TY - JOUR
T1 - Real-Time Peak Valley Pricing Based Multi-Objective Optimal Scheduling of a Virtual Power Plant Considering Renewable Resources
AU - Pandey, Anubhav Kumar
AU - Jadoun, Vinay Kumar
AU - Sabhahit, Jayalakshmi N.
N1 - Funding Information:
This research is funded by DST sponsored INSPIRE Fellowship, Govt. of India, fellowship number [INSPIRE Code: IF 190938] and “The APC is funded by MANIPAL ACADEMY OF HIGHER EDUCATION (MAHE) MANIPAL”.
Funding Information:
The first author positively acknowledges the funding organization: DST sponsored INSPIRE Fellowship, Govt. of India, [INSPIRE Code: IF 190938] in carrying out the research work with their incessant funding. The authors also acknowledge gratefulness to the host University Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India for providing a conducive research atmosphere to carry out the envisioned research work.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.
AB - In the era of aiming toward reaching a sustainable ecosystem, the primary focus is to curb the emissions generated by non-conventional resources. One way to achieve this goal is to find an alternative to traditional power plants (TPP) by integrating various distributed energy resources (DERs) via a Virtual Power Plant (VPP) in modern power systems. Apart from reducing emissions, a VPP enhances the monetary benefits to all its participants, including the DER owners, participants, and utility personnel. In this paper, the multi-objective optimal scheduling of the VPP problem considering multiple renewable energy resources has been solved using the multi-objective black widow optimization (MOBWO) algorithm. Renewable resources consist of solar PV modules, wind turbines, fuel cells, electric loads, heat-only units, and CHP units. The weighting factor method was adopted to handle the multi-objective optimal scheduling (MOOS) problem by simultaneously maximizing profit and minimizing emission while satisfying the related constraints. In this research, a peak valley power pricing strategy is introduced and the optimal scheduling of the VPP is attained by performing a multi-objective scheduling strategy (MOSS), which is day-ahead (on an hourly basis) and 15-min based (for a one-day profile), to observe the behavior of the anticipated system with a better constraint handling method. This algorithm is capable of dealing with a complex problem in a reduced computational time, ensuring the attainment of the considered objective functions. The numerical results obtained by the MOBWO algorithm after 100 independent trials were compared with the latest published work showing the effectiveness and suitability of the developed system.
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U2 - 10.3390/en15165970
DO - 10.3390/en15165970
M3 - Article
AN - SCOPUS:85137656781
SN - 1996-1073
VL - 15
JO - Energies
JF - Energies
IS - 16
M1 - 5970
ER -
