uFRdnH

k)

n

n

(②靈活爬坡調節(jié)不確定性偏差需求價格nn(H

k

)

PsD(,min

)k

n

L

FRup

(uFRup

)

FRdn

(uFRdn

)

,

n(2(H

K)

N

n))靈活爬坡調節(jié)不確定性偏差需求價格與負荷、風電、光伏出力波動量的不確定性偏差值有關。

nn(2(H

K)

N

n))nn(

DD,max

)n

L靈活爬坡調節(jié)需求向下偏差價格

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

定價與結算機制ü

容量市場機制與規(guī)則設計火電機組、儲能電站?

保障負荷峰值時段系統(tǒng)充裕性的容量收益l

容量市場結算機制：+保障系統(tǒng)靈活性的容量收益

g

Cap

Pg

(

FRup

FRdn

)F

g

,

iin:i

nin:i

nn:i

ni?

給出火電、新能源、儲能等不

e

Cap

Pe

(

FRup

FRdn

)F

e

,

m同類型資源相應的結算規(guī)則。?

有效區(qū)分不同類型資源的對于保障負荷峰值時段系統(tǒng)充裕度、靈活爬坡調節(jié)能力充裕度的有效容量貢獻與引起靈活爬坡調節(jié)需求的責任。n:m

nn:m

nn:m

nmmm風電場、光伏電站?

提供容量保障負荷峰值時段系統(tǒng)充裕性的收益-分攤由于自身出力波動造成的靈活調節(jié)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

定價與結算機制ü

容量市場機制與規(guī)則設計區(qū)域間傳輸容量l

容量市場結算機制：?

考慮了區(qū)域之間的價格差異，當區(qū)域間傳輸通道發(fā)生阻塞時會產生阻塞盈余，應分配給對應輸電權所有者。?

給出火電、新能源、儲能等不同類型資源相應的結算規(guī)則。?

有效區(qū)分不同類型資源的對于保障負荷峰值時段系統(tǒng)充裕度、靈活爬坡調節(jié)能力充裕度的有效容量貢獻與引起靈活爬坡調節(jié)需求的責任。

CapPCIO

(

nFRup

nFRdn

)FCIOsn

snsnn負荷?

向容量市場支付保障負荷峰值時段系統(tǒng)充裕性+保障系統(tǒng)靈活性的容量費用

EFR

DD,exp

UFRDN

(

DD,min

)

d

CapPd

l,nnnnn

n,

nn

UFRIN

DD,max

lnn19/30Part3

定價與結算機制ü

容量市場機制性質驗證?

良好的市場機制應滿足社會效率、收支平衡、個體理性和激勵相容等性質，激勵市場主體主動參與，促進資源優(yōu)化配置。社會效率(SocialEfficiency)所提出的容量市場魯棒優(yōu)化出清模型的目標函數(shù)為最大化社會福利，即出清結果能夠在應對負荷、風電、光伏的任何不確定波動情況下實現(xiàn)盡可能大的社會福利，因此可以滿足社會效率性質。20/30Part3

定價與結算機制ü

容量市場機制性質驗證收支平衡(Budget

Balance)?

市場運營機構應為非盈利機構，市場的流入和流出資金應相等，即收支平衡。?容量市場流出資金：?容量市場流入資金：

(

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負荷為引起峰值時段需求、引起靈活調節(jié)需求所支付的費用

m

EFR

(

PwD,exp

)

(

UFRDN

PwD,max

nUFRIN

(

PwD,min))nhnhh支付給火電、儲能保障負荷峰值時段

hh系統(tǒng)充裕度、滿足系統(tǒng)靈活調節(jié)需求的費用風電為引起靈活調節(jié)需求所支付的費用

n

Cap

Pw

Cap

Psk

EFR

(

PsD,exp

)

(

UFRDN

PsD,max

nUFRIN

(

PsD,min

))nh

nknkk

hkkk支付給風電和光伏保障負荷峰值時段系統(tǒng)充裕度的費用光伏為引起靈活調節(jié)需求所支付的費用

(

Cap

PCIO

(

FRup

FRdn

)FCIO

)nsnnnsn

IN

OT

根據(jù)供需平衡約束和KKT條件，可以推導出sn區(qū)域傳輸容量阻塞盈余21/30Part3

定價與結算機制ü

容量市場機制性質驗證個體理性(Individual

Rationality)?

