Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming

Nemati, Mohsen; Braun, Martin; Tenbohlen, Stefan

doi:10.1016/j.apenergy.2017.07.007

Cited by 331 publications

(184 citation statements)

References 26 publications

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“…J diesel includes two fundamental parts as fuel cost and switching cost given by :

J_{diesel} = true \sum_{k = 1}^{N_{h}} L_{diesel} false(k false),

L_{diesel} false(k false) = L_{fuel} false(k false) + L_{s w} false(k false),

L_{fuel} false(k false) = C_{fuel} \cdot false(a \cdot P_{g}^{2} false(k false) + b \cdot P_{g} false(k false) + c \cdot s_{g} false(k false) false) \cdot normalΔ t_{h},

L_{s w} false(k false) = C_{switching} \cdot ((), L_{on} false(k false) + L_{off} false(k false)),

where C fuel is the price of the fuel in US$/L, and the amount of consumed fuel is calculated by using diesel generator power P g . The constants a , b , and c can be obtained from fuel consumption given at different load percentages in the datasheet . The C switching is the estimated price of switching on and off of diesel generator, which is determined by binary variable

s_{g} false(k false) \in Z_{2}

\forall k \in {double-struckI}_{false[1 : N_{h} false]}

indicating on/off status of diesel generator.…”

Section: Methodsmentioning

confidence: 99%

“…In our EMS design, we have also considered the effect of these environmental externalities, which are given as CO 2 , SO 2 , and NO x . A possible cost function for these emitted gases is given in Nemati et al In our study, we assume that the diesel generator is the fundamental source of these gases, and emissions caused by upfront production of microgrid components are not taken into account. Consequently, the tailored cost is given by Equation :

J_{emissions} = true \sum_{k = 1}^{N_{h}} L_{emissions} false(k false),

L_{emissions} false(k false) = C_{emissions} ((), true \sum_{i = 1}^{3} α_{i} β_{i}) P_{g} false(k false) normalΔ t_{h},

where C emissions and Δ t h denote the total cost factor of emissions and fixed time step in hours, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…The performance evaluation is conducted by a 1‐year simulation using the nominal cost coefficients given in Table . Values in the table are determined by considering several sources on the day of August 18 as follows: • C fuel is taken from Shell Singapore's website, and SG$ is converted to US$ using conversion rate of 0.7295. • C switching is estimated as a quasi‐realistic value because of its difficulty of exact identification • C emissions is a coefficient to scale up or down all emission factors simultaneously.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Therefore, it has a unit value. However, the emission rates are given in Table by Nemati et al • C battery for 2018 is found by interpolation of LIB prices between 2016 and 2020 • C shifting and C curtailment do cause any direct economical loss, and therefore their nominal values are assigned as negligible values close to zero. …”

Section: Performance Evaluationmentioning

confidence: 99%

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Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

et al. 2019

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Summary The rapid increase of renewable energy sources made coordinated control of the distributed and intermittent generation units a more demanded task. Matching demand and supply is particularly challenging in islanded microgrids. In this study, we have demonstrated a mixed‐integer quadratic programming (MIQP) method to achieve efficient use of sources within an islanded microgrid. A unique objective function involving fuel consumption of diesel generator, degradation in a lithium‐ion battery energy storage system, carbon emissions, load shifting, and curtailment of the renewable sources is constructed, and an optimal operating point is pursued using the MIQP approach. A systematic and extensive methodology for building the objective function is given in a sequential and explicit manner with an emphasis on a novel model‐based battery aging formulation. Performance of the designed system and a sensitivity analysis of resulting battery dispatch, diesel generator usage, and storage aging against a range of optimization parameters are presented by considering real‐world specifications of the Semakau Island, an island in the vicinity of Singapore.

