scholarly journals Assessing the Techno-Economic Benefits of Flexible Demand Resources Scheduling for Renewable Energy–Based Smart Microgrid Planning

2019 ◽  
Vol 11 (10) ◽  
pp. 219 ◽  
Author(s):  
Mark Kipngetich Kiptoo ◽  
Oludamilare Bode Adewuyi ◽  
Mohammed Elsayed Lotfy ◽  
Theophilus Amara ◽  
Keifa Vamba Konneh ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Economic Benefits ◽  
Mixed Integer ◽  
Optimal Size ◽  
Renewable Energy Resources ◽  
Power Prediction ◽  
Short Term ◽  
Load Demand ◽  
Battery Energy ◽  
The Right

The need for innovative pathways for future zero-emission and sustainable power development has recently accelerated the uptake of variable renewable energy resources (VREs). However, integration of VREs such as photovoltaic and wind generators requires the right approaches to design and operational planning towards coping with the fluctuating outputs. This paper investigates the technical and economic prospects of scheduling flexible demand resources (FDRs) in optimal configuration planning of VRE-based microgrids. The proposed demand-side management (DSM) strategy considers short-term power generation forecast to efficiently schedule the FDRs ahead of time in order to minimize the gap between generation and load demand. The objective is to determine the optimal size of the battery energy storage, photovoltaic and wind systems at minimum total investment costs. Two simulation scenarios, without and with the consideration of DSM, were investigated. The random forest algorithm implemented on scikit-learn python environment is utilized for short-term power prediction, and mixed integer linear programming (MILP) on MATLAB® is used for optimum configuration optimization. From the simulation results obtained here, the application of FDR scheduling resulted in a significant cost saving of investment costs. Moreover, the proposed approach demonstrated the effectiveness of the FDR in minimizing the mismatch between the generation and load demand.

Very Short-Term Renewable Energy Power Prediction Using XGBoost Optimized by TPE Algorithm

2020 ◽  
Author(s):  
Zhenchuan Ma ◽  
Haijun Chang ◽  
Zhongqing Sun ◽  
Fusuo Liu ◽  
Wei Li ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Power Prediction ◽  
Short Term

scholarly journals Renewable energy resources in the EU (Policy scenario)

2013 ◽  
pp. 143-146
Author(s):  
Orsolya Nagy
Keyword(s):  
Climate Change ◽  
European Union ◽  
Renewable Energy ◽  
Energy Use ◽  
Biomass Energy ◽  
Energy Resources ◽  
The European Union ◽  
Renewable Energy Resources ◽  
The Right ◽  
The Eu

The use of renewable energies has a long past, even though its share of the total energy use is rather low in European terms. However, the tendencies are definitely favourable which is further strengthened by the dedication of the European Union to sustainable development and combat against climate change. The European Union is on the right track in achieving its goal which is to be able to cover 20% its energy need from renewable energy resources by 2020. The increased use of wind, solar, water, tidal, geothermal and biomass energy will reduce the energy import dependence of the European Union and it will stimulate innovation.

scholarly journals Analysis of Techno-Economic-Environmental Suitability of an Isolated Microgrid System Located in a Remote Island of Bangladesh

2020 ◽  
Vol 12 (7) ◽  
pp. 2880 ◽  
Author(s):  
Hasan Masrur ◽  
Harun Or Rashid Howlader ◽  
Mohammed Elsayed Lotfy ◽  
Kaisar R. Khan ◽  
Josep M. Guerrero ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Economic Feasibility ◽  
Added Value ◽  
Renewable Energy Resources ◽  
Hybrid Microgrid ◽  
Load Demand ◽  
Cost Of Energy ◽  
Remote Island ◽  
Heavy Burden ◽  
Energy Options

