Developing a CCHP- microgrid operation decision model under uncertainty

Marino, Carlos; Marufuzzaman, Mohammad; Hu, Mengqi; Sarder, M.D.

doi:10.1016/j.cie.2017.11.021

Cited by 41 publications

(15 citation statements)

References 47 publications

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“…Except for load uncertainty, Reference [17] also proposes a method to handle the uncertainties of wind power and power generation equipment failure, which uses the hybrid stochastic-information gap decision theory (HS-IGDT) algorithm to deal with the uncertain set, and verifies the feasibility of the model through a 10-node Institute of Electrical and Electronics Engineers (IEEE) standard test system. 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.…”

Section: Literature Reviewmentioning

confidence: 99%

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%

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

Liu

Nie

2019

Sustainability

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“…The longer-term energy marketing is considered in the planning problem and then given as a target to the tracking problem. In addition, a two-stage stochastic programming model for CCHP-microgrid operation considering demand uncertainty is proposed in [9]. However, with the introduction of the continuous intraday market, the boundaries between the planning and tracking phases might become blurred.…”

Section: Computational Time T Smentioning

confidence: 99%

“…The time-step width along the prediction horizon increases for time steps that lie further in the future, since if only the most recent time step is actually executed, model accuracy can be maintained while the number of decision variables is reduced. In this regard, the general idea of the proposed controller is to predict both the thermal and electrical power production that maximize Luo et al [8] Marino et al [9] Zhang et al [11] Aluisio et al [5] Costa and Fichera [4] Zhang et al [3] Proposed strategy the profit during the CHP operation considering both operating and energy market constraints. Thus, the proposed controller will be designed based on a model for the operation of a CHP plant obtained by using SI methods and real data sets.…”

Section: Computational Time T Smentioning

confidence: 99%

“…Finally, since the optimization problem in (14) explicitly considers models for the generation of both electric and thermal power (9), suitable expressions for characteristic maps f 1 , f 2 and f 3 in (9) are required. Taking into account the complexity of these systems, this paper proposes the identification of datadriven models by using SI methods.…”

Section: Non-constant Time-step Sizementioning

confidence: 99%

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Optimal operation of combined heat and power systems: An optimization-based control strategy

Diaz

Ocampo‐Martínez

Panten³

et al. 2019

Energy Conversion and Management

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The use of decentralized Combined Heat and Power (CHP) plants is increasing since the high levels of efficiency they can achieve. Thus, to determine the optimal operation of these systems in dynamic energy-market scenarios, operational constraints and the time-varying price profiles for both electricity and the required resources should be taken into account. In order to maximize the profit during the operation of the CHP plant, this paper proposes an optimization-based controller designed according to the Economic Model Predictive Control (EMPC) approach, which uses a non-constant time step along the prediction horizon to get a shorter step size at the beginning of that horizon while a lower resolution for the far instants. Besides, a softening of related constraints to meet the market requirements related to the sale of electric power to the grid point is proposed. Simulation results show that the computational burden to solve optimization problems in real time is reduced while minimizing operational costs and satisfying the market constraints. The proposed controller is developed based on a real CHP plant installed at the ETA research factory in Darmstadt, Germany.

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“…Benders decomposition has been applied to numerous optimization problems such as the fixed charge network design problem [10], the unit commitment problem [28], the network-constrained unit commitment problem [51], and the scheduling of crude oil in an oil refinery [42]. It is also proven quite effective on multi-stage stochastic energy planning problems [33,40] and CCHP-microgrid operation involving battery storage [30].…”

Section: Benders Decompositionmentioning

confidence: 99%

A Benders decomposition approach to transmission expansion planning considering energy storage

MacRae

Ernst

Özlen

2016

Energy

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Developing a CCHP- microgrid operation decision model under uncertainty

Cited by 41 publications

References 47 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

Optimal operation of combined heat and power systems: An optimization-based control strategy

A Benders decomposition approach to transmission expansion planning considering energy storage

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