scholarly journals Optimal Energy Management of Building Microgrid Networks in Islanded Mode Considering Adjustable Power and Component Outages

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2351 ◽  
Author(s):  
Van-Hai Bui ◽  
Akhtar Hussain ◽  
Hak-Man Kim ◽  
Yong-Hoon Im
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Energy System ◽  
Heat Pumps ◽  
External Energy ◽  
Optimal Energy ◽  
Unit Energy ◽  
Islanded Mode ◽  
Optimal Energy Management ◽  
The Impact

In this paper, an optimal energy management scheme for islanded building microgrid networks is proposed. The proposed building microgrid network comprises of several inter-connected building microgrids (BMGs) and an external energy supplier. Each BMG has a local combined heat and power (CHP) unit, energy storage, renewables and loads (electric and thermal). The external energy system comprises of an external CHP unit, chillers, electric heat pumps and heat pile line, for thermal energy storage. The BMGs can trade energy with other BMGs of the network and can also trade energy with the external energy supplier. In order to efficiently utilize the components of the BMGs and the network, the concept of adjustable power is adopted in this study. Adjustable power can reduce the operation cost of the network by increasing/decreasing the power of dispatchable units. In addition, the failure/recovery of components in the BMGs and the external system are also considered to analyze the performance of the proposed operation method. In order to optimally utilize the available resources during events, precedence among loads of BMGs and the external energy supplier is considered. Simulation results have proved the applicability of the proposed method for both normal islanded mode and with outage/recovery of equipment during the operation horizon. Finally, sensitivity analysis is carried out to analyze the impact of change in components’ parameters values on the saved cost of the network.

scholarly journals Economical Evaluation and Optimal Energy Management of a Stand-Alone Hybrid Energy System Handling in Genetic Algorithm Strategies

Electronics ◽  
2018 ◽  
Vol 7 (10) ◽  
pp. 233 ◽  
Author(s):  
Omar Mohammed ◽  
Yassine Amirat ◽  
Mohamed Benbouzid
Keyword(s):  
Genetic Algorithm ◽  
Energy Management ◽  
Hybrid System ◽  
High Reliability ◽  
Energy System ◽  
System Model ◽  
Optimization Approach ◽  
Hybrid Energy ◽  
Optimal Energy ◽  
Optimal Energy Management

Hybrid renewable energy systems are a promising technology for clean and sustainable development. In this paper, an intelligent algorithm, based on a genetic algorithm (GA), was developed and used to optimize the energy management and design of wind/PV/tidal/ storage battery model for a stand-alone hybrid system located in Brittany, France. This proposed optimization focuses on the economic analysis to reduce the total cost of hybrid system model. It suggests supplying the load demand under different climate condition during a 25-years interval, for different possible cases and solutions respecting many constraints. The proposed GA-based optimization approach achieved results clear highlight its practicality and applicability to any hybrid power system model, including optimal energy management, cost constraint, and high reliability.

Optimal Energy Management of Hybrid Power System With Two-Scale Dynamic Programming

2011 ◽  
Author(s):  
Lei Zhang ◽  
Yaoyu Li
Keyword(s):  
Dynamic Programming ◽  
Wind Energy ◽  
Energy Management ◽  
Electricity Price ◽  
Short Term ◽  
Optimal Energy ◽  
Macro Scale ◽  
Optimal Energy Management ◽  
The Impact

Energy management is one of the main issues in operating the HPS, which needs to be optimized with respect to the current and future change in generation, demand, and market price, particularly for HPS with strong renewable penetration. Optimal energy management strategies such as dynamic programming (DP) may become significantly suboptimal under strong uncertainty in prediction of renewable generation and utility price. In order to reduce the impact of such uncertainties, a two-scale dynamic programming scheme is proposed in this study to optimize the operational benefit based on multi-scale prediction. First, a macro-scale dynamic programming (MASDP) is performed for the long term period, based on long term ahead prediction of hourly electricity price and wind energy (speed). The battery state-of-charge (SOC) is thus obtained as the macro-scale reference trajectory. The micro-scale dynamic programming (MISDP) is then applied with a short term interval, based on short term-hour ahead auto-regressive moving average (ARMA) prediction of hourly electricity price and wind energy. The nodal SOC values from the MASDP result are used as the terminal condition for the MISDP. The simulation results show that the proposed method can significantly decrease the operation cost, as compared with the single scale DP method.

scholarly journals Optimal Energy Management of Railroad Electrical Systems with Renewable Energy and Energy Storage Systems

2019 ◽  
Vol 11 (22) ◽  
pp. 6293 ◽  
Author(s):  
Seunghyun Park ◽  
Surender Reddy Salkuti
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Energy Management ◽  
Storage Systems ◽  
Solar Pv ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Optimal Energy ◽  
Optimal Energy Management

The proposed optimal energy management system balances the energy flows among the energy consumption by accelerating trains, energy production from decelerating trains, energy from wind and solar photovoltaic (PV) energy systems, energy storage systems, and the energy exchange with a traditional electrical grid. In this paper, an AC optimal power flow (AC-OPF) problem is formulated by optimizing the total cost of operation of a railroad electrical system. The railroad system considered in this paper is composed of renewable energy resources such as wind and solar PV systems, regenerative braking capabilities, and hybrid energy storage systems. The hybrid energy storage systems include storage batteries and supercapacitors. The uncertainties associated with wind and solar PV powers are handled using probability distribution functions. The proposed optimization problem is solved using the differential evolution algorithm (DEA). The simulation results show the suitability and effectiveness of proposed approach.

Stochastic Optimal Energy Management of Smart Home With PEV Energy Storage

2018 ◽  
Vol 9 (3) ◽  
pp. 2065-2075 ◽  
Author(s):  
Xiaohua Wu ◽  
Xiaosong Hu ◽  
Xiaofeng Yin ◽  
Scott J. Moura
Keyword(s):  
Energy Storage ◽  
Energy Management ◽  
Smart Home ◽  
Optimal Energy ◽  
Optimal Energy Management
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