Following a rise in population, load demand is increasing even in the remote areas and islands of Bangladesh. Being an island that is also far from the mainland of Bangladesh, St. Martin’s is in need of electricity. As it has ample renewable energy resources, a renewable energy-based microgrid system seems to be the ultimate solution, considering the ever-increasing price of diesel fuel. This study proposes a microgrid system and tests its technical and economic feasibility in that area. All possible configurations have been simulated to try and find the optimal system for the island, which would be eco-friendly and economical with and without considering renewable energy options. The existing power supply configuration has also been compared to the best system after analyzing and investigating all technical and economic feasibility. Sensitivity and risk analysis between different cases provide added value to this study. The results show that the current diesel-based system is not viable for the island’s people, but rather a heavy burden to them due to the high cost of per unit electricity and the net present cost. In contrast, a PV /Wind/Diesel/Battery hybrid microgrid appeared to be the most feasible system. The proposed system is found to be around 1.5 times and 28% inexpensive considering the net present cost and cost of energy, respectively, with a high (56%) share of renewable energy which reduces 23% carbon dioxide.

scholarly journals Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 512 ◽  
Author(s):  
Chao Fu ◽  
Guo-Quan Li ◽  
Kuo-Ping Lin ◽  
Hui-Juan Zhang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Wind Power ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction ◽  
Svm Model

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

scholarly journals Congestion Management of Microgrids With Renewable Energy Resources and Energy Storage Systems

2021 ◽  
Vol 9 ◽  
Author(s):  
Yaling Chen ◽  
Yinpeng Liu
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Programming Model ◽  
Congestion Management ◽  
Heat Pumps ◽  
Energy Resources ◽  
Mixed Integer ◽  
Network Reconfiguration ◽  
Renewable Energy Resources ◽  
Reliable Operation

With the increasing share of renewable energy resources in the microgrid, the microgrid faces more and more challenges in its reliable operation. One major challenge is the potential congestion caused by the uncoordinated operation of flexible demands such as heat pumps and the high penetration of renewable energy resources such as photovoltaics. Therefore, it is important to conduct microgrid energy management to ensure its reliable operation. The energy storage system (ESS) scheduling as an efficient means to alleviate congestion has been widely used. However, in the existing literature, the ESSs are usually scheduled by the microgrid system operator (MSO) in a direct control manner, which is impractical in the case where customers own ESSs and are willing to schedule ESSs by themselves. To resolve this issue, this study proposes a network reconfiguration integrated dynamic tariff–subsidy (DTS) congestion management method to utilize ESSs and network reconfiguration to alleviate congestion in microgrids caused by renewable energy resources and flexible demands. In the proposed method, the MSO controls sectionalization switches while customers or aggregators schedule ESSs in response to DTS to alleviate congestion. The DTS calculation model is formulated as a mixed-integer linear programming model, considering heat pumps (HPs), ESSs, and reconfigurable microgrid topology. The numerical results demonstrate that the proposed method can effectively use ESSs and network topology to alleviate congestion and the MSO does not need to take over the scheduling of the ESS.

scholarly journals A Review on Demand Side Management Applications, Techniques, and Potential Energy and Cost saving

2021 ◽  
Vol 20 (1) ◽  
pp. 21-33
Author(s):  
Hossam Eldin Hamed Shalaby
Keyword(s):  
Dynamic Pricing ◽  
Energy Savings ◽  
Peak Load ◽  
Cost Effective ◽  
Demand Side Management ◽  
Special Focus ◽  
Demand Side ◽  
Renewable Energy Resources ◽  
Load Demand ◽  
Battery Energy

Electrical peak load demand all over the world is always anticipated to grow, which is challenging electrical utility to supply such increasing load demand in a cost effective, reliable and sustainable manner. Thus, there is a need to study some of load management (LM) techniques employed to minimize energy consumption, reduce consumers' electricity bills and decrease the greenhouse gas emissions responsible for global warming. This paper presents a review of several recent LM strategies and optimization algorithms in different domains. The review is complemented by tabulating several demand side management (DSM) techniques with a specific view on the used demand response (DR) programs, key finding and benefits gained. A special focus is directed to the communication protocols and wireless technology, incorporation of renewable energy resources (RERs), battery energy storage (BES), home appliances scheduling and power quality applications. The outcome of this review reveals that the real time pricing (RTP) is the most efficient price-based mechanism program (PBP), whilst time of use (TOU) is the basic PBP and easiest to implement. Energy efficiency programs have proved the highest influential impact on the annual energy saving over the other dynamic pricing mechanism programs. Through a forecasted proposal of future study, DSM proved tremendous potential annual energy savings, peak demand savings, and investment cost rates within different consumption sectors progressively up to year 2030.

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