Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties

Shams, Mohammad H.; Shahabi, Majid; Kia, Mohsen; Heidari, Alireza; Lotfi, Mohamed; Shafie‐khah, Miadreza; Catalão, João P.S.

doi:10.1016/j.energy.2019.115949

Cited by 79 publications

(49 citation statements)

References 20 publications

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“…Moreover, a two-stage stochastic optimization model is proposed in Reference [18] to solve the uncertainty of power demand and to further help CCHP system operators formulate reasonable price strategies at different demand levels. Similarly, Reference [19] adopts stochastic optimization model to accommodate the fluctuations of renewable energy power generation and heating load and uses the scenario reduction method to reflect the randomness of the model. It is not hard to find that numerous studies have adopted the theory of stochastic programming, but this theory has the disadvantage that the final solution result is an expected value.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When solving the stochastic programming issue, the most pivotal step is scenario clustering or scenario reduction. A scenario generation and reduction method is proposed in Reference [19] to deal with electrical and thermal load uncertainty. For the sake of simplicity, some literature directly gives a number of typical scenarios to describe randomness.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Equation (19) represents the battery's electric energy state transfer equation; E BESS t denotes the battery's stored electric energy in the time period t; η C , η D , and η L represent the battery's charging and discharging efficiencies and the battery leakage rate, respectively; u BESS chr,t and u BESS dis,t are the state variables of the battery's charging and discharging in the time period, respectively; and P chr,t and P dis,t are the battery's corresponding charging and discharging power in the time period, respectively. Equations (20)-(23) respectively represent charge state constraint, charge and discharge power constraint, and charge and discharge state constraint.…”

Section: • Batterymentioning

confidence: 99%

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Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

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With the rapid transformation of energy structures, the Integrated Energy System (IES) has developed rapidly. It can meet the complementary needs of various energy sources such as cold, thermal, and electricity in industrial parks; can realize multi-energy complements and centralized energy supplies; and can further improve the use efficiency of energy. However, with the extensive access of renewable energy, the uncertainty and intermittentness of renewable energy power generation will greatly reduce the use efficiency of renewable energy and the supply flexibility of IES so as to increase the operational risk of the system operator. With the goal of minimum sum of the system-operating cost and the carbon-emission penalty cost, this paper analyzes the combined supply of cooling, heating, and power (CCHP) influence on system efficiency, compared with the traditional IES. The flexible modified IES realizes the decoupling of cooling, thermal, and electricity; enhances the flexibility of the IES in a variety of energy supply; at the same time, improves the use efficiency of multi-energy; and reasonably avoids the occurrence of energy loss and resource waste. With the aim of reducing the risk that the access of renewable energy may bring to the IES, this paper introduces the fuzzy c-mean-clustering comprehensive quality (FCM-CCQ) algorithm, which is a novel method superior to the general clustering method and performs cluster analysis on the output scenarios of wind power and photovoltaic. Meanwhile, conditional value at risk (CVaR) theory is added to control the system operation risk, which is rarely applied in the field of IES optimization. The model is simulated in a numerical example, and the results demonstrate that the availability and applicability of the presented model are verified. In addition, the carbon dioxide emission of the traditional operation mode; thermoelectric decoupling operation mode; and cooling, thermal, and electricity decoupling operation mode of the IES decrease successively. The system flexibility is greatly enhanced, and the energy-use rate of the system is improved as a whole. Finally, IES, after its flexible transformation, significantly achieve energy conservation, emission reduction, and environmental protection.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: • Batterymentioning

confidence: 99%

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Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Liu

Nie

2019

Sustainability

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“…In order to realise the potential of distributed energy resources (DERs), they are clustered as microgrids (MGs). [1][2][3] As per the definition of CIGRÉ C6. 22 Working Group, Microgrid Evolution Roadmap, "Microgrids are electricity distribution systems containing loads and DERs (such as distributed generators, storage devices, or controllable loads) that can be operated in a controlled, coordinated way either while connected to the main power network or while islanded".…”

Section: Introductionmentioning

confidence: 99%

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Jordehi

2020

Int J Energy Res

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Using electric storage systems (ESSs) is known as a viable strategy to mitigate the volatility and intermittency of renewable distributed generators (DGs) in microgrids (MGs). Among different electric storage technologies, battery energy storage (BES) is considered as the best option. In unit commitment (UC) module, the set of committed dispatchable DGs along with their power, power exported to/imported from macrogrid and status and power of ESS units are determined. In this paper, BES degradation is considered in UC formulation and an efficient particle swarm optimisation with quadratic transfer function is proposed for solving UC in BES-integrated MGs, while the uncertainties of demand, renewable generation and market price are considered and dealt with robust optimisation. UC is formulated as a multi-objective optimisation problem whose objectives are MG operation cost and BES degradation. The resultant multi-objective optimisation problem is converted into a singleobjective optimisation problem and the effect of weight factors on MG operation cost and BES lifecycle are investigated. The results show that by consideration of BES degradation in objective function, BES lifecycle increases from 350 to 500 and the minimum depth of charge increases from 5.5% to 34%; however, MG operation cost increases from $8717 to $8910.2. The results also show that by consideration of uncertainties, MG's operation cost increases by 8.22%.

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“…Specific to the planning of islanded microgrids with uncertain load demands and renewable generations, limited works exist in the literature. A novel approach based on linear programming is proposed in [22], for estimating the optimal configuration planning of islanded multi‐energy hub. The correlation among various uncertain variables does exist in practice, and hence the methodologies proposed should consider this aspect [23–25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic planning of islanded microgrids with uncertain multi‐energy demands and renewable generations

Jithendranath

Das

2020

IET Renewable Power Generation

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Islanded microgrids (IMGs) are embedded power networks with distributed energy resources (DERs) providing a reliable and flexible energy option for off‐grid customers. This work addresses the planning model of renewable‐based IMGs feeding multi‐energy demands considering investment and emission related objectives. The proposed solution is to determine the optimal mix and sizing of various energy sources in IMG, including renewables; for multiple energy demands. This study also presents a hybrid‐scenario and Monte Carlo approach to gauge the uncertainty involved in multi‐energy demands, i.e. electrical, heating, and cooling loads; together with correlation among wind and solar generations. The spatial interdependence among renewable generations is implemented using copula; that generates a synthetic set of stochastic correlated data. The combined load scenarios for multi‐energy demands and renewable samples are implemented with the proposed hybrid approach in the formulated stochastic planning model. In this work, the formulated problem is proposed to solve using meta‐heuristic multi‐objective ant lion optimiser algorithm, that is validated on the test system. The superiority of the proposed approach is highlighted in comparison with other multi‐objective optimisers. The multi‐energy dispatch between associated sources and loads were simulated to show how the obtained capacity can suffice the seasonal multi‐energy demands of a typical day considered.

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Optimal operation of electrical and thermal resources in microgrids with energy hubs considering uncertainties

Cited by 79 publications

References 20 publications

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

Low Carbon Scheduling Optimization of Flexible Integrated Energy System Considering CVaR and Energy Efficiency

An improved particle swarm optimisation for unit commitment in microgrids with battery energy storage systems considering battery degradation and uncertainties

Stochastic planning of islanded microgrids with uncertain multi‐energy demands and renewable generations

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