Offering Decision of Risk-Based Wind-Photovoltaic-Thermal GenCo Using Downside Risk Constraints Approach

Peng Liu,Jinfeng Wang,Sayyad Nojavan,Kittisak Jermsittiparsert

doi:10.1109/access.2020.3006261

Abstract

Risk analysis and scheduling of a Generation Company (GenCo) under uncertain environments are challenging issues. So, stochastic optimization and downside risk constraints approaches are used in this paper to model and manage the risk associated with various uncertainties. The presented GenCo model comprised five thermal units, photovoltaic systems, and wind farms. It is assumed that all thermal units, photovoltaic systems, and wind farms can participate in the energy market. In contrast, only thermal units can participate in the reserve market. The uncertainty of electricity and reserve market prices and output power of photovoltaic systems and wind farms are modeled via a stochastic optimization approach. Afterward, the downside risk constraints method is used to manage the risks associated with various uncertainties. By analyzing the obtained results, it can be seen that the level of the average risk can plunge to 0 by gaining 4.68% less average profit. So, the GenCo can be immune against considered uncertainties with gaining a little bit less profit. Furthermore, the offering strategy is studied in two risk-neutral and risk-averse strategies. Finally, the CPLEX solver of GAMS software is used to optimize the studied linear-based model of GenCo.

Highlights

Nowadays, different kinds of approaches such as the stochastic optimization approach [1], conditional-value-atrisk approach [2], [3], chance-constraint approach [4], information gap decision theory [5], [6], robust optimization approach [7], [8], interval optimization approach, and twopoint estimate approach are used to handle the risk associated with various uncertain parameters
It should be noted that the scenario-based stochastic optimization approach is used in this paper to model the uncertain natures of the energy and reserve market prices, the output power of wind farms, and photovoltaic systems
Particle Swarm Optimization approach has been used in [39] to maximize the profit of a Generation Company (GenCo) that comprised of CHP units, renewable-based units such as wind and photovoltaic systems under the wind speed, solar irradiation and market price uncertainties

Summary

INTRODUCTION

Different kinds of approaches such as the stochastic optimization approach [1], conditional-value-atrisk approach [2], [3], chance-constraint approach [4], information gap decision theory [5], [6], robust optimization approach [7], [8], interval optimization approach, and twopoint estimate approach are used to handle the risk associated with various uncertain parameters. To maximize the expected profit and emitted emission in a GenCo under uncertain environment, a multi-objective model comprising thermal units, wind farms, and large scale battery storage systems has been provided and studied in [35]. A heuristic-based algorithm has been provided in [38] to maximize the profit and minimize the emission in a GenCo that comprised of renewable wind units, pumped storage, and thermal power generation units under the wind speed uncertainty. Particle Swarm Optimization approach has been used in [39] to maximize the profit of a GenCo that comprised of CHP units, renewable-based units such as wind and photovoltaic systems under the wind speed, solar irradiation and market price uncertainties. The mixture of scenario-based stochastic optimization method and downside risk constraints approach has been used in this paper to model and manage the risk associated with various considered uncertainties. Analyzing the effects of various uncertainties on the offering decisions of the GenCo

PROBLEM FORMULATION

DOWNSIDE RISK CONSTRAINTS

Findings

CONCLUSION