Economic and environmental operation of power systems including combined cooling, heating, power and energy storage resources using developed multi-objective grey wolf algorithm

2021 ◽  
Vol 298 ◽  
pp. 117257
Author(s):  
Jie Chen ◽  
Shoujun Huang ◽  
Laleh Shahabi
2021 ◽  
Vol 13 (9) ◽  
pp. 4681
Author(s):  
Khashayar Hamedi ◽  
Shahrbanoo Sadeghi ◽  
Saeed Esfandi ◽  
Mahdi Azimian ◽  
Hessam Golmohamadi

Growing concerns about global greenhouse gas emissions have led power systems to utilize clean and highly efficient resources. In the meantime, renewable energy plays a vital role in energy prospects worldwide. However, the random nature of these resources has increased the demand for energy storage systems. On the other hand, due to the higher efficiency of multi-energy systems compared to single-energy systems, the development of such systems, which are based on different types of energy carriers, will be more attractive for the utilities. Thus, this paper represents a multi-objective assessment for the operation of a multi-carrier microgrid (MCMG) in the presence of high-efficiency technologies comprising compressed air energy storage (CAES) and power-to-gas (P2G) systems. The objective of the model is to minimize the operation cost and environmental pollution. CAES has a simple-cycle mode operation besides the charging and discharging modes to provide more flexibility in the system. Furthermore, the demand response program is employed in the model to mitigate the peaks. The proposed system participates in both electricity and gas markets to supply the energy requirements. The weighted sum approach and fuzzy-based decision-making are employed to compromise the optimum solutions for conflicting objective functions. The multi-objective model is examined on a sample system, and the results for different cases are discussed. The results show that coupling CAES and P2G systems mitigate the wind power curtailment and minimize the cost and pollution up to 14.2% and 9.6%, respectively.


Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2891 ◽  
Author(s):  
Jalel Ben Hmida ◽  
Mohammad Javad Morshed ◽  
Jim Lee ◽  
Terrence Chambers

The optimal power flow (OPF) module optimizes the generation, transmission, and distribution of electric power without disrupting network power flow, operating limits, or constraints. Similarly to any power flow analysis technique, OPF also allows the determination of system’s state of operation, that is, the injected power, current, and voltage throughout the electric power system. In this context, there is a large range of OPF problems and different approaches to solve them. Moreover, the nature of OPF is evolving due to renewable energy integration and recent flexibility in power grids. This paper presents an original hybrid imperialist competitive and grey wolf algorithm (HIC-GWA) to solve twelve different study cases of simple and multiobjective OPF problems for modern power systems, including wind and photovoltaic power generators. The performance capabilities and potential of the proposed metaheuristic are presented, illustrating the applicability of the approach, and analyzed on two test systems: the IEEE 30 bus and IEEE 118 bus power systems. Sensitivity analysis has been performed on this approach to prove the robustness of the method. Obtained results are analyzed and compared with recently published OPF solutions. The proposed metaheuristic is more efficient and provides much better optimal solutions.


Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 918 ◽  
Author(s):  
Xiao Gong ◽  
Fan Li ◽  
Bo Sun ◽  
Dong Liu

Combined cooling, heating, and power (CCHP) systems are a promising energy-efficient and environment-friendly technology. However, their performance in terms of energy, economy, and environment factors depends on the operation strategy. This paper proposes a multi-energy complementary CCHP system integrating renewable energy sources and schedulable heating, cooling, and electrical loads. The system uses schedulable loads instead of energy storage, at the same time, a collaborative optimization scheduling strategy, which integrates energy supply and load demand into a unified optimization framework to achieve the optimal system performance, is presented. Schedulable cooling and heating load models are formulated using the relationship between indoor and outdoor house temperatures. A genetic algorithm is employed to optimize the overall performance of energy, economy, and environment factors and obtain optimal day-ahead scheduling scheme. Case studies are conducted to verify the efficiency of the proposed method. Compared with a system involving thermal energy storage and demand response (DR), the proposed method exhibits a higher primary energy saving rate, greenhouse gas emission reduction rate, and operation costs saving rate of 7.44%, 6.59%, and 4.73%, respectively, for a typical summer day, thereby demonstrating the feasibility and superiority of the proposed approach.


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