개발 기간

2022.03-2023.04

개발 내용

• 산단의 RE100 연계형 등의 에너지사업을 추진
• 가상발전소 플랫폼의 IOE 모니터링 고도화를 위한 ESS 상태감시 솔루션 성능개선을 목적으로 ESS의 HILS 시스템을 개발
• ESS의 배터리 실증운영 데이터 시뮬레이션을 통한 설계 용량의 검증 및 ESS의 HILS 개발 시스템을 이용한 ESS의 최적운전 방법 제공

목표 데이터 정의

• 데이터 예시 배터리 충방전 데이터 구성 싸이클 / Capacity

목표 데이터 정의

• 데이터 예시 배터리 충방전 데이터 구성 싸이클 / Capacity
• Dataset 형식 셀별 온도 , V, A , 측정시간, 싸이클수 (샘플)

[설계]시뮬레이터

[설계]시스템 구조

ESS-based DSM algorithm

Explanation of the Flowchart:

1. Start: Where the decision-making process starts.
2. Is Grid Demand High?: If grid demand is high, the ESS might engage in Peak Shaving to reduce peak demand.
3. Is Price High?: If electricity prices are high, the ESS might engage in Energy Arbitrage by selling stored energy.
4. Is Price Low?: If electricity prices are low, the ESS might engage in Energy Arbitrage by buying and storing energy.
5. Is Renewable Generation High?: If there is a surplus of renewable energy, the ESS might engage in Renewable Energy Integration by storing this energy.
6. Is Grid Frequency Unstable?: If the grid frequency is unstable, the ESS might engage in Frequency Regulation.
7. Is Voltage Unstable?: If the grid voltage is unstable, the ESS might provide Voltage Support.
8. Is Outage Detected?: If an outage is detected, the ESS might switch to Emergency Backup mode to supply power to critical loads.
9. End: Where the decision-making process ends.

Energy Storage Systems (ESS) play a crucial role in modern energy management, helping to balance supply and demand, integrate renewable resources, and improve grid stability and efficiency. The operational strategies for ESS management can be broadly categorized based on the objectives they aim to achieve. Here are some of the common ESS management strategies, along with brief descriptions:

1. Peak Shaving:

• This strategy involves using the ESS to reduce peak demand on the grid. The ESS is charged during low-demand periods and discharged during high-demand periods, effectively "shaving" the peaks off the demand curve.

1. Load Leveling (or Load Shifting):

• Similar to peak shaving, this strategy involves shifting energy consumption from peak hours to off-peak hours. The ESS is charged during off-peak hours and discharged during peak hours to minimize demand charges and take advantage of lower energy prices.

1. Renewable Energy Integration:

• ESS can be used to store excess energy generated from renewable sources (like solar or wind) and release it when there is a demand, thus helping to integrate variable renewable energy into the grid.

1. Frequency Regulation (or Ancillary Services):

• The ESS can be used to provide rapid responses to changes in grid frequency, either by discharging energy to counteract frequency drops or by absorbing energy to counteract frequency rises.

1. Voltage Support and Reactive Power Control:

• ESS can be used to support the voltage level in the grid by providing or absorbing reactive power, thereby maintaining the voltage within desired limits.

1. Emergency Backup (or Uninterruptible Power Supply):

• In this strategy, the ESS is used to provide power during outages, ensuring a continuous power supply for critical loads.

1. Energy Arbitrage:

• This strategy involves buying (and storing) electricity when prices are low and selling (or using) it when prices are high. The goal is to profit from the difference in electricity prices at different times.

1. Grid Congestion Relief:

• The ESS can be used to relieve congestion in transmission and distribution networks by storing energy during times of low congestion and releasing it during times of high congestion.

1. Community Energy Storage (CES):

• In this strategy, ESS units are distributed across a community and operated in a coordinated manner. They can be used for various purposes, such as reducing peak demand, supporting local renewables, and providing resilience during outages.

1. Self-consumption Optimization:

• For prosumers (consumers who also produce energy, e.g., with solar panels), the ESS can be used to maximize the use of self-generated electricity, thereby reducing the need to buy electricity from the grid.

TOU Schedule

The Time-of-Use (TOU) Optimization or Scheduled Dispatch strategy is typically the one that operates based on a predetermined schedule, which could be set according to time of day, day of the week, season of the year, or other specific times when electricity prices or demand are known to vary predictably. This strategy aims to operate the Energy Storage System (ESS) in a way that capitalizes on these known variations.

Here’s a more detailed explanation of the Time-of-Use (TOU) Optimization or Scheduled Dispatch strategy, and how it might be implemented:

Time-of-Use (TOU) Optimization or Scheduled Dispatch Strategy

Objective:

To use the ESS to shift energy consumption from high-cost periods to low-cost periods based on a predefined schedule, which could align with time-of-use (TOU) electricity rates, demand charges, or other predictable patterns.

Example Use Case:

In a region where electricity is more expensive during peak hours (e.g., late afternoon and early evening) and cheaper during off-peak hours (e.g., late night and early morning), this strategy would involve:

1. Charging the ESS during off-peak hours when electricity is cheap.
2. Discharging the ESS during peak hours to avoid drawing expensive electricity from the grid.

Seasonal Adjustments:

The schedule might also vary by season. For example, peak hours might be different in the summer (when air conditioning use is high) compared to the winter (when heating demand might shift the peak to a different time).

How a Human Operator Might Manage This Strategy:

A human operator or energy manager could set a schedule for the ESS based on known TOU rates or other predictable factors. For example, they might program the ESS to:

1. Charge every day from 12 AM to 6 AM (when rates are lowest).
2. Discharge every day from 4 PM to 9 PM (when rates are highest).

This schedule could be set manually at the beginning of a season, based on TOU rates published by the local utility, and adjusted as needed throughout the year.

Here’s a simplified Java class that demonstrates this concept: