Chanwit Boonchuay

Optimal congestion management in an electricity market using particle swarm optimization with time-varying acceleration coefficients

This paper proposes an optimal congestion management approach in a deregulated electricity market using particle swarm optimization with time-varying acceleration coefficients (PSO-TVAC). Initially, the values of generator sensitivity are used to select redispatched generators. PSO-TVAC is used to determine the minimum redispatch cost. Test results on IEEE 30-bus and 118-bus systems indicate that the PSO-TVAC approach could provide a lower rescheduling cost solution compared to classical particle swarm optimization and particle swarm optimization with time-varying inertia weight.

OPTIMAL CONGESTION MANAGEMENT IN ELECTRICITY MARKET USING PARTICLE SWARM OPTIMIZATION WITH TIME VARYING ACCELERATION COEFFICIENTS

This paper proposes an optimal power redispatching approach for congestion management in deregulated electricity market. Generator sensitivity is considered to indicate the redispatched generators. It can reduce the number of participating generators. The power adjustment cost and total redispatched power are minimized by particle swarm optimization with time varying acceleration coefficients (PSO‐TVAC). The IEEE 30‐bus and IEEE 118‐bus systems are used to illustrate the proposed approach. Test results show that the proposed optimization scheme provides the lowest adjustment cost and redispatched power compared to the other schemes. The proposed approach is useful for the system operator to manage the transmission congestion.

Improving Regulation Service Based on Adaptive Load Frequency Control in LMP Energy Market

In a new environment of energy deregulation, the locational marginal price (LMP) basis is overwhelming congestion management issues. But it still requires proper ancillary services to serve the system. Load frequency control (LFC) is needed to maintain the system frequency in the dynamic environment. In this letter, an efficient LFC approach with the critical load level (CLL)-based adaptive participation factor is proposed for an LMP-based market. The CLL is considered when the load varies from the generation set point. The proposed approach could enhance the LFC performance in securely following up the aggressive change of energy demand. The PJM 5-bus system is used to illustrate the proposed LFC.

Optimal Switching Pattern for PWM AC-AC Converters Using Bee Colony Optimization

This paper proposes a harmonic reduction approach for a pulse width modulation (PWM) AC-AC converters using Bee Colony Optimization (BCO). The optimal switching angles are provided by BCO to minimize harmonic distortions. The sequences of the PWM switching angles are considered as a technical constraint. In this paper, simulation results from various optimization techniques including BCO, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are compared. The test results indicate that BCO can provide a better solution than the others in terms of power quality and power factor improvement. Lastly, experiments on a 200W AC-AC converter confirm the performance of the proposed switching pattern in reducing harmonic distortions of the output waveform.

RISK-CONSTRAINED OPTIMAL BIDDING STRATEGY FOR A GENERATION COMPANY USING SELF-ORGANIZING HIERARCHICAL PARTICLE SWARM OPTIMIZATION

This article proposes optimal bidding strategies for a generation company (GenCo) considering risk of profit variation by self-organizing hierarchical particle swarm optimization with time-varying acceleration coefficients (SPSO-TVAC). Based on a trade-off technique, the expected profit maximization and risk minimization are achieved. Nonconvex operating cost functions of thermal generation units and minimum up/down time constraints are cooperated to provide the optimal bid prices in a day-ahead uniform price spot market. The rivals’ bidding behavior is estimated by Monte Carlo simulation. Test results indicate that SPSO-TVAC is superior to inertia weight approach particle swarm optimization (IWAPSO) and genetic algorithm (GA) in searching the optimal bidding strategy solutions.

Robust optimization-based DC optimal power flow for managing wind generation uncertainty

Integrating wind generation into the wider grid causes a number of challenges to traditional power system operation. Given the relatively large wind forecast errors, congestion management tools based on optimal power flow (OPF) need to be improved. In this paper, a robust optimization (RO)-based DCOPF is proposed to determine the optimal generation dispatch and locational marginal prices (LMPs) for a day-ahead competitive electricity market considering the risk of dispatch cost variation. The basic concept is to use the dispatch to hedge against the possibility of reduced or increased wind generation. The proposed RO-based DCOPF is compared with a stochastic non-linear programming (SNP) approach on a modified PJM 5-bus system. Primary test results show that the proposed DCOPF model can provide lower dispatch cost than the SNP approach.

MULTI‐OBJECTIVE BIDDING STRATEGY FOR GENCO USING NON‐DOMINATED SORTING PARTICLE SWARM OPTIMIZATION

This paper proposes a multi‐objective bidding strategy for a generation company (GenCo) in uniform price spot market using non‐dominated sorting particle swarm optimization (NSPSO). Instead of using a tradeoff technique, NSPSO is introduced to solve the multi‐objective strategic bidding problem considering expected profit maximization and risk (profit variation) minimization. Monte Carlo simulation is employed to simulate rivals’ bidding behavior. Test results indicate that the proposed approach can provide the efficient non‐dominated solution front effectively. In addition, it can be used as a decision making tool for a GenCo compromising between expected profit and price risk in spot market.

Multi-objective Economic Dispatch Considering Wind Power Penetration Using Stochastic Weight Trade-off Chaotic NSPSO

Abstract In this paper, a stochastic weight trade-off chaotic non-dominated sorting particle swarm optimization (SWTC_NSPSO) is proposed for solving multi-objective economic dispatch considering wind power penetration. Multi-objective functions including generator fuel cost and system risk are considered. The SWTC_NSPSO algorithm improves the solution search capability by balancing between global best exploration and local best utilization through the stochastic weight trade-off technique combining dynamistic coefficients trade-off methods. The proposed algorithm cooperates with the freak, lethargy factors, and chaotic mutation to enhance diversity and search capability. Non-dominated sorting and crowding distance techniques efficiently provide the optimal Pareto front. The fuzzy function is used to select the local compromise best solution. Using a two stage approach, the global best compromise solution is selected from a large number of local best compromise trial solutions. Simulation results on the modified IEEE 30-bus test system indicate that SWTC_NSPSO can provide a lower and wider Pareto front than non-dominated sorting genetic algorithm II (NSGAII), non-dominated sorting particle swarm optimization (NSPSO), non-dominated sorting chaotic particle swarm optimization (NS_CPSO), and a stochastic weight trade-off non-dominated sorting particle swarm optimization (SWT_NSPSO) in a less computation effort, leading to a lower generator fuel cost and a higher system reliability trade-off solution.