Integrated supply–demand energy management for optimal design of off-grid hybrid renewable energy systems for residential electrification in arid climates

The growing research interest in hybrid renewable energy systems (HRESs) has been regarded as a natural and yet critical response to address the challenge of rural electrification. Based on a Bibliometric analysis performed by authors, it was concluded that most studies simply adopted supply-side management techniques to perform the design optimization of such a renewable energy system. To further advance those studies, this paper presents a novel approach by integrating demand-supply management (DSM) with particle swarm optimization and applying it to optimally design an off-grid hybrid PV-solar-diesel-battery system for the electrification of residential buildings in arid environments, using a typical dwelling in Adrar, Algeria, as a case study. The proposed HRES is first modelled by an in-house MATLAB code based on a multi-agent system concept and then optimized by minimizing the total net present cost (TNPC), subject to reliability level and renewable energy penetration. After validation against the HOMER software, further techno-economic analyses including sensitivity study are undertaken, considering different battery technologies. By integrating the proposed DSM, the results have shown the following improvements: with RF = 100%, the energy demand and TNPC are reduced by 7% and 18%, respectively, compared to the case of using solely supply-side management. It is found that PV-Li-ion represents the best configuration, with TNPC of $23,427 and cost of energy (COE) of 0.23$/kWh. However, with lower RF values, the following reductions are achieved: energy consumption (19%) and fuel consumption or CO 2 emission (57%), respectively. In contrast, the RF is raised from 15% (without DSM) to 63% (with DSM). It is clear that the optimal configuration consists of wind-diesel, with COE of 0.21 $/kWh, smaller than that obtained with a stand-alone diesel generator system. The outcomes of this work can provide valuable insights into the successful design and deployment of HRES in Algeria and surrounding regions

Multiobjective intelligent energy management optimization for grid-connected microgrids

In the rapid growing of the green energy technology, microgrid systems with renewable energy sources (RESs) such as solar, wind and fuel cells are becoming a prevalent and efficient way to control and manage these renewable sources. Moreover, owing to the intermittency and the frequent irregular responses of the RESs, battery energy storages have become an integral part of microgrids. In such complex systems, optimal use of RESs heavily depend on the energy management strategy used. Besides, the reduction of conventional fuel utilization and the resultant drop in the emissions also depend on the energy management strategy. This paper presents a novel expert system Fuzzy Logic - Grey Wolf Optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management in grid-connected microgrids. The proposed method is tested on different scenarios, and the simulation results are compared with other existing approaches methods such as GA, PSO, BA, IBA and GWO. The simulation results show a significant improvement with the proposed method in terms of satisfying the demands and to minimizing the operating costs of the microgrid compared to other existing methods

Impact of tidal energy on battery sizing in standalone microgrids: A case study

This paper investigates the impact of adding tidal energy on the size of battery energy storage (BES) required to absorb power fluctuations present in a standalone microgrid with wind, solar and diesel engine is driven generation sources. The Flinders Island power system is chosen as the standalone microgrid for the case study. In addition to the battery capacity, emissions and operational costs are also taken as the variables that should be minimized in optimization problem formulated in this study. In order to solve this multi-objective optimization problem an intelligent expert fuzzy system - grey wolf optimization (FL-GWO) algorithm is proposed in this paper. Different scenarios based on the weather conditions in the Flinders Island is considered to demonstrate the robust performance of the proposed (FL-GWO) method. The numerical results show that when tidal energy is introduced the required battery capacity dropped from 300kWh to 250kWh which is equivalent to 16.67% drop. The effectiveness of the FL-GWO is validated by comparing it with other existing approaches such as the rules-based method and conventional GWO algorithm

Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia

Challenging frequency control issues, such as the reliability and security of the power system, arise when increasing penetration levels of inverter-interfaced generation are imposed. As a result of the displacement of convention generation in favour of renewable energy sources, the reduction of frequency response capabilities can be seen. A promising method of overcoming the aforementioned challenges is to utilise Battery Energy Storage Systems (BESS), which provides frequency support by injecting instantaneous power to the grid and back up the conventional generation systems. However, large battery systems increase the cost while inadequate battery capacities result in poor performance. This paper, therefore, proposes an approach for finding the optimum BESS size for an islanded microgrid power system. The determination of the optimum BESS size is based on an existing case study, under which the most severe contingencies of generation loss and load loss have been accounted for, as well as different levels of penetration of renewable energy sources. As a result of using meta-heuristic optimization (Grey Wolf Optimization), the constraint optimization problem has been identified as BESS sizing. Through the use of real-time simulation DIgSILENT PowerFactory software, estimated BESS size can be applied to a standalone microgrid to test the frequency of support capabilities. The simulation has made it apparent that through the selection of the optimum BESS size, the system frequency response is not only mitigated, but improved