A multi-objective optimization evaluation framework for integration of distributed energy resources

Ahmadi, Bahman; Ceylan, Oğuzhan; Özdemir, Aydoğan

doi:10.1016/j.est.2021.103005

Cited by 38 publications

(17 citation statements)

References 37 publications

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“…This study overlooked different DER constraints. In [40,41], the researchers studied the optimal operation of BESSs to minimize distribution losses and battery cycle loss by regulating the voltage, reducing losses and peak loads. However, the rates of charge and discharge of the BESS were not considered, which may pose a safety hazard.…”

Section: Cost Optimizationmentioning

confidence: 99%

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Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Hijazi,

Hong

2024

Energies

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Over the past few years as COVID-19 was declared a worldwide pandemic that resulted in load changes and an increase in residential loads, utilities have faced increasing challenges in maintaining load balance. Because out-of-home activities were limited, daily residential electricity consumption increased by about 12–30% with variable peak hours. In addition, battery energy storage systems (BESSs) became more affordable, and thus higher storage system adoption rates were witnessed. This variation created uncertainties for electric grid operators. The objective of this research is to study the optimal operation of residential battery storage systems to maximize utility benefits. This is accomplished by formulating an objective function to minimize distribution and generation losses, generation fuel prices, market fuel prices, generation at peak time, and battery operation cost and to maximize battery capacity. A mixed-integer linear programming (MILP) method has been developed and implemented for these purposes. A residential utility circuit has been selected for a case study. The circuit includes 315 buses and 100 battery energy storage systems without the connection of other distributed energy resources (DERs), e.g., photovoltaic and wind. Assuming that the batteries are charging overnight, the results show that energy costs can be reduced by 10% and losses can decrease by 17% by optimally operating batteries to support increased load demand.

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Section: Cost Optimizationmentioning

confidence: 99%

“…Losses were not included as part of the study. MOLP BESS, PV [40] A new formulation for optimal allocation and sizing of DERs and energy storage systems (ESSs) to improve voltage profile and minimize annual costs using a multi-objective multiverse optimization method (MOMVO).…”

Section: Cost Optimizationmentioning

confidence: 99%

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Hijazi,

Hong

2024

Energies

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“…A dynamic stopping criterion is added to the algorithm to find more accurate Pareto solutions. The dynamic stopping criterion uses the C index [39] of the obtained solutions and will be explained in 4.4. The percentage of domination of the solutions at each iteration is compared to the predefined iteration number before.…”

Section: Multi-objective Sine-cosine Algorithmmentioning

confidence: 99%

A multi-objective framework for distributed energy resources planning and storage management

et al. 2022

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“…To analyze the performance and the quality of the MOSSA results, comparisons have been made with MOEA/D and MOPSO for the same condition, parameters, and twenty algorithm runs. Since the Pareto optimal front for the proposed objective function and data is not known to make sure that MOSSA Pareto results are optimal solutions, the algorithms comparisons are based on the C index [36] and spacing metric (S metric) [37], [38].…”

Section: Multi-objective Algorithm Comparisonmentioning

confidence: 99%

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

Ahmadi

Çağlar

2022

IJECE

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<span>The integration of distributed generators (DGs), which are based on renewable energy sources, energy storage systems, and static VAR compensators (SVCs), requires considering more challenging operational cases due to the variability of DG production contributed by different characteristics for different time sequences. The size, quantity, technology, and location of DG units have major effects on the system to benefit from the integration. All these aspects create a multi-objective scope; therefore, it is considered a multi-objective mixed-integer optimization problem. This paper presents an improved multi-objective salp swarm optimization algorithm (MOSSA) to obtain multiple Pareto efficient solutions for the optimal number, location, and capacity of DGs and the controlling strategy of SVC a radial distribution system. MOSSA is a bio-inspired optimizer based on swarm intelligence techniques and it is used in finding the optimal solution for a global optimization problem. Two sets of objective functions have been formulated minimizing DGs and SVC cost, voltage violation, energy losses, and system emission cost. The usefulness of the proposed MOSSA has been tested with the 33-bus and 141-bus radial distribution systems and the qualitative comparisons against two well-known algorithms, multiple objective evolutionary algorithms based on decomposition (MOEA/D), and multiple objective particle swarm optimization (MOPSO) algorithm.</span>

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A multi-objective optimization evaluation framework for integration of distributed energy resources

Cited by 38 publications

References 37 publications

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

Optimal Operation of Residential Battery Energy Storage Systems under COVID-19 Load Changes

A multi-objective framework for distributed energy resources planning and storage management

Determining the Pareto front of distributed generator and static VAR compensator units placement in distribution networks

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