Search Results (4)

The aim of this work is to explore a load frequency control (LFC) strategy in micro hydro power plants (MHPPs). Using MATLAB/Simulink, we examined several variants of genetic algorithms (GAs), including Roulette, Tournament, and Uniform, which are utilized to optimize tuning proportional integral derivative (PID) parameters by addressing the problem of instability caused by load variations. The performances are compared with conventional PID methods and other advanced techniques like particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural networks (ANN) algorithms for both single and dual-area MHPP systems. The results show that the GA-optimized PID controller with the roulette wheel achieves the fastest settling time of 0.3 s and the smallest undershoot of 0.015 pu in the single area. Also, optimizing GA demonstrates superior performance in the dual area, with the fastest settling times of 2.5 s for both Roulette and Uniform. In contrast, PSO is slower than GA, and conventional PID requires a much longer settling time of 19.8 s, a similar result occurring in the dual area. These findings confirm the effectiveness of the GA-optimized PID controller, especially the Roulette variant, as a reliable and fast solution for maintaining frequency stability in MHPPs. Full article

Off-grid renewable energy systems have become a cost-effective way to supply electricity in remote rural areas, contributing to achieving universal energy access as mandated by Sustainable Development Goal 7 (SDG7). However, benefits are often compromised by limitations in the financial and technical capacity and capabilities of rural beneficiaries to operate and maintain the technology, raising concerns about the cost-effectiveness of investment in the systems. This study examines the non-economic social benefits of providing electricity through off-grid renewable systems and whether these benefits justify investment in the efforts and costs borne by rural communities. Using the case study of the community-managed Kalilang micro-hydro power plant (MHPP) operating on Sumba Island, Indonesia, we estimate the value of non-market benefits of off-grid renewable electricity in rural Indonesia. By applying a mixed-methods approach, this research qualitatively identified perceived non-market benefits through 16 key informant interviews and subsequently employed contingent valuation (CV) with 105 households to estimate their willingness-to-pay (WTP) for these benefits. The results suggest that off-grid renewable projects remain socially viable even when direct economic returns are lacking. Inclusion of these social values into project evaluation and appraisals is needed to better reflect the contribution of off-grid renewable energy systems to community well-being. Full article

This paper proposes an optimal solution for the design of a hybrid power system that will supply a remote fishpond in eastern Serbia. In terms of structure, this off-grid system should be a hydro-photovoltaic-diesel-converter-battery setup. The optimization objectives are to minimize total net present cost (NPC) and greenhouse gas (GHG) emissions and to maximize total annual electricity generation based on the modification of hydro-turbine performance. This study considers the following three cases of a hydro-turbine with fixed propeller blades: having fixed guide vanes, for the annual average flow rate-Case 1; having adjustable guide vanes, for smaller flow rates-Case 2 and having adjustable guide vanes, for higher flow rates-Case 3. The optimization is performed using HOMER Pro v. 3.16.2 software. The results show that the total NPC, levelized cost of energy (COE) and GHG emissions in Case 3 are 16.6%, 16.8% and 13.1% lower than in Case 1, and 8.1%, 8% and 11.7% lower than in Case 2, respectively. It is also found that the total annual electricity generation and power output from the entire system in Case 3 are 33.3% and 1.2% higher than in Case 1, and 11.9% higher and not different than in Case 2, respectively. Full article

Micro Hydro Power Plants (MHPP) constitute an effective, environmentally-friendly solution to deal with energy poverty in rural isolated areas, being the most extended renewable technology in this field. Nevertheless, the context of poverty and lack of qualified manpower usually lead to a poor usage of the resources, due to the use of thumb rules and user experience to design the layout of the plants, which conditions the performance. For this reason, the development of robust and efficient optimization strategies are particularly relevant in this field. This paper proposes a Genetic Algorithm (GA) to address the problem of finding the optimal layout for an MHPP based on real scenario data, obtained by means of a set of experimental topographic measurements. With this end in view, a model of the plant is first developed, in terms of which the optimization problem is formulated with the constraints of minimal generated power and maximum use of flow, together with the practical feasibility of the layout to the measured terrain. The problem is formulated in both single-objective (minimization of the cost) and multi-objective (minimization of the cost and maximization of the generated power) modes, the Pareto dominance being studied in this last case. The algorithm is first applied to an example scenario to illustrate its performance and compared with a reference Branch and Bound Algorithm (BBA) linear approach, reaching reductions of more than 70% in the cost of the MHPP. Finally, it is also applied to a real set of geographical data to validate its robustness against irregular, poorly sampled domains. Full article