A New Energy Management Control Method for Energy Storage Systems in Microgrids

Hajebrahimi, Hadis; Kaviri, Sajjad Makhdoomi; Eren, Suzan; Bakhshai, Alireza

doi:10.1109/tpel.2020.2978672

Cited by 50 publications

(22 citation statements)

References 35 publications

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“…According to [28][29][30], the economic dispatch of DC-based dispatchable DG units was achieved based on linearized models of the MG, while an optimal control problem was formulated to enhance common bus voltage regulation by tuning controller parameters on one hand [27,[31][32][33] and by achieving optimal storage cycle on the other [34,35]. The DC droop settings were selected based on the economic criteria in [36] and the environmentally oriented objectives in [37].…”

Section: Microgridmentioning

confidence: 99%

“…On the other hand, an interior point method was To address certain limitations of primary control, many research studies have focused on reinforcing primary and secondary control action to achieve accurate and dynamic reactive power sharing [61][62][63][64]72]. Accurate voltage reference adjustments have been achieved by means of classical and metaheuristic optimization techniques in [73,74], while battery energy storage systems' (BESS) charging and discharging balance has been obtained in [27,33,35] by adopting a unified cooperative secondary controller to minimize voltage regulation error on a common DC bus in droop-regulated DCIMG. Multi-agent systems (MAS) were employed in [75][76][77][78][79][80][81] as a secondary control mechanism in a distributed control manner to reduce participating DG units' reliance on a central controller.…”

Section: Tertiary Controlmentioning

confidence: 99%

“…Control problems combined with dispatch and scheduling problems form the bases for optimal EMS problems, which are responsible for implementing the hierarchal control strategy of DCIMG. Therefore, solving an EMS problem acts as a buffer to serve a variety of technical [33,35,54,59,85,91,92,117,134,148,154,[163][164][165][166], economic [28][29][30][75][76][77][78][79][80][81]84,85,91,[124][125][126][127][128][129][130][131][132][133]135,144,145,[148][149][150][151]167,168], and environmental [84,…”

Section: Control and Ems Problemmentioning

confidence: 99%

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Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review

Kreishan

Zobaa

2021

Energies

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This review paper provides a critical interpretation and analysis of almost 150 dedicated optimization research papers in the field of droop-controlled islanded microgrids. The significance of optimal microgrid allocation and operation studies comes from their importance for further deployment of renewable energy, reliable and stable autonomous operation on a larger scale, and the electrification of rural and isolated communities. Additionally, a comprehensive overview of islanded microgrids in terms of structure, type, and hierarchical control strategy was presented. Furthermore, a larger emphasis was given to the main optimization problems faced by droop-controlled islanded microgrids such as allocation, scheduling and dispatch, reconfiguration, control, and energy management systems. The main outcome of this review in relation to optimization problem components is the classification of objective functions, constraints, and decision variables into 10, 9, and 6 distinctive categories, respectively, taking into consideration the multi-criteria decision problems as well as the optimization with uncertainty problems in the classification criterion. Additionally, the optimization techniques used were investigated and identified as classical and artificial intelligence algorithms with the latter gaining popularity in recent years. Lastly, some future trends for research were put forward and explained based on the critical analysis of the selected papers.

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Section: Microgridmentioning

confidence: 99%

Section: Tertiary Controlmentioning

confidence: 99%

Section: Control and Ems Problemmentioning

confidence: 99%

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Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review

Kreishan

Zobaa

2021

Energies

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“…Thus, to overcome this restriction, nonlinear controls have been investigated in the literature. in [17], an adaptive droop controller algorithm is proposed. Energy management-based optimal control is investigated in [18] for multiple energy storage system in a microgrid.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

et al. 2021

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Global environmental changes, nuclear power risks, losses in the electricity grid, and rising energy costs are increasing the desire to rely on more renewable energy for electricity generation. Recently, most people prefer to live and work in smart places like smart cities and smart universities which integrating smart grid systems. The large part of these smart grid systems is based on hybrid energy sources which make the energy management a challenging task. Thus, the design of an intelligent energy management controller is required. The present paper proposes an intelligent energy management controller based on combined fuzzy logic and fractional-order proportional-integral-derivative (FO-PID) controller methods for a smart DC-microgrid. The hybrid energy sources integrated into the DC-microgrid are constituted by a battery bank, wind energy, and photovoltaic (PV) energy source. The source-side converters (SSCs) are controller by the new intelligent fractional order PID strategy to extract the maximum power from the renewable energy sources (wind and PV) and improve the power quality supplied to the DC-microgrid. To make the microgrid as cost-effective, the (wind and PV) energy sources are prioritized. The proposed controller ensures smooth output power and service continuity. Simulation results of the proposed control schema under Matlab/Simulink are presented and compared with the super twisting fractional-order controller.

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“…In 2019, the cities accounted for more than 75% of energy consumed across China. This proportion is expected to surpass 80% in 2029 [1][2][3][4][5][6]. To balance urban development with environment protection and energy consumption, it is necessary to complete the shift from the traditional model of urban construction to the planned construction of a composite system between smartness and energy, along with the rise of smart cities.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Optimization of Digital Management for Renewable Energies in Smart Cities

Huang¹,

Huang²,

Huang³

2020

JESA

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Smart cities mark the shift from the traditional model of urban construction to the planned construction of a composite system between smartness and energy. Considering the defects of renewable energies, e.g., intermittency, randomness, and low dispatchability, it is imperative to explore the digital and unified management of urban renewable energies. Therefore, this paper presents a multi-objective optimization algorithm for digital management, which can quantify the multiple energy models of smart cities. Firstly, the dimensions of renewable energy construction in smart cities were detailed, and the functions, hierarchy, and data flows of the digital management system for renewable energies were plotted in turn. After that, the output probability models of typical renewable energy power generation systems were established, plus the objective functions of digital management for renewable energies. Finally, particle swarm optimization (PSO) was combined with genetic algorithm (GA) for multi-objective optimization of the digital management for renewable energies in smart cities. The proposed models and algorithm were proved effective through experiments.

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A New Energy Management Control Method for Energy Storage Systems in Microgrids

Cited by 50 publications

References 35 publications

Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review

Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review

Hybrid Wind/PV/Battery Energy Management-Based Intelligent Non-Integer Control for Smart DC-Microgrid of Smart University

Multi-objective Optimization of Digital Management for Renewable Energies in Smart Cities

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