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Bi-level decision making in techno-economic planning and probabilistic analysis of community based sector-coupled energy system

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Abstract

The paper proposes a bi-level programming model for community participation in techno-economic planning of an integrated energy system. Distribution system operator/System planner considered as a upper level decision maker anticipates the optimal operational response of community operator by solving lower level problem to make financial decisions while planning a sector coupled integrated energy system.To establish reliable, scalable, robust and data-driven decision making tool in techno-economic planning, a smart framework is proposed to quantify the effects of solar power uncertainties, assess hourly electric vehicle (EV) charging profile, analyze probabilistic reliability and obtain optimal financial decision by solving bi-level programming model using four meta-heuristics algorithms. During the preliminary stage, the potential effects of seasonal and climatic variations on solar radiation are quantified through nine distinct probabilistic classifiers trained using support vector classification approach. It serves as a data driven scenario generation tool for robust exploration of bi-level decision making process. Additionally, the impact of EV arrivals in a limited server charging facility is studied using queueing theory to estimate the mean hourly EV charging demand. Finally, the optimal settings of particle swarm optimisation (PSO), genetic algorithm (GA), red deer algorithm (RDA) and evolve class topper optimization (E-CTO) are identified through Taguchi’s design of experiments. Based on these optimal settings, the proposed bi-level model is solved to obtain the best investment decision for planning a community based energy system. The results suggest that E-CTO shows a better response in terms of computational speed, convergence rate, accuracy and reliability. A minimum value of Rs 4.18 per kWh is obtained as the cost of energy using E-CTO. Numerical investigations were studied on a two area consumer centric energy system connected to the local grid to show the effectiveness of the proposed bi-level approach and meta-heuristic algorithms.

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Data Availability

Hourly solar radiation dataset analysed during the current study are not publicly available but are available from the corresponding author on reasonable request. EV arrival and load consumption datasets are publicly available and eventually Cited in the references.

Abbreviations

WP:

Wind power

HV:

Hybrid vehicle

RW:

Roulette wheel

COE:

Cost of energy

BG:

Biogas

Bat:

Battery

RF:

Renewable fraction

SMC:

Sequential Monte Carlo

GE:

Gas Ex-changer

HHChome:

Health care

ACO:

Ant colony optimization

D i − G e n :

Diesel generation

TNPC:

Total net present cost

C L O E E :

Cost of loss of expected energy

FPA:

Flower pollination algorithm

IFPA:

Intelligent flower pollination algorithm

HSFC:

Hydrogen storage fuel cell

C L O L E :

Cost of loss of load expected

GWO:

Grey wolf optimization

MPOE:

minimum power outage probability

DRO:

Distribution-ally robust optimization

DA:

Day ahead

LCOE:

Levelized cost of energy

NSMO-PSO:

Non dominated sorting multi objective particle swarm optimization

NSGA-II:

Non dominated sorting Genetic Algorithm

EC:

Electric chillers

HWSA:

hybrid of water wave and salp swarm algorithm

AMSEO:

Adaptive memory social engineering optimizer

PESR:

Primary energy saving ratio

EMR:

Energy matching ratio

SPECSR:

Specific ex-cargo environmental cost savings

GA-SA-EX:

genetic algorithm-simulated annealing explained

HSIA:

Hybrid of salp swarm algorithm and imperialistic algorithm

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  1. Electrical Engineering, National Institute of Technology, Jamshedpur, Jharkhand, 831015, India

    Nishant Kumar, Kumari Namrata & Akshit Samadhiya

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Nishant Kumar and Kumari Namrata contributed equally to this work.

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Kumar, N., Namrata, K. & Samadhiya, A. Bi-level decision making in techno-economic planning and probabilistic analysis of community based sector-coupled energy system. Appl Intell 53, 6604–6628 (2023). https://doi.org/10.1007/s10489-022-03794-9

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