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“…J diesel includes two fundamental parts as fuel cost and switching cost given by :

J_{diesel} = true \sum_{k = 1}^{N_{h}} L_{diesel} false(k false),

L_{diesel} false(k false) = L_{fuel} false(k false) + L_{s w} false(k false),

L_{fuel} false(k false) = C_{fuel} \cdot false(a \cdot P_{g}^{2} false(k false) + b \cdot P_{g} false(k false) + c \cdot s_{g} false(k false) false) \cdot normalΔ t_{h},

L_{s w} false(k false) = C_{switching} \cdot ((), L_{on} false(k false) + L_{off} false(k false)),

s_{g} false(k false) \in Z_{2}

\forall k \in {double-struckI}_{false[1 : N_{h} false]}

indicating on/off status of diesel generator.…”

Section: Methodsmentioning

confidence: 99%

J_{emissions} = true \sum_{k = 1}^{N_{h}} L_{emissions} false(k false),

L_{emissions} false(k false) = C_{emissions} ((), true \sum_{i = 1}^{3} α_{i} β_{i}) P_{g} false(k false) normalΔ t_{h},

where C emissions and Δ t h denote the total cost factor of emissions and fixed time step in hours, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

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Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

et al. 2019

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“…Multitude researches have investigated the MG operation with various DGs . For example, Hernandez‐Aramburo et al presented a fuel minimization in an MG, which contains two reciprocating gas engines, one combined heat/power plant, a photovoltaic (PV) set, and a wind turbine.…”

Section: Introductionmentioning

confidence: 99%

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Zeng

Berti

2019

Computational Intelligence

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Microgrids can be assumed as a solution model for green energy sources, energy storage systems, and combined heat and power (CHP) systems. In this work, the cost and emission minimization based on a demand response (DR) program is considered an optimization problem. To solve the mentioned problem a new multiobjective optimization algorithm (improved particle swarm optimization) is proposed based on a fuzzy mechanism to select the optimal value. The microgrid system includes two CHP units, fuel cell and battery systems, and the heat buffer tank. In this problem, two different feasible operating regions have been assumed in CHPs. Accordingly, to decrease the operational cost, time‐of‐use, and real‐time pricing DR programs have been simulated, and the impacts of the mentioned models are evaluated overload profiles. The effectiveness of proposed models has been applied on different cases studies by different scenarios. The proposed model solved the DR program, time of use‐DR and real‐time pricing‐DR problems. The proposed model could reduce the cost about 10%.

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Techno‐economic analysis of solar grid‐based virtual power plant in Indian power sector: A case study

Sharma

Mishra

2019

Int Trans Electr Energ Syst

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Summary The environmental threat and rising fuel prices are the two major challenges faced by the utility to provide clean and affordable electricity to the consumers. The change in government policies provides assistance for large‐scale deployment of Distributed Energy Resource (DER) like rooftop solar PV, but at the same time, the intermittent nature of this renewable‐based DER produce adverse effects on utility stability and its economic viability. The concept of virtual power plant (VPP) can be a possible solution for these issues through coordination and scheduling of DER, storage, and flexible load, which results in secure operation with high penetration of DER and utility peak‐load reduction. In this paper, the Distributed Energy Resources Customer Adoption Model (DER‐CAM) is utilized, which is a Mixed Integer Linear Programming‐based decision‐making tool. For determining the potential of VPP and its implications, a case study of Punjab State Power Corporation Limited (PSPCL), a state power utility, is studied. The main objectives of the paper are cost minimization, peak‐load reduction, and reliability enhancement. The DER‐CAM model simulates different load profiles, and the optimal investment solution is determined, which ensures monetary benefits for both consumer and utility.

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Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming

Cited by 331 publications

References 26 publications

Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

Design and analysis of an aging‐aware energy management system for islanded grids using mixed‐integer quadratic programming

New optimization method based on energy management in microgrids based on energy storage systems and combined heat and power

Techno‐economic analysis of solar grid‐based virtual power plant in Indian power sector: A case study

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