Generation cost allocation-a methodology based on optimal power flow and cooperative game theory

2002 ◽  
Author(s):  
A.D.R. Medeiros ◽  
R. Salgado ◽  
H.H. Zurn
Keyword(s):  
Game Theory ◽  
Cooperative Game ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cooperative Game Theory ◽  
Cost Allocation ◽  
Optimal Power ◽  
Generation Cost

scholarly journals Multi-objective optimal power flow in the presence of intermittent renewable energy sources

2018 ◽  
Vol 7 (4) ◽  
pp. 2766 ◽  
Author(s):  
S. Surender Reddy
Keyword(s):  
Wind Power ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Cost Minimization ◽  
Renewable Energy Sources ◽  
Thermal Power ◽  
Wind Farms ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost

This paper solves a multi-objective optimal power flow (MO-OPF) problem in a wind-thermal power system. Here, the power output from the wind energy generator (WEG) is considered as the schedulable, therefore the wind power penetration limits can be determined by the system operator. The stochastic behavior of wind power and wind speed is modeled using the Weibull probability density function. In this paper, three objective functions i.e., total generation cost, transmission losses and voltage stability enhancement index are selected. The total generation cost minimization function includes the cost of power produced by the thermal and WEGs, costs due to over-estimation and the under-estimation of available wind power. Here, the MO-OPF problems are solved using the multi-objective glowworm swarm optimiza-tion (MO-GSO) algorithm. The proposed optimization problem is solved on a modified IEEE 30 bus system with two wind farms located at two different buses in the system.  

Optimal Power Flow Based Economic Generation Scheduling in Day-ahead Power Market

2017 ◽  
Vol 6 (3) ◽  
pp. 124
Author(s):  
Kshitij Choudhary ◽  
Rahul Kumar ◽  
Dheeresh Upadhyay ◽  
Brijesh Singh
Keyword(s):  
Power System ◽  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Congestion Management ◽  
Test Results ◽  
Loss Minimization ◽  
Optimal Power ◽  
Generation Scheduling ◽  
Generation Cost

The present work deals with the economic rescheduling of the generation in an hour-ahead electricity market. The schedules of various generators in a power system have been optimizing according to active power demand bids by various load buses. In this work, various aspects of power system such as congestion management, voltage stabilization and loss minimization have also taken into consideration for the achievement of the goal. The interior point (IP) based optimal power flow (OPF) methodology has been used to obtain the optimal generation schedule for economic system operation. The IP based OPF methodology has been tested on a modified IEEE-30 bus system. The obtained test results shows that not only the generation cost is reduced also the performance of power system has been improved using proposed methodology.

Security Enhanced OPF with Transmission Cost Solution under Bilateral and Multilateral Transactions

2014 ◽  
Vol 573 ◽  
pp. 734-740
Author(s):  
J. Bastin Solai Nazaran ◽  
K. Selvi
Keyword(s):  
Electricity Market ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Operating Conditions ◽  
Human Group ◽  
Transmission Cost ◽  
Intelligent Technique ◽  
Optimal Power ◽  
Deregulated Electricity Market ◽  
Generation Cost

In a deregulated electricity market, it is important to dispatch the generation in an economical manner. While dispatching it is also important to ensure security under different operating conditions. In this study intelligent technique based solution for optimal power flow is attempted. Transmission cost is calculated using Bialek’s upstream tracing method. Generation cost, transmission costs are combined together for pre and post contingency periods to form objective function. Different bilateral and multilateral conditions are considered for analysis. A human group optimization algorithm is used to find the solution of the problem. IEEE 30 bus system is taken as test systems.

scholarly journals Optimal Power Flow Considering Intermittent Solar and Wind Generation using Multi-Operator Differential Evolution Algorithm

2021 ◽  
Author(s):  
Md Alamgir Hossain ◽  
Karam M. Sallam ◽  
Seham S. Elsayed ◽  
Ripon K. Chakrabortty ◽  
Michael J. Ryan
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Differential Evolution Algorithm ◽  
Adaptive Method ◽  
Optimal Power ◽  
Generation Cost ◽  
Algorithmic Framework ◽  
Evolution Algorithm

In this paper, a multi-operator differential evolution algorithm (MODE) is proposed to solve the Optimal Power Flow (OPF) problem and is called MODE-OPF. The MODE-OPF utilizes the strengths of more than one differential evolution (DE) operator in a single algorithmic framework. Additionally, an adaptive method (AM) is proposed to update the number of solutions evolved by each DE operator based on both the diversity of population and quality of solutions. This adaptive method has the ability to maintain diversity at the early stages of the optimization process and boost convergence at the later ones. The performance of the proposed MODE-OPF is tested by solving OPF problems for both small and large IEEE bus systems (i.e., IEEE-30 and IEEE-118) while considering the intermittent solar and wind power generation. To prove the suitability of this proposed algorithm, its performance has been compared against several state-of-the-art optimization algorithms, where MODE-OPF outperforms other algorithms in all experimental results and thereby improving a network's performance with lower cost. MODE-OPF decreases the total generation cost up to 24.08%, the real power loss up to 6.80% and the total generation cost with emission up to 8.56%.

