Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study

Bozorgavari, Seyed Aboozar; Aghaei, Jamshid; Pirouzi, Sasan; Nikoobakht, Ahmad; Farahmand, Hossein; Korpås, Magnus

doi:10.1016/j.rser.2020.109739

Cited by 70 publications

(34 citation statements)

References 28 publications

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“…Equation (2) describes the expected ELF that is equal to the sum of active power loss at the proposed operation horizon, that is, 24 h. Also, active power loss at hour t is related to the difference between active power generation by DGs and upstream network and load active power in that hour. Finally, the minimisation of the expected VDF is modelled in Equation (3), where it is expected that the voltage magnitude of all busses is close to the voltage of the slack bus during all simulation hours [16].

\min Cost = \sum_{w \in S} π_{w} \{\begin{matrix} left \sum_{t \in S T} (ρ_{t, w}^{normalele} P_{ref, t, w}^{normalG}) + \sum_{t \in S T} ρ_{t, w}^{normalgas} \sum_{n \in N} \\ left (0true \frac{P_{n, t, w}^{normalMT}}{η^{normalmt}} + G_{n, t, w}^{CHP} + G_{n, t, w}^{BO}) \\ left - \sum_{t \in S T} ρ_{t, w}^{normalheat} \sum_{n \in N} (H_{n, t, w}^{normalCHP} + H_{n, t, w}^{normalBO} \\ left + H_{n, t, w}^{normaldch} - H_{n, t, w}^{normalch}) - \sum_{t \in S T} ρ_{t, w}^{normalele} \sum_{n \in N} \\ left true(P_{n, t, w}^{MT} + P_{n, t, w}^{PV} + P_{n, t, w}^{WT} + P_{n, t, w<...>} \end{matrix}\}

…”

Section: Proposed Problem Formulationmentioning

confidence: 99%

Multi‐objective operation of distributed generations and thermal blocks in microgrids based on energy management system

Homayoun

Bahmani-Firouzi

Niknam

2021

IET Generation Trans & Dist

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The optimal multi-objective operation of microgrids with distributed generations and thermal block based on the energy management system is presented. In the thermal block, the combined heat and power system and boiler and thermal storage system supply the load of the block thermal. Therefore, the proposed strategy minimises three objectives of an microgrid's operation, that is, cost, energy loss and voltage deviation functions. Also, it is subject to AC power flow equations, system operation limits and distributed generations and thermal block constraints. The problem is modelled as a multi-objective optimisation method; then is converted to a single-objective problem model using the ε-constraintbased Pareto optimisation. Moreover, the proposed strategy includes uncertainties of the load, energy price and active power of renewable distributed generations; hence, a stochastic programming based on the hybrid Mont Carlo simulation and point estimate method is used to model these uncertain parameters. Then, the teaching-learning-based optimisation and firefly algorithm are utilised to solve the problem to achieve a reliable optimal solution. Finally, the proposed strategy is simulated on the IEEE 69-bus microgrid, and thus, the capabilities of the proposed strategy are extracted.Nomenclature: π, Probability of occurrence of a scenario; η b , Efficiency of the boiler; ρ ele , ρ gas , ρ heat , Electricity, gas and heat energy price ($ MWh −1 ); η mt , Efficiency of the MT; η st , Efficiency of the TSS; η t , η l , η h , Electricity, loss and heat efficiency of the CHP; A L , The bus incidence matrix considering the line current direction; C MT , Operation price of the MT ($ MWh −1 ); E, Stored energy in the TSS in perunit (p.u.); E min , E max , Minimum and maximum allowed stored energy in the TSS (p.u.); g, b, Conductance and susceptance of a line (p.u.); H BO , G BO , Heat and gas power of the boiler (p.u.); H BOmin , H BOmax , Minimum and maximum allowed heat power of the boiler (p.u.); H ch , H dch , Heat charging and discharging power of the TSS (p.u.); H CHP , G CHP , Heat and gas power of the CHP (p.u.); H CHPmin , H CHPmax , Minimum and maximum allowed heat power of the CHP (p.u.); H D , Heat demand (p.u.); H STmax , Maximum heat charging/discharging power of the TSS (p.u.); n, l, N, Bus and bus, and set of buses; P CHPmin , P CHPmax , Minimum and maximum allowed active power of the CHP (p.u.); P D , Q D , Active and reactive demand (p.u.); P G , Q G , Active and reactive power of the distribution station (p.u.); P L , Q L , Active and reactive power flow on the line (p.u.); P MT , P CHP , Active power of micro-turbine (MT) and CHP (p.u.); P MTmin , P MTmax , Minimum and maximum allowed active power of the MT (p.u.); P PV , P WT , Active power of photovoltaic (PV) and wind turbine (WT) (p.u.); P PVmax , Maximum generation active power of the PV (p.u.); P WTmax , Maximum generation active power of the WT (p.u.); ref, Slack bus; S Gmax , Maximum capacity of the distribution station (p.u.); S Lmax , Maximum capac...

