Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Locational Scenario-based Pricing in a Bilateral Distribution Energy Market under Uncertainty

Hien Thanh Doan, Minsoo Kim, Keunju Song, Hongseok Kim

Abstract

In recent years, there has been a significant focus on advancing the next generation of power systems. Despite these efforts, persistent challenges revolve around addressing the operational impact of uncertainty on predicted data, especially concerning economic dispatch and optimal power flow. To tackle these challenges, we introduce a stochastic day-ahead scheduling approach for a community. This method involves iterative improvements in economic dispatch and optimal power flow, aiming to minimize operational costs by incorporating quantile forecasting. Then, we present a real-time market and payment problem to handle optimization in real-time decision-making and payment calculation. We assess the effectiveness of our proposed method against benchmark results and conduct a test using data from 50 real households to demonstrate its practicality. Furthermore, we compare our method with existing studies in the field across two different seasons of the year. In the summer season, our method decreases optimality gap by 60% compared to the baseline, and in the winter season, it reduces optimality gap by 67%. Moreover, our proposed method mitigates the congestion of distribution network by 16.7\% within a day caused by uncertain energy, which is a crucial aspect for implementing energy markets in the real world.

Locational Scenario-based Pricing in a Bilateral Distribution Energy Market under Uncertainty

Abstract

In recent years, there has been a significant focus on advancing the next generation of power systems. Despite these efforts, persistent challenges revolve around addressing the operational impact of uncertainty on predicted data, especially concerning economic dispatch and optimal power flow. To tackle these challenges, we introduce a stochastic day-ahead scheduling approach for a community. This method involves iterative improvements in economic dispatch and optimal power flow, aiming to minimize operational costs by incorporating quantile forecasting. Then, we present a real-time market and payment problem to handle optimization in real-time decision-making and payment calculation. We assess the effectiveness of our proposed method against benchmark results and conduct a test using data from 50 real households to demonstrate its practicality. Furthermore, we compare our method with existing studies in the field across two different seasons of the year. In the summer season, our method decreases optimality gap by 60% compared to the baseline, and in the winter season, it reduces optimality gap by 67%. Moreover, our proposed method mitigates the congestion of distribution network by 16.7\% within a day caused by uncertain energy, which is a crucial aspect for implementing energy markets in the real world.
Paper Structure (23 sections, 59 equations, 11 figures, 4 tables, 1 algorithm)

This paper contains 23 sections, 59 equations, 11 figures, 4 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model
  3. Structure of energy market
  4. Energy community model
  5. Energy storage systems model
  6. Peer-to-peer trades
  7. Energy balance model
  8. Distributed network model
  9. Incorporation energy model
  10. Stochastic optimization and day-ahead market
  11. Deep learning based scenario generation
  12. Stochastic P2P optimization problem
  13. Distributed energy exchange costs problem
  14. Coupling variables and algorithm process
  15. Energy market clearing problem
  16. ...and 8 more sections

Figures (11)

  • Figure 1: A sequential market framework between community owner (CO) and distribution system operator (DSO), illustrates from left to right the day-ahead, real-time, and payment problem.
  • Figure 2: Distributed network test system
  • Figure 3: Example of quantile-based demand/PV scenario for a household. The quantile forecasting algorithm generates the median value (red) and each quantile value (orange). The true demand/PV energy is illustrated by the black line.
  • Figure 4: Electricity price of main grid during weekdays (top) and weekend (bottom).
  • Figure 5: Energy scheduling of household indexed 10 (positive values are for PV generation, injection into the grid and battery discharging).
  • ...and 6 more figures