Design of joint active and reactive power reserve market: a multi‐objective approach using NSGA II

Ahmadi, Hamed; Foroud, Asghar Akbari

doi:10.1049/iet-gtd.2014.1226

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…Based on available historical data, the K-means clustering method is employed to obtain the scenarios of load uncertainties, while a discretization method is used to get states of DGs including wind turbine and PV array outputs. In addition, considering the wide and successful application of NSGA-II in power system optimization [38][39][40], the application of NSGA-II presented in [38] is adopted to solve the constructed multi-objective formulas. The flow chart is shown in Fig.…”

Section: Multi-objective Formulasmentioning

confidence: 99%

Economic optimization for configuration and sizing of micro integrated energy systems

Zhang

Deng

et al. 2017

J. Mod. Power Syst. Clean Energy

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Based on analysis of construction and operation of micro integrated energy systems (MIES), this paper presents economic optimization for their configuration and sizing. After presenting typical models for MIES, a residential community MIES is developed by analyzing residential direct energy consumption within a general design procedure. Integrating with available current technologies and local resources, the systematic design considers a prime mover, fed by natural gas, with wind power, photovoltaic generation, and two storage devices serving thermal energy and power to satisfy cooling, heating and electricity demands. Control strategies for MIES also are presented in this study. Multi-objective formulas are obtained by analyzing annual cost and dumped renewable energy to achieve optimal coordination of energy supply and demand. According to historical load data and the probability distribution of distributed generation output, clustering methods based on K-means and discretization methods are employed to obtain typical scenarios representative of uncertainties. The modified non-dominated sorting genetic algorithm is applied to find the Pareto frontier of the constructed multi-objective formulas. In addition, aiming to explore the Pareto frontier, the dumped energy cost ratio is defined to check the energy balance in different MIES designs and provide decision support for the investors. Finally, simulations and comparision show the appropriateness of the developed model and the applicability of the adopted optimization algorithm.

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Section: Multi-objective Formulasmentioning

confidence: 99%

Economic optimization for configuration and sizing of micro integrated energy systems

Zhang

Deng

et al. 2017

J. Mod. Power Syst. Clean Energy

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“…Various methods for RP pricing were proposed in RP market papers [16][17][18]. The TPF, which is used in (1), was proposed in [16,17].…”

Section: Total Pfmentioning

confidence: 99%

Design of reactive power and reactive power reserve market

Khandani

Foroud

2017

IET Generation, Transmission & Distribution

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“…Compared with contemporary constraint-handling strategy, application of NSGA-II is encouraged to solve more complex and realworld multiobjective optimization problems [34]. NSGA-II has also played an indispensable role in solving constrained multiobjective optimization of the electric industry [35][36][37]. Fast and elitist NSGA-II algorithm is employed to avoid artificially balanced solutions [35].…”

Section: Introductionmentioning

confidence: 99%

“…Fast and elitist NSGA-II algorithm is employed to avoid artificially balanced solutions [35]. In Reference [36], NSGA-II is applied to deal with simultaneously determining optimal capacities of active and reactive power reserve. NSGA-II and fuzzy set theory is chosen to find the best compromise solution [37].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Optimization Approach for Coordinating Different DG from Distribution Network Operator

Zhang

Yang

et al. 2018

Journal of Electrical and Computer Engineering

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Integrating with analysis of uncertainties, this paper presented a multiobjective optimization approach for coordinating different DG from the perspective of Distribution Network Operator (DISOPER). Aiming to three uncertain factors including fuzzy variable, random variable, and interval variable, the information entropy and interval analysis methods are adopted to construct multistate models of multisource uncertainty. The information entropy method is to convert fuzzy variable into equivalent random variable. Interval analysis method is to transform random variables into interval variables by setting a confidence level. Then plenty of simulation analysis based on the small probability event and expectation are investigated to reduce the computational burden and eliminate invalid computation. Subsequently, multiobjective formulations based on multistate are built by analyzing systematical power loss, voltage quality, reliability, and environment change provide some reference for DISOPER in dealing with access of privately owned DG units. Furthermore, based on network topology analysis and modified nondominated sorting genetic algorithm (NSGA), a combinatorial optimization method is proposed to reduce search space and solve the constructed formulations efficiently. Simulations are carried out on IEEE 37-bus systems and results are presented and discussed.

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Design of joint active and reactive power reserve market: a multi‐objective approach using NSGA II

Cited by 17 publications

References 22 publications

Economic optimization for configuration and sizing of micro integrated energy systems

Economic optimization for configuration and sizing of micro integrated energy systems

Design of reactive power and reactive power reserve market

Multiobjective Optimization Approach for Coordinating Different DG from Distribution Network Operator

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