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\min Cost = \sum_{w \in S} π_{w} \{\begin{matrix} left \sum_{t \in S T} (ρ_{t, w}^{normalele} P_{ref, t, w}^{normalG}) + \sum_{t \in S T} ρ_{t, w}^{normalgas} \sum_{n \in N} \\ left (0true \frac{P_{n, t, w}^{normalMT}}{η^{normalmt}} + G_{n, t, w}^{CHP} + G_{n, t, w}^{BO}) \\ left - \sum_{t \in S T} ρ_{t, w}^{normalheat} \sum_{n \in N} (H_{n, t, w}^{normalCHP} + H_{n, t, w}^{normalBO} \\ left + H_{n, t, w}^{normaldch} - H_{n, t, w}^{normalch}) - \sum_{t \in S T} ρ_{t, w}^{normalele} \sum_{n \in N} \\ left true(P_{n, t, w}^{MT} + P_{n, t, w}^{PV} + P_{n, t, w}^{WT} + P_{n, t, w<...>} \end{matrix}\}

…”

Section: Proposed Problem Formulationmentioning

confidence: 99%

Multi‐objective operation of distributed generations and thermal blocks in microgrids based on energy management system

Homayoun

Bahmani-Firouzi

Niknam

2021

IET Generation Trans & Dist

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“…In many studies, such as [6,14,19], the ARO technique was used for this purpose. However, ARO and other robust optimization methods, such as the boundary uncertaintybased robust optimization (BURO) model [32], are applicable to linear optimization models. Nevertheless, due to the use of power flow equations, the planning and operation problems in the power system were in the MINLP and nonlinear programming (NLP) forms, respectively.…”

Section: Proposed Ea-aro Model For Solving Ac-scuc Problemmentioning

confidence: 99%

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Baziar

Ghotbabadi

et al. 2021

IEEE Access

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An AC security constrained unit commitment (AC-SCUC) in the presence of the renewable energy sources (RESs) and parallel flexible AC transmission system (FACTS) devices is conventionally modeled as a deterministic optimization problem to minimize the operation cost of conventional generation units (CGUs) subject to AC optimal power flow (AC-OPF) equations, operation constraints of RESs, parallel FACTS devices, and CGUs. To cope with the uncertainties of load and RES generation, robust and stochastic optimization and linearized formulation have been used to achieve a sub-optimal solution. To achieve the optimal solution, this paper proposed an evolutionary algorithm-based adaptive robust optimization (EA-ARO) approach to solve the non-linear and non-convex optimization problem. A hybrid solver of grey wolf optimization (GWO) and teaching learning-based optimization (TLBO) obtained the robust and reliable optimal solution for the proposed AC-SCUC in the worst-case scenario. Finally, the proposed method was simulated on standard IEEE test systems to demonstrate its capabilities, and the results showed the proposed hybrid solver obtained robust optimal solutions with reduced computation time and standard deviation. Moreover, the numerical results proved the proposed strategy's capabilities of improving the economics of generation units, such as lower operational cost, and technical performance of the transmission networks, such as improved voltage profile and reduced energy losses.INDEX TERMS AC security constrained unit commitment, Evolutionary algorithm-based adaptive robust optimization, Renewable energy sources, Parallel FACTS devices.

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“…(13), which minimizes the expected total operation cost of SDGs and the expected received energy cost from the upstream network 19 . Constraints (14)- (17) are correspond to the power flow equations, which represent the active and reactive power balance in network buses, and active and reactive power flowing through distribution lines, respectively 18,20 . In addition, the technical constraint of the distribution network including line and substation capacity limits and bus voltage limitations are expressed in Eqs.…”

Section: Proposed Protection Coordination Mechanismmentioning

confidence: 99%

“… In most studies, pickup current (PC) and time dial setting (TDS) are generally considered as DOCR adjustment parameters [18][19][20] . It is estimated that by adjusting the A and B constants in the digital relays beside the PC and TDS regulations, faster protection coordination can be achieved.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Security-Constrained Optimal Protection Coordination for Dual-Setting Digital Directional Overcurrent Relays in the Distribution Network Including Non-Renewable/Renewable Synchronous Distributed Generation

Pirouzi¹,

Shahi²,

Zadeh³

et al. 2021

Preprint

Self Cite

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In this paper, the security-constrained optimal protection coordination (SCOPC) is introduced for dual setting digital directional overcurrent relay (DDOCR) in distribution network, which including renewable and non-renewable synchronous distributed generation (SDG). The SCOPC minimizes the total operation time of DDOCRs in primary and backup protection operating to achieve a fast protection coordination. Also, to improve the flexibility in DDOCRs setting, the allowable limits of A and B coefficients, pickup current (PC) and time dial setting (TDS) in both reverse and forward directions are considered as constraints. Another constraint is the Coordination Time interval (CTI). To consideration of the mentioned scheme security, the SCOPC mechanism considered the unavailability of DDOCRs due to their failure, so the stochastic method is used to modelling of this parameter. To calculate the fault current, network variables are proportional to the daily stochastic operation results of distribution network. Moreover, the proposed problem is implemented on the standard distribution networks, and then the optimal solution is obtained with hybrid algorithm of grey wolf optimization (GWO) and training and learning optimization (TLBO). The numerical results illustrate that the proposed algorithm is able to achieve a reliable and fast protection coordination that has a low standard deviation.

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Robust planning of distributed battery energy storage systems in flexible smart distribution networks: A comprehensive study

Cited by 70 publications

References 28 publications

Multi‐objective operation of distributed generations and thermal blocks in microgrids based on energy management system

Multi‐objective operation of distributed generations and thermal blocks in microgrids based on energy management system

Evolutionary Algorithm-Based Adaptive Robust Optimization for AC Security Constrained Unit Commitment Considering Renewable Energy Sources and Shunt FACTS Devices

Security-Constrained Optimal Protection Coordination for Dual-Setting Digital Directional Overcurrent Relays in the Distribution Network Including Non-Renewable/Renewable Synchronous Distributed Generation

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