Exploiting Flexibility of Renewable Energy Integrated Buildings for Optimal Day-ahead and Real-time Power Bidding Considering Batteries and EVs as Demand Response Resources

被引:1
作者
Eseye, Abinet Tesfaye [1 ]
Lehtonen, Matti [1 ]
Tukia, Toni [1 ]
Uimonen, Semen [1 ]
Millar, R. John [1 ]
机构
[1] Aalto Univ, Dept Elect Engn & Automat, Espoo, Finland
来源
2019 16TH INTERNATIONAL CONFERENCE ON THE EUROPEAN ENERGY MARKET (EEM) | 2019年
关键词
Building microgrid; energy flexibility; electricity market; optimization; demand response; battery; EV; renewable energy; ELECTRICITY; GENERATION; STORAGE; MODEL;
D O I
10.1109/eem.2019.8916277
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This study examines the flexibility potential of energy demand resources in buildings. The building flexible demand resources considered are electric vehicles (EVs) and energy storage batteries. The paper investigates the combined optimization of EVs and batteries with the objective of maximizing the total profit of building microgrids in day-ahead and regulation (real-time) electricity markets. The major contribution of the paper is the exploitation of the energy flexibility of buildings using EVs as dynamic energy storage device and batteries as manageable demand facility with possibilities of advancing or delaying their consumptions. The proposed optimization objective takes into account EV driving patterns, penalties for renewable energy curtailment, involuntary load shedding and bid imbalance in an explicit optimization setup. The proposed optimization problem is formulated as a dual-step mixed-integer linear programming (MILP) problem, and solved using the CPLEX solver. A number of simulation results are provided to demonstrate the effectiveness of the proposed optimization framework using real data of building electricity consumption and local renewable energy production in the Otaniemi area of Espoo, Finland. We reveal that the devised optimization solution achieves considerable saving in electricity bills, increase profit, reduce renewable energy curtailment, and smoothen peak electricity consumption, compared to a non-optimized operation.
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页数:6
相关论文
共 18 条
[1]   Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method [J].
Amjady, Nima ;
Keynia, Farshid .
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, 2008, 30 (09) :533-546
[2]   The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid [J].
Clement-Nyns, Kristien ;
Haesen, Edwin ;
Driesen, Johan .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2010, 25 (01) :371-380
[3]   ESTIA - A REAL-TIME CONSUMER CONTROL SCHEME FOR SPACE CONDITIONING USAGE UNDER SPOT ELECTRICITY PRICING [J].
CONSTANTOPOULOS, P ;
SCHWEPPE, FC ;
LARSON, RC .
COMPUTERS & OPERATIONS RESEARCH, 1991, 18 (08) :751-765
[4]  
Eseye A.T., 2016, IEEE ICPRE
[5]  
Eseye A.T., 2017, IEEE GREENTECH
[6]  
Eseye A.T., 2017, IEEE ICCCBDA
[7]  
Eseye A.T., IEEE INDIN 2019
[8]   Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information [J].
Eseye, Abinet Tesfaye ;
Zhang, Jianhua ;
Zheng, Dehua .
RENEWABLE ENERGY, 2018, 118 :357-367
[9]   Stochastic joint optimization of wind generation and pumped-storage units in an electricity market [J].
Garcia-Gonzalez, Javier ;
Ruiz de la Muela, Rocio Moraga ;
Matres Santos, Luz ;
Mateo Gonzalez, Alicia .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2008, 23 (02) :460-468
[10]   Optimizing Electric Vehicle Charging With Energy Storage in the Electricity Market [J].
Jin, Chenrui ;
Tang, Jian ;
Ghosh, Prasanta .
IEEE TRANSACTIONS ON SMART GRID, 2013, 4 (01) :311-320