Journal
APPLIED ENERGY
Volume 298, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2021.117215
Keywords
Energy planning; Electricity market; Multi-microgrid; Renewable energy resources (RERs); Uncertainty
Categories
Funding
- Business Finland through SolarX Research Project, 2019-2021 [6844/31/2018]
- FCT [POCI-01-0145-FEDER-029803 (02/SAICT/2017)]
- FEDER through COMPETE 2020
Ask authors/readers for more resources
This paper introduces a stochastic planning algorithm for the operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The algorithm investigates optimal operation of resources and energy storage by forecasting various uncertainties and using Cournot equilibrium and game theory to model the interactions between the MMG and electricity market. The proposed algorithm is shown to result in above 8% cost reduction in the MMG, highlighting the importance of modeling the interaction between MMG and electricity market under high integration of uncertain RERs.
This paper presents a stochastic planning algorithm to plan an operation of a multi-microgrid (MMG) in an electricity market considering the integration of stochastic renewable energy resources (RERs). The proposed planning algorithm investigates the optimal operation of resources (i.e., wind turbine (WT), fuel cell (FC), Electrolyzer, photovoltaic (PV) panel, and microturbine (MT)) and energy storage (ES). Various uncertainties (e.g., the power production of WT, the power production of PV, the departure time of electric vehicle (EV), the arrival time of EV, and the traveled distance of EV) are initially forecasted according to the observed data. The prediction error is estimated by fitting the forecasted data and observed data using a Copula method. A Cournot equilibrium and game theory (GT) are applied to model the real-time electricity market and its interactions with the MMG. The proposed algorithm is examined in a sample MMG to determine the operation of uncertain resources and ES. The obtained results are compared with a baseline and the other conventional optimization methods to verify the effectiveness of the proposed algorithm. The obtained results authenticate the importance of modeling the interaction between the MMG and electricity market, especially under the high integration of uncertain RERs, resulting in above 8% cost reduction in the MMG.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available