A Probabilistic Optimal Power Flow in Wind-Thermal Coordination Considering Intermittency of the Wind

2021 ◽  
Vol 10 (1) ◽  
pp. 82-110
Author(s):  
Sriparna Banerjee ◽  
Dhiman Banerjee ◽  
Provas Kumar Roy ◽  
Pradip Kumar Saha ◽  
Goutam Kumar Panda
Keyword(s):  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Optimization Techniques ◽  
Wind Energy Conversion ◽  
Optimal Power ◽  
Generation Cost ◽  
Bus Voltage ◽  
Doubly Fed ◽  
Probabilistic Nature

This article specifically aims to prove the superiority of the proposed moth swarm algorithm (MSA) in view of wind-thermal coordination. In the present article, a probabilistic optimal power flow (POPF) problem is formulated to reflect the probabilistic nature of wind. Modelling of doubly fed induction generator (DFIG) is included in the proposed POPF to represent the wind energy conversion system (WECS). To reduce DFIG imposed deviation of bus voltage ancillary reactive power support is considered. Moreover, three different optimization techniques, namely, MSA, biogeography-based optimization (BBO), and particle swarm optimization (PSO) are independently applied for the minimization of active power generation cost for wind-thermal coordination, considering different instances in case of IEEE 30-bus and IEEE 118-bus system. From the simulation results, it is confirmed and validated that the proposed MSA performs considerably better than BBO and PSO.

scholarly journals State-Aware Stochastic Optimal Power Flow

2021 ◽  
Vol 13 (14) ◽  
pp. 7577
Author(s):  
Parikshit Pareek ◽  
Hung D. Nguyen
Keyword(s):  
Real Time ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Variable Load ◽  
Point Change ◽  
Optimal Power ◽  
Time Operation ◽  
Generation Cost ◽  
Real Time Operation ◽  
Bus System

The increase in distributed generation (DG) and variable load mandates system operators to perform decision-making considering uncertainties. This paper introduces a novel state-aware stochastic optimal power flow (SA-SOPF) problem formulation. The proposed SA-SOPF has objective to find a day-ahead base-solution that minimizes the generation cost and expectation of deviations in generation and node voltage set-points during real-time operation. We formulate SA-SOPF for a given affine policy and employ Gaussian process learning to obtain a distributionally robust (DR) affine policy for generation and voltage set-point change in real-time. In simulations, the GP-based affine policy has shown distributional robustness over three different uncertainty distributions for IEEE 14-bus system. The results also depict that the proposed SA-OPF formulation can reduce the expectation in voltage and generation deviation more than 60% in real-time operation with an additional day-ahead scheduling cost of 4.68% only for 14-bus system. For, in a 30-bus system, the reduction in generation and voltage deviation, the expectation is achieved to be greater than 90% for 1.195% extra generation cost. These results are strong indicators of possibility of achieving the day-ahead solution which lead to lower real-time deviation with minimal cost increase.

A Solution to Multi Objective Optimal Power Flow Using Hybrid Cultural-Based Differential Evolution

2013 ◽  
Vol 457-458 ◽  
pp. 1236-1240
Author(s):  
Isaree Srikun ◽  
Lakkana Ruekkasaem ◽  
Pasura Aungkulanon
Keyword(s):  
Differential Evolution ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Test System ◽  
System Operation ◽  
Power System Operation ◽  
Multi Objective ◽  
Optimal Power ◽  
Generation Cost ◽  
And Control

This paper presents a hybrid Cultural-based Differential Evolution for solving a multi-objective Optimal Power Flow (OPF) in support of power system operation and control . The multi-objective OPF was formulated for tackling with total generation cost and environmental impacts simultaneously. The proposed method was applied to the standard IEEE 30-bus test system. The results show that solving the multi-objective OPF problem by the Cultural-based Differential Evolution is more effective than other swarm intelligence methods in the literature.

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