scholarly journals Scheduling for Prosumer Microgrid with Considering Price Based Demand Response

2021 ◽  
Vol 2117 (1) ◽  
pp. 012027
Author(s):  
T Suheta ◽  
O Penangsang ◽  
M Ashari ◽  
R Delfianti
Keyword(s):  
Renewable Energy ◽  
Power Unit ◽  
Demand Response ◽  
Firefly Algorithm ◽  
Unit Commitment ◽  
Optimization Problems ◽  
Operating Costs ◽  
Total Cost ◽  
Network Losses ◽  
Cost Differences

Abstract Unit commitment (UC) is the scheduling of power unit operating outages to meet electricity needs at a certain time with the aim of obtaining a total economical generating cost. Differences in the characteristics of each generating unit and limitations result in different scheduling combinations based on equations, and for this research, the renewable energy penetration will also be considered. Firefly Algorithm (FA) is a method to determine load requests and complete renewable energy scheduling power by using unit commitment. FA is a simple but reliable algorithm that solves optimization problems. Firefly Algorithm (FA) method obtained maximum generating power of 81,329 MW with the total cost of 827,556 $ and network losses of 3.6696 Coefficient of operating costs.

Demand Response Management by Rolling Unit Commitment for High Renewable Energy Penetration

2018 ◽  
Author(s):  
Emmanouil A. Bakirtzis ◽  
Ilias G. Marneris ◽  
Stylianos I. Vagropoulos ◽  
Pandelis N. Biskas ◽  
Anastasios G. Bakirtzis
Keyword(s):  
Renewable Energy ◽  
Demand Response ◽  
Unit Commitment

scholarly journals Unit commitment and dispatch with coordination of wind and pumped storage hydro units by using cuckoo search algorithm

2017 ◽  
Vol 7 (1.1) ◽  
pp. 94
Author(s):  
R. Jayashree ◽  
R. Soundarapandian
Keyword(s):  
Wind Power ◽  
Unit Commitment ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Bench Mark ◽  
Method Performance ◽  
Total Cost ◽  
Multi Objective ◽  
Comparison Results ◽  
Pumped Storage

This paper proposes a multi objective model for Advanced Unit Commitment (AUC) with wind power and Pumped Storage (PS) units using Cuckoo Search (CS) algorithm. The novelty of the proposed method is improved levy flight searching ability, random reduction and ability to adapt complex optimization problems. Here, the CS algorithm to accommodate wind output uncertainty, with the multi-objective of providing an optimal AUC schedule for the thermal generators in the day-ahead market that minimizes the total cost under the different wind power output scenario. The proposed method is more reliable for AUC because it considering the wind power uncertainty using the Artificial Neural Network (ANN) and PS units, which are significantly reduces the total cost. Then the proposed method is implemented in the MATLAB/simulink platform and tested under IEEE standard bench mark system. The proposed method performance has been verified through the comparison analysis with the existing techniques. The comparison results were proved the superiority of the proposed method.

scholarly journals Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response

2021 ◽  
Vol 13 (6) ◽  
pp. 3400
Author(s):  
Jia Ning ◽  
Sipeng Hao ◽  
Aidong Zeng ◽  
Bin Chen ◽  
Yi Tang
Keyword(s):  
Renewable Energy ◽  
Real Time ◽  
Demand Response ◽  
System Operation ◽  
Real Time Scheduling ◽  
High Penetration ◽  
Time Scheduling ◽  
Grid Load ◽  
Grid Side ◽  
Scheduling Models

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.

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