個體理性指市場成員愿意主動參與市場，即各市場成員的凈利潤非負。以火電機組為例

ig

capPig

(

FRup

FRdn

)Fg

cigPig火電機組利潤為：n:i

nn:i

nn:i

ni

(

cap

cig)Pg

(

FRup

FRdn

)Fig根據(jù)KKT條件，可以推導出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

定價與結算機制ü

容量市場機制性質驗證激勵相容(Incentive

Compatibility)?

激勵相容是指市場成員追求自身利潤最大的結果與市場整體實現(xiàn)社會福利最大化的結果一致，即市場成員根據(jù)市場出清價格計算使得自身利潤最大化的出力計劃與市場根據(jù)成員報價出清的出力計劃一致。?

容量市場出清模型?市場成員根據(jù)市場出清價格以自身利潤最大化為目標進行優(yōu)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對偶轉換由KKT可得，目標函數(shù)滿足

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當上式等號成立，即市場成員使得自身利rrrrrr潤最大化的容量策略與容量市場出清的中標容量一致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

dif?culties

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

pro?ts.

Three

types

of

options

are

utilised

and

com-bined

to

form

a

portfolio

of

?nancial

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

pro?ts

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.標題目錄CONTENTS01容量市場的作用及問題P

A

R

T02保障靈活調節(jié)資源充裕性的容量市場出清模型P

A

R

T03

保障靈活調節(jié)資源充裕性的容量市場定價與結算機制04

保障靈活調節(jié)資源充裕性的容量市場仿真測算P

A

R

T結論與展望Part4

仿真測算ü

算例分析?

考慮保障負荷峰值時段系統(tǒng)充裕度、靈活爬坡能力充裕度。下一步計劃將類似考慮調峰能力充裕度。?

選取修正的IEEE-118節(jié)點系統(tǒng)進行算例分析，將該系統(tǒng)劃分為3個區(qū)域，其中區(qū)域1新能源機組較為集中，共有2臺火電機組、7座風電場、5座光伏電站和1座儲能電站；區(qū)域2和3則具有較多爬坡性能優(yōu)異的靈活性資源，區(qū)域2共有12臺火電機組、4座風電場、2座光伏電站和2座儲能電站；區(qū)域3共有11臺火電機組、3座風電場、2座光伏電站和3座儲能電站。25/30Part4

仿真測算各區(qū)域出清價格ü

算例分析出清價格/(元/(MW·天))區(qū)域

1區(qū)域

2區(qū)域

3

Cap210180210n

EFR11011008080010100n

Capn

UFRIN?

保障負荷峰值時段系統(tǒng)充裕性的容量價格：n

UFRDN區(qū)域2為180元/(MW·天)，低于區(qū)域1和3。因為區(qū)域1和區(qū)域3需要由區(qū)域2來提供容量保障各自區(qū)域內的負荷峰值時段系統(tǒng)充裕性，區(qū)域2和其它區(qū)域之間存在阻塞，區(qū)域1、3的保障負荷峰值時段系統(tǒng)充裕度的容量出清價格高于區(qū)域2。n區(qū)域需求容量火電中標容量儲能中標容量風電中標容量光伏中標容量1201008060402001

0009008007006005004003002001000區(qū)域需求容量火電中標容量儲能中標容量906.7595區(qū)域需求容量區(qū)間710

EFRn?

靈活爬坡調節(jié)預測需求價格：區(qū)域1為110元545/(MW·天)，高于區(qū)域2、3。因為區(qū)域1為高比例新能源區(qū)域，風電場、光伏電站裝機容量占比高達80%，具有較大的靈活調節(jié)需求，但其火電機組數(shù)量少且爬坡系數(shù)小，靈活調節(jié)性能較差，需要由其他區(qū)域提供容量來保障系統(tǒng)靈活性，傳輸通道發(fā)生阻塞。364.444.5

上范圍45上范圍4523上范圍下范圍38.25下范圍31.5195下范圍178.85231.25區(qū)域1區(qū)域2區(qū)域3區(qū)域1區(qū)域2區(qū)域3(a)各區(qū)域保障負荷峰值時段充裕度的中標容量與需求容量(b)各區(qū)域滿足靈活爬坡調節(jié)需求的中標容量與需求容量26/30Part4

仿真測算各區(qū)域出清價格出清價格/(元/(MW·天))區(qū)域

1區(qū)域

2區(qū)域

3ü

算例分析

Cap210180210n

EFR11011008080010100n

UFRIN

UFRIN：等于靈活?

