A new multi-objective solution approach to solve transmission congestion management problem of energy markets

Self Cite

Summary This paper presents a stochastic multiobjective model for generation maintenance scheduling (GMS) problem and a solution method to solve it. The proposed model properly considers both competing objectives and uncertainty sources of GMS problem. Three competing objective functions including total cost, risk measure, and total emission are simultaneously minimized in the proposed model. Moreover, forced outages of generating units throughout the GMS horizon are characterized by externally generated scenarios. Stochastic programming as an efficient approach to model uncertainty sources has been used in the proposed stochastic multiobjective GMS. Augmented normalized normal constraint (A‐NNC) method is developed as an efficient multiobjective mathematical programming approach to obtain Pareto optimal solutions for the proposed model. Further, a stochastic decision maker based on out‐of‐sample analysis is suggested to find the most preferred solution for GMS problem. The IEEE 118‐bus test system is used to investigate the effectiveness of the proposed model and solution method.

Section: The Proposed A-nnc Methodsmentioning

confidence: 99%

Section: The Proposed Stochastic Decision Makermentioning

confidence: 99%

“…Since this problem is deterministic, the most preferred solution can be singled out by a deterministic DM. To do this, the preference of the Pareto solution

S_{l}^{r}, l = 1, ⋯, L

with objective functions

{\overset{F}{true}}_{k}^{S_{l}^{r}}, k = 1, ⋯, N

is calculated as follows:

double-struckP F^{S_{l}^{r}} = true \sum_{k = 1}^{N} I C_{k} true(1 - {\overset{F}{true}}_{k}^{S_{l}^{r}} true) .

The Pareto point with the highest value of preference

double-struckP F^{S_{l}^{r}}

is selected as the most preferred solution for the scenario r whose objective functions are denoted by

{\overset{F}{true}}_{k}^{r}, k = 1, ⋯, N

{\overset{F}{true}}_{k}^{A, j}

, is calculated using a probabilistic average of

{\overset{F}{true}}_{k}^{r}

results:

{\overset{F}{true}}_{k}^{A, j} = \frac{\sum_{r = 1}^{N_{R}} P_{r} \times {\overset{F}{true}}_{k}^{r}}{\sum_{r = 1}^{N_{R}}}

…”

Section: The Proposed Stochastic Decision Makermentioning

confidence: 99%

“…Since this problem is deterministic, the most preferred solution can be singled out by a deterministic DM. To do this, the preference of the Pareto solution

S_{l}^{r}, l = 1, ⋯, L

with objective functions

{\overset{F}{true}}_{k}^{S_{l}^{r}}, k = 1, ⋯, N

is calculated as follows:

double-struckP F^{S_{l}^{r}} = true \sum_{k = 1}^{N} I C_{k} true(1 - {\overset{F}{true}}_{k}^{S_{l}^{r}} true) .

The Pareto point with the highest value of preference

double-struckP F^{S_{l}^{r}}

is selected as the most preferred solution for the scenario r whose objective functions are denoted by

{\overset{F}{true}}_{k}^{r}, k = 1, ⋯, N

.…”

Section: The Proposed Stochastic Decision Makermentioning

confidence: 99%

Section: The Proposed Stochastic Decision Makermentioning

confidence: 99%

See 3 more Smart Citations

Stochastic multiobjective generation maintenance scheduling using augmented normalized normal constraint method and stochastic decision maker

Bagheri

Amjady

2018

Self Cite

Multi‐objective squirrel search algorithm to solve economic environmental power dispatch problems

Sakthivel¹,

Murugesan

Sathya

2020

Major challenge in the modern power system assessment is to address the issue of Combined Economic and Environmental Power Dispatch (CEEPD). The prime intention of CEEPD is to mitigate the total expense in the power generation process considering the environmental collision caused owing to the discharge of gaseous pollutants of fossil energy. This paper bestows a new swarm intelligence technique, Squirrel Search Algorithm (SSA) to deal with multiobjective CEEPD problem in power systems. The SSA is devised from the foraging behavior of squirrels which is based on the dynamic jumping and gliding strategies. Multi-Objective SSA (MOSSA) approach employs the Pareto dominance and crowding distance concepts for finding the Pareto front solutions set. An external elitist depository mechanism is employed to preserve Pareto front solutions acquired during the optimization process. Then, an optimality based fuzzy decision maker is used to decide the best compromise solution. Furthermore, a renovate strategy and selection rules are utilized in the MOSSA approach to appropriately handle the CEEPD constraints. To access the practicability and effectiveness of the proposed MOSSA algorithm, it has been applied for 6, 10, and 40 units' power systems and compared with those of List of symbols and abbreviations: a i , b i , c i , cost coefficients of generator i; a ij , b ij , c ij , e ij and f ij , cost coefficients of the unit i for fuel type j; B ij , line loss coefficients; C D , drag coefficient; C L , lift coefficient; d i , e i , cost coefficients of the VPL effect of generator i; D, drag force; ed i , euclidean distance between nondominated solution and the nearest Pareto front solution in objective space; d, mean value of ed i ; d g , gliding distance; E i , emission of the generator i; F i , total fuel cost of the generators; F max j and F min j , maximum and minimum values of jth objective function respectively; F(P i)F(P i) max , cost function of the worst feasible solution in the population; F bcs and E bcs , fuel cost and emission attained by CEEPD; F min and E max , fuel cost and emission attained by ELD minimization respectively; F max and E min , fuel cost and emission attained by emission minimization respectively; G c , gliding constant; h g , gliding height; i and j, indices of unit and fuel type, respectively; k, index of prohibited zone; L, lift force; M, number of nondominated solutions; n, number of solutions in the nondominated set; nf, number of fuel types for each unit; ng, total number of generating units; nz, total number of POZs; P D , power demand; P dp , predator presence probability; P i , P 0 i , current and previous power output of ith unit respectively; P ij, min and P ij, max , minimum and maximum power output of unit i with fuel option j, respectively; P L i,k , P U i,k , lower and upper power outputs of the kth prohibited zone of the ith generator, respectively; Pi, max i, min , minimum and maximum generation of unit i; P L , transmission losses; r 1 , r 2 and r 3 , random numbers in the range...

Social welfare maximization for congestion management in multiutility market using improved PSO incorporating transmission loss cost allocation

Singh¹,

Mahanty

Singh

2018

Summary The objective of the present work is to investigate a decentralized optimal power flow decision‐based congestion‐free operational strategy in multiutility power markets that maximizes the social welfare. The effects of network losses incurred by various utilities on social welfare of the market have also been investigated. As a result, it can resolve the market disputes due to the problem of congestion and allocation of the additional cost of generation caused by transmission losses. The centralized optimal power flow decision‐based strategy has also been adopted to address the problem. In this work, a new optimization method, interior point–initialized particle swarm optimization (IP‐PSO) method, has been introduced to solve the mentioned problem. The method harnesses the benefits of both interior point method (IPM) and particle swarm optimization (PSO) method. To facilitate the faster optimal solution, results obtained using IPM has been used to initialize PSO solution. The proposed study has been performed on the modified IEEE‐30 bus and modified IEEE‐118 bus systems. The results show the effectiveness of decentralized optimal power flow decision‐based strategy over centralized optimal power flow decision‐based strategy using IPM, sequential quadratic programming, PSO, IP‐initialized sequential quadratic programming, and IP‐PSO methods.