A<scp>multimicrogrid</scp>energy management model implementing an evolutionary game‐theoretic approach

Águila-León, Jesús; Chiñas-Palacios, Cristian; García, Edith; Vargas‐Salgado, Carlos

doi:10.1002/2050-7038.12617

Cited by 17 publications

(9 citation statements)

References 46 publications

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“…In addition, HOMER has the advantage of being able to integrate storage systems [35][36][37]. Moreover, much scientific work uses HOMER to simulate renewable energy systems [38][39][40][41][42]. It is used to solve rural electrification problems where grid power is expensive or insufficient, in scenarios where the software calculates the most cost-effective and techno-economically renewable energy alternative [43].…”

Section: Introductionmentioning

confidence: 99%

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

et al. 2022

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The decarbonization of the electric generation system is fundamental to reaching the desired scenario of zero greenhouse gas emissions. For this purpose, this study describes the combined utilization of renewable sources (PV and wind), which are mature and cost-effective renewable technologies. Storage technologies are also considered (pumping storage and mega-batteries) to manage the variability in the generation inherent to renewable sources. This work also analyzes the combined use of renewable energies with storage systems for a total electrification scenario of Grand Canary Island (Spain). After analyzing the natural site’s resource constraints and focusing on having a techno-economically feasible, zero-emission, and low-waste renewable generation mix, six scenarios for 2040 are considered combining demand response and business as usual. The most optimal solution is the scenario with the maximum demand response, consisting of 3700 MW of PV, around 700 MW of off-shore wind system, 607 MW of pump storage, and 2300 MW of EV batteries capacity. The initial investment would be EUR 8065 million, and the LCOE close to EUR 0.11/kWh, making the total NPC EUR 13,655 million. The payback is 12.4 years, and the internal rate of return is 6.39%.

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Section: Introductionmentioning

confidence: 99%

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

et al. 2022

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“…This model was used to estimate the amount of biomass that was used to produce the required syngas, which is needed to meet the energy demand [14]. To enhance power exchange, the two-round fuzzy-based speed (TRFS) algorithm followed Stackelberg's game theory, and the Quasi-oppositional Symbiotic Organism Search Algorithm was used in a multi-MG environment to study the power exchange problem [15].…”

Section: Introductionmentioning

confidence: 99%

CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization

Tiwari¹,

Dubey²,

Pandit³

et al. 2022

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In this paper, the economically self-sufficient microgrid is planned to provide electric power and heat demand. The combined heat and power-based microgrid needs strategic placement of distributed generators concerning optimal size, location, and type. As fossil fuel cost and emission depend mainly on the types of distributed generator units used in the microgrid, economic emission dispatch is performed for an hour with a static load demand and multiple load demands over 24 h of a day. The TOPSIS ranking approach is used as a tool to obtain the best compromise solution. Harris Hawks Optimization (HHO) is used to solve the problem. For validation, the obtained results in terms of cost, emission, and heat are compared with the reported results by DE and PSO, which shows the superiority of HHO over them. The impact of renewable integration in terms of cost and emission is also investigated. With renewable energy integration, fuel cost is reduced by 18% and emission is reduced by 3.4% for analysis under static load demand, whereas for the multiple load demands over 24 h, fuel cost is reduced by 14.95% and emission is reduced by 5.58%.

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“…The daily DG production and load profiles are used for optimization, and the cost of BESS installation and operation is considered as an objective function. The various methods of computational intelligence, artificial neural network, fuzzy logic, and metaheuristic optimization are used in [13] to predict the production of DG based on weather data, define the operational state of the microgrid (grid-connected or islanded), and dynamically control the microgrid in islanded mode. For a recent review of the application of computational intelligence techniques to PV system modeling, see [14].…”

Section: Introductionmentioning

confidence: 99%

Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference

et al. 2022

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The main problem in planning the optimal operation of renewable energy sources and battery storage systems is the amount of data that must be considered to cover an entire observation period. If the observation period is one year, the characteristic days or averaged data (daily, weekly or monthly averages) are considered to reduce the number of data. Since the average values of the entered data differ from the actual values, it is better to work with hourly or 15-minute data at the annual level. The study presents a framework for solving the problem of the optimal allocation and operation of renewable energy sources and battery storage systems. The proposed method simultaneously solves the optimal allocation and energy management problem considering hourly data at the annual level. The fuzzy inference-based system is proposed for scheduling optimal profiles of battery storage systems and renewable energy sources. The developed fuzzy inference system manages the power factors of the photovoltaic and wind power systems, the power factor and output of the biogas plant, and the operating status of the battery storage system. The presented method simultaneously finds the optimal parameters of the energy management system and the optimal allocation and operation of the renewable energy sources and the battery storage system. The developed method is based on the calculation of steady-state power flow. The proposed method is to be used in the design phase for the installation of various renewable energy sources and battery storage systems. In addition, the method is intended to be used to optimally control the power output of energy sources and the operation of energy storage systems during steady-state operation in order to operate the distribution network with minimum annual active energy losses. The developed method is applied to the test distribution system IEEE with 37 nodes. The reduction in annual energy losses in the tested distribution system is about 80% compared to the base case without renewable energy sources and battery storage system.

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Amultimicrogridenergy management model implementing an evolutionary game‐theoretic approach

Cited by 17 publications

References 46 publications

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

Optimization of All-Renewable Generation Mix According to Different Demand Response Scenarios to Cover All the Electricity Demand Forecast by 2040: The Case of the Grand Canary Island

CHP-Based Economic Emission Dispatch of Microgrid Using Harris Hawks Optimization

Optimal Allocation of Renewable Energy Sources and Battery Storage Systems Considering Energy Management System Optimization Based on Fuzzy Inference

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