Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid

Zhang, Yan; Meng, Fanyong; Wang, Rui; Kazemtabrizi, Behzad; Shi, Jianmai

doi:10.1016/j.energy.2019.04.151

Cited by 94 publications

(33 citation statements)

References 32 publications

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“…Among these strategies, the Model Predictive Control (MPC) approach has had a great application in the tracking stages once the planning stages have been previously optimized [10]. In [11], a stochastic MPC framework to optimally schedule and control the CHP microgrid with large-scale renewable energy sources to reduce the negative impacts introduced by uncertainties is proposed.…”

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%

“…Following this idea, the CHP system will have three inputs, namely, T 1 , F W and F G , which feed the system to produce the outputs P E and T 2 . Although the direct output of the CHP system is T 2 , from this variable, the heat flow recovered q CHP can be computed by using (11).…”

Section: Case Studymentioning

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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Section: Computational Time T Smentioning

confidence: 99%

Section: Computational Time T Smentioning

confidence: 99%

See 1 more Smart Citation

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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“…A weighting method is applied to systematically combine multiple predictive models in [20], which can reduce the prediction bias. References [21][22][23][24] deal with wind power uncertainty through scenario generation and reduction methods. The solution to the uncertainty of renewable energy output are relatively simple and quite limited.…”

Section: Introductionmentioning

confidence: 99%

Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response

et al. 2019

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Renewable energy resources (RESs) play an important role in the upgrading and transformation of the global energy structure. However, the question of how to improve the utilization efficiency of RESs and reduce greenhouse gas emissions is still a challenge. Combined heating and power (CHP) is one effective solution and has experienced rapid development. Nevertheless, with the large scale of RESs penetrating into the power system, CHP microgrid economic operation faces great challenges. This paper proposes a CHP microgrid system that contains renewable energy with considering economy, the environment, and system flexibility, and the ultimate goal is to minimize system operation cost and carbon dioxide emissions (CO2) cost. Due to the volatility of renewable energy output, the fuzzy C-means (FCM) and clustering comprehensive quality (CCQ) models were first introduced to generate clustering scenarios of the renewable energy output and evaluate the clustering results. In addition, for the sake of improving the flexibility and reliability of the CHP microgrid, this paper considers the battery and integrated energy demand response (IEDR). Moreover, the strategy choices of microgrid operators under the condition of grid-connected and islanded based on environment and interest aspects are also developed, which have rarely been involved in previous studies. Finally, this stochastic optimization problem is transformed into a mixed integer linear programming (MILP), which simplifies the calculation process, and the results show that the operation mode under different conditions will have a great impact on microgrid economic and environmental benefits.

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“…Using a time-sharing electricity grid and grid-connected operating conditions, a simulated annealing particle swarm optimization algorithm was used to solve the problem [4,5]. In order to cope with the uncertainty of the thermal power load and the output of renewable energy such as wind and light, Sang and Zhang established a stochastic optimization operation model of a CCHP microgrid [6,7]. Aiming at the stochastic characteristics of cold and heat loads and renewable energy output, Grover-Silva and Ji established a pre-economic scheduling model for a cold-heat-powered microgrid based on interval planning [8,9].…”

mentioning

confidence: 99%

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

Luo

Zhu

et al. 2020

Processes

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With renewable generation resources and multiple load demands increasing, the combined cooling, heating, and power (CCHP) microgrid energy management system has attracted much attention due to its high efficiency and low emissions. In order to realize the integration of substation resources and solve the problems of inaccurate, random, volatile and intermittent load forecasting, we propose a three-stage coordinated optimization scheduling strategy for a CCHP microgrid. The strategy contains three stages: a day-ahead economic scheduling stage, an intraday rolling optimization stage, and a real-time adjustment stage. Forecasting data with different accuracy at different time scales were used to carry out multilevel coordination and gradually improve the scheduling plan. A case study was used to verify that the proposed scheduling strategy can mitigate and eliminate the load forecasting error of renewable energy (for power balance and scheduling economy).

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Uncertainty-resistant stochastic MPC approach for optimal operation of CHP microgrid

Cited by 94 publications

References 32 publications

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

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

Research on Economic Operation Strategy of CHP Microgrid Considering Renewable Energy Sources and Integrated Energy Demand Response

A Three-Stage Coordinated Optimization Scheduling Strategy for a CCHP Microgrid Energy Management System

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