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Operation of Energy and Regulation Reserve Markets in the presence of Virtual Power Plant Including Storage System and Distributed Generation based on Unit Commitment Model

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Abstract

The operation model of a virtual power plant (VPP) that includes synchronous distributed generating units, combined heat and power unit, renewable sources, small pumped and thermal storage elements, and electric vehicles is described in the present research. The VPPs are involved in the day-ahead energy and regulation reserve market so that escalate their profit by selling their generated power. This is formulated as an optimization problem with maximization of VPPs profit subject to constraints of the VPP’s sources and storage, and VPP flexibility limits. Also, uncertain nature of renewables output power, prices of the markets, electric vehicles parameters, and reserve parameters are taken into account. Then, uncertain parameters are properly modeled by incorporating the Unscented transformation (UT)-based stochastic optimization, and GAMS software is adopted to test the efficacy of the presented approach so that the VPP’s operation and economic situation is suitably improved.

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Abbreviations

l :

Piecewise linearization index

h :

Hour index

v :

Electric vehicle (EV) index

w :

Scenario

\(w{\prime}\) :

Scenario

φ l :

The set of linearization pieces

φ h :

The set of hours

φ v :

The set of EVs

φ w :

The set of scenario

I SDG :

The variable of the presence of SDG

J SDG :

The variable of startup of SDG

L SDG :

The variable of the shutdown of SDG

I PSP :

The variable of the status of SPS

I ch :

The variable of charging status of EVs

I dch :

The variable of discharging status of EVs

I ST :

The variable of the status of TS

R :

Revenue in $

C :

Cost in $

P SDG :

Power of SDG in per-unit (p.u.)

RR SDG :

Regulation reserve of SDG in p.u.

E PSP :

Energy of SPS in p.u.

P pum :

Charging power of SPS in p.u.

P tur :

Discharging power of SPS in p.u.

RR PSP :

Regulation reserve of SPS in p.u.

E EV :

Energy of EV in p.u.

P ch :

Charging power of EV in p.u.

P dch :

Discharging power of EV in p.u.

RR EV :

Regulation reserve of EV in p.u.

P CHP,e :

Electrical power of CHP in p.u.

P CHP,t :

Thermal power of CHP in p.u.

E ST :

Energy of TS in p.u.

P ch,t :

Charging power of TS in p.u.

P dch,t :

Discharging power of TS in p.u.

P DA,e :

Electrical power of day-ahead market in p.u.

P DA,t :

Thermal power of day-ahead market in p.u.

RR DA :

Regulation reserve of day-ahead market in p.u

a, b, c :

Cost coefficients of SDG in $/h, $/MWh, $/MWh2, respectively

SUC :

Startup cost in $

SDC :

Shutdown cost in $

CO2 SDG :

Carbon dioxide pollution of SDG in kg

CO2 cap :

Allowable carbon dioxide pollution in kg

λ co2 :

Penalty cost of carbon dioxide pollution in $/kg

P max :

Maximum power in p.u.

P min :

Minimum power in p.u.

RDN p :

Ramp up rate in p.u.

RUP p :

Ramp down rate in p.u.

H on :

Maximum switched on time of SDG in hour

H off :

Minimum shutdown time of SDG in hour

λ pum :

The charging price of the SPS in $/MWh

η :

Efficiency in %

E max :

Maximum stored energy in p.u.

E min :

Minimum stored energy in p.u.

λ CHP :

Power price of CHP in $/MWh

K chp :

Coefficient of the ratio between thermal and electrical sectors in CHP

λ ST :

The price of power of TS in $/MWh

λ E :

The price of electric energy in the day-ahead market in $/MWh

λ T :

The price of thermal energy in the day-ahead market in $/MWh

λ spot :

Spot market price $/MWh

π :

Probability of scenario

Δ F :

Flexibility tolerance in p.u

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  1. SPIC Integrated Smart Energy Technology Co., Ltd, Beijing, Beijing, 100080, China

    Jian Zhu, Zhiyuan Zhao, Xiaoqiang Yao, Huan Wang, Canzhang Chen & Jun Zhou

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Zhu, J., Zhao, Z., Yao, X. et al. Operation of Energy and Regulation Reserve Markets in the presence of Virtual Power Plant Including Storage System and Distributed Generation based on Unit Commitment Model. J. Electr. Eng. Technol. 19, 2159–2179 (2024). https://doi.org/10.1007/s42835-023-01732-4

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