靈活爬坡調節(jié)需求向上偏差價格nn

UFRDN

EFR爬坡調節(jié)預測需求價格

。因為本算例中各區(qū)域nn的靈活調節(jié)需求大于0，即表示需要向上的滿足靈活調節(jié)需求的容量。此時算例中的靈活爬坡調節(jié)預測需求價格與靈活爬坡調節(jié)不確定性偏差需求價格均由靈活爬坡供需約束的對偶變量決定。區(qū)域需求容量火電中標容量儲能中標容量風電中標容量光伏中標容量1201008060402001

0009008007006005004003002001000區(qū)域需求容量火電中標容量儲能中標容量906.7595區(qū)域需求容量區(qū)間710

UFRDNn545?

靈活爬坡調節(jié)需求向下偏差價格：等于0。44.5

上范圍45上范圍下范圍4523上范圍下范圍因為算例中靈活爬坡調節(jié)需求大于0，當靈活爬坡調節(jié)需求不確定性向下偏差時，即需求減小，原本的出清結果依舊能夠滿足需求。364.438.25下范圍31.5195178.85231.25區(qū)域1區(qū)域2區(qū)域3區(qū)域1區(qū)域2區(qū)域3(a)各區(qū)域保障負荷峰值時段充裕度的中標容量與需求容量(b)各區(qū)域滿足靈活爬坡調節(jié)需求的中標容量與需求容量27/30Part4

仿真測算ü

算例分析典型機組收益?

對比火電機組G40和G41，可見二者的報價和裝機容量均相同，由于G40爬坡系數(shù)大于G41，靈活調節(jié)能力更好，能夠提供更多的容量來滿足系統(tǒng)靈活調節(jié)需求，因此G40的收益高于G41。?

對比風電場G28和G29、光伏電站G32和G33、儲能電站G34和G35，可見相同報價與裝機容量下，可用容量系數(shù)越大，即資源參與容量市場的可用容量越大，保障負荷峰值時段系統(tǒng)充裕度的容量中標量越大，收益越多。?

所提出的容量市場機制能夠有效區(qū)分不同類型資源對于保障負荷峰值時段系統(tǒng)充裕度和滿足靈活爬坡調節(jié)需求的貢獻，并給予相應的獎勵。相同條件下，資源的靈活調節(jié)能力越好，可用容量系數(shù)越大，則所能獲得的收益越多。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

?ow

is

considered

to

minimise

the

operating

cost

of

the

active

power

gener-ation

in

the

ex-ante

energy

market

and

an

augmented

optimal

power

?ow

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

bene?ts

of

the

double-sided

auction

are

assessed

from

technical

and

economic

per-spectives.標題目錄CONTENTS01容量市場的作用及問題P

A

R

T02保障靈活調節(jié)資源充裕性的容量市場出清模型P

A

R

T03

保障靈活調節(jié)資源充裕性的容量市場定價與結算機制04

保障靈活調節(jié)資源充裕性的容量市場仿真測算P

A

R

T05結論與展望P

A

R

T29/30Part5

結論與展望l

針對新型電力系統(tǒng)發(fā)展下靈活調節(jié)資源稀缺性逐漸凸顯的問題，提出了保障靈活調節(jié)資源充裕性的容量市場機制總結l

采用不確定性定價方法給出了靈活調節(jié)容量電價l

所提機制有效保障了系統(tǒng)靈活調節(jié)資源的充裕性l

進一步應考慮新型電力系統(tǒng)其他維度的充裕性需求，并與現(xiàn)貨電能量與輔助服務市場做好有效銜接30/30謝謝！請批評指正！西安交通大學電氣工程學院肖云鵬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

signi?cant

grid

?exibility

andeconomic

bene?ts,

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

ef?cient

distributed

algorithmis

developed

to

solve

the

optimal

power

?ow

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

ef?ciency

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

?ex-ible

operation

of

the

system,

the

existing

research

about

mech-anism

design

for

prosumers

can

be

classi?ed

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-pro?t

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,

con?icting

inter-ests

of

prosumers

are

characterized.

The

key

point

here

is

tomodel

the

decision-making

processes

of

prosumers

and

?nd

theNash

equilibrium

so

that

each

prosumer

maximizes

their

prof-its

while

ensuring

the

system

supply

and

demand

balance.

Ref.[9]

systematically

clari?es

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

suf?cien