The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtail...The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.展开更多
Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design sp...Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment.While exact mathematical optimisa-tion approaches to the MG capacity planning are highly computationally efficient,they often fail to preserve the associated problem nonlinearities and non-convexities.This translates into the fact that the available MG sizing tools potentially return a sub-optimal(inferior)MG design.This brings to light the importance of nature-inspired,swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem–and better approximate the globally optimum solution–though at the expense of increased computational complexity.Accordingly,this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic,namely the Lévy-flight moth-flame optimisation algorithm(MFOA).An intelligent linear programming-based day-ahead en-ergy scheduling design is,additionally,integrated into the proposed model.A case study is presented for a real grid-tied community MG in rural New Zealand.A comparison of the modelling results with those of the most popular tool in the literature and industry,HOMER Pro,verifies the superiority of the proposed meta-heuristic-based MG sizing model.Additionally,the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature.The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics.Notably,its superiority to the original MFOA,the hybrid genetic algorithm-particle swarm optimisation,and the ant colony optimiser,by at least~6.5%,~8.4%,and~12.8%,is demonstrated.Moreover,comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.展开更多
文摘The uncertainty inherent in power load forecasts represents a major factor in the mismatches between supply and demand in renewables-rich electricity networks, which consequently increases the energy bills and curtailed generation. As the transition to a power grid founded on the so-called grid-of-grids becomes more evident, the need for distributed control algorithms capable of handling computationally challenging problems in the energy sector does so as well. In this light, the consensus-based distributed algorithm has recently been shown to provide an effective platform for solving the complex energy management problem in microgrids. More specifically, in a microgrid context, the consensus-based distributed algorithm requires reliable information exchange with customers to achieve convergence. However, packet losses remain an important issue, which can potentially result in the failure of the overall system. In this setting, this paper introduces a novel method to effectively characterize such packet losses during information exchange between the customers and the microgrid operator, whilst solving the microgrid scheduling optimization problem for a multi-agent-based microgrid. More specifically, the proposed framework leverages the virulence optimization algorithm and the earth-worm optimization algorithm to optimally shift the energy consumption during peak periods to lower-priced off-peak hours. The effectiveness of the proposed method in minimizing the overall active power mismatches in the presence of packet losses has also been demonstrated based on benchmarking the results against the business-as-usual iterative scheduling algorithm. Also, the robustness of the overall meta-heuristic- and multi-agent-based method in producing optimal results is confirmed based on comparing the results obtained by several well-established meta-heuristic optimization algorithms, including the binary particle swarm optimization, the genetic algorithm, and the cuckoo search optimization.
文摘Bridging the gap between simulation and reality for successful micro-grid(MG)implementation requires accu-rate mathematical modelling of the underlying energy infrastructure and extensive optimisation of the design space defined by all possible combinations of the size of the equipment.While exact mathematical optimisa-tion approaches to the MG capacity planning are highly computationally efficient,they often fail to preserve the associated problem nonlinearities and non-convexities.This translates into the fact that the available MG sizing tools potentially return a sub-optimal(inferior)MG design.This brings to light the importance of nature-inspired,swarm-based meta-heuristic optimisation algorithms that are able to effectively handle the nonlinear and non-convex nature of the MG design optimisation problem–and better approximate the globally optimum solution–though at the expense of increased computational complexity.Accordingly,this paper introduces a robust MG capacity planning optimisation framework based on a state-of-the-art meta-heuristic,namely the Lévy-flight moth-flame optimisation algorithm(MFOA).An intelligent linear programming-based day-ahead en-ergy scheduling design is,additionally,integrated into the proposed model.A case study is presented for a real grid-tied community MG in rural New Zealand.A comparison of the modelling results with those of the most popular tool in the literature and industry,HOMER Pro,verifies the superiority of the proposed meta-heuristic-based MG sizing model.Additionally,the efficiency of the Lévy-flight MFOA is compared to nine well-established meta-heuristics in the MG capacity planning literature.The comparative analyses have revealed the statistically significant outperformance of the Lévy-flight MFOA to the examined meta-heuristics.Notably,its superiority to the original MFOA,the hybrid genetic algorithm-particle swarm optimisation,and the ant colony optimiser,by at least~6.5%,~8.4%,and~12.8%,is demonstrated.Moreover,comprehensive capital budgeting analyses have confirmed the financial viability of the test-case system optimised by the